WorldWideScience

Sample records for cancer prediction software

  1. Software failures prediction using RBF neural network

    Directory of Open Access Journals (Sweden)

    Vitaliy S. Yakovyna

    2015-06-01

    Full Text Available One of the prospective techniques for software reliability prediction are those based on nonparametric models, in particular on artificial neural networks. In this paper the study of influence of number of input neurons of network based on radial basis function on the efficiency of software failures prediction presented in the form of time series is carried out. Software faults time series are constructed using Chromium and Chromium-OS open source software systems testing data with proposed further processing as a normalized values of the number of software failures in equal intervals, followed by transfer to man-days. It is demonstrated that the closest prediction can be achieved using Inverse Multiquadric activation function with 10…20 input layer neurons and 30 hidden neurons.

  2. Intelligence System for Software Maintenance Severity Prediction

    Directory of Open Access Journals (Sweden)

    Parvinder S. Sandhu

    2007-01-01

    Full Text Available The software industry has been experiencing a software crisis, a difficulty of delivering software within budget, on time, and of good quality. This may happen due to number of defects present in the different modules of the project that may require maintenance. This necessitates the need of predicting maintenance urgency of the particular module in the software. In this paper, we have applied the different predictor models to NASA five public domain defect datasets coded in C, C++, Java and Perl programming languages. Twenty one software metrics of different datasets and Java Classes of thirty five algorithms belonging to the different learner categories of the WEKA project have been evaluated for the prediction of maintenance severity. The results of ten fold cross validation are recorded in terms of Accuracy, Mean Absolute Error (MAE and Root Mean Squared Error (RMSE for different project datasets. The results show that logistic model Trees (LMT and Complimentary Naïve Bayes (CNB based Model provide a relatively better prediction consistency compared to other models and hence, can be used for the maintenance severity prediction of the software. The developed system can also be used for analysis and to evaluate the influence of different factors on the maintenance severity of different software project modules.

  3. Fault Predictions in Object Oriented Software

    CERN Document Server

    Bremananth, R

    2009-01-01

    The dynamic software development organizations optimize the usage of resources to deliver the products in the specified time with the fulfilled requirements. This requires prevention or repairing of the faults as quick as possible. In this paper an approach for predicting the run-time errors in java is introduced. The paper is concerned with faults due to inheritance and violation of java constraints. The proposed fault prediction model is designed to separate the faulty classes in the field of software testing. Separated faulty classes are classified according to the fault occurring in the specific class. The results are papered by clustering the faults in the class. This model can be used for predicting software reliability.

  4. Prediction of Defective Software Modules Using Class Imbalance Learning

    OpenAIRE

    Divya Tomar; Sonali Agarwal

    2016-01-01

    Software defect predictors are useful to maintain the high quality of software products effectively. The early prediction of defective software modules can help the software developers to allocate the available resources to deliver high quality software products. The objective of software defect prediction system is to find as many defective software modules as possible without affecting the overall performance. The learning process of a software defect predictor is difficult due to the imbal...

  5. FSO and quality of service software prediction

    Science.gov (United States)

    Bouchet, O.; Marquis, T.; Chabane, M.; Alnaboulsi, M.; Sizun, H.

    2005-08-01

    Free-space optical (FSO) communication links constitute an alternative option to radio relay links and to optical cables facing growth needs in high-speed telecommunications (abundance of unregulated bandwidth, rapid installation, availability of low-cost optical components offering a high data rate, etc). Their operationalisation requires a good knowledge of the atmospheric effects which can negatively affect role propagation and the availability of the link, and thus to the quality of service (QoS). Better control of these phenomena will allow for the evaluation of system performance and thus assist with improving reliability. The aim of this paper is to compare the behavior of a FSO link located in south of France (Toulouse: with the following parameters: around 270 meters (0.2 mile) long, 34 Mbps data rate, 850 nm wavelength and PDH frame) with airport meteorological data. The second aim of the paper is to assess in-house FSO quality of service prediction software, through comparing simulations with the optical link data and the weather data. The analysis uses in-house software FSO quality of service prediction software ("FSO Prediction") developed by France Telecom Research & Development, which integrates news fog fading equations (compare to Kim & al.) and includes multiple effects (geometrical attenuation, atmospheric fading, rain, snow, scintillation and refraction attenuation due to atmospheric turbulence, optical mispointing attenuation). The FSO link field trial, intended to enable the demonstration and evaluation of these different effects, is described; and preliminary results of the field trial, from December 2004 to May 2005, are then presented.

  6. Lessons from applying experimentation in software engineering prediction systems

    OpenAIRE

    Afzal, Wasif

    2008-01-01

    Within software engineering prediction systems, experiments are undertaken primarliy to investigate relationships and to measure/compare models' accuracy. This paper discusses our experience and presents useful lessons/guidelines in experimenting with software engineering prediction systems. For this purpose, we use a typical software engineering experimentation process as a baseline. We found that the typical software engineering experimentation process in software engineering is suppor...

  7. Collision prediction software for radiotherapy treatments

    International Nuclear Information System (INIS)

    Purpose: This work presents a method of collision predictions for external beam radiotherapy using surface imaging. The present methodology focuses on collision prediction during treatment simulation to evaluate the clearance of a patient’s treatment position and allow for its modification if necessary. Methods: A Kinect camera (Microsoft, Redmond, WA) is used to scan the patient and immobilization devices in the treatment position at the simulator. The surface is reconstructed using the SKANECT software (Occipital, Inc., San Francisco, CA). The treatment isocenter is marked using simulated orthogonal lasers projected on the surface scan. The point cloud of this surface is then shifted to isocenter and converted from Cartesian to cylindrical coordinates. A slab models the treatment couch. A cylinder with a radius equal to the normal distance from isocenter to the collimator plate, and a height defined by the collimator diameter is used to estimate collisions. Points within the cylinder clear through a full gantry rotation with the treatment couch at 0° , while points outside of it collide. The angles of collision are reported. This methodology was experimentally verified using a mannequin positioned in an alpha cradle with both arms up. A planning CT scan of the mannequin was performed, two isocenters were marked in PINNACLE, and this information was exported to AlignRT (VisionRT, London, UK)—a surface imaging system for patient positioning. This was used to ensure accurate positioning of the mannequin in the treatment room, when available. Collision calculations were performed for the two treatment isocenters and the results compared to the collisions detected the room. The accuracy of the Kinect-Skanect surface was evaluated by comparing it to the external surface of the planning CT scan. Results: Experimental verification results showed that the predicted angles of collision matched those recorded in the room within 0.5°, in most cases (largest deviation

  8. Collision prediction software for radiotherapy treatments

    Energy Technology Data Exchange (ETDEWEB)

    Padilla, Laura [Virginia Commonwealth University Medical Center, Richmond, Virginia 23298 (United States); Pearson, Erik A. [Techna Institute and the Princess Margaret Cancer Center, University Health Network, Toronto, Ontario M5G 2M9 (Canada); Pelizzari, Charles A., E-mail: c-pelizzari@uchicago.edu [Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, Illinois 60637 (United States)

    2015-11-15

    Purpose: This work presents a method of collision predictions for external beam radiotherapy using surface imaging. The present methodology focuses on collision prediction during treatment simulation to evaluate the clearance of a patient’s treatment position and allow for its modification if necessary. Methods: A Kinect camera (Microsoft, Redmond, WA) is used to scan the patient and immobilization devices in the treatment position at the simulator. The surface is reconstructed using the SKANECT software (Occipital, Inc., San Francisco, CA). The treatment isocenter is marked using simulated orthogonal lasers projected on the surface scan. The point cloud of this surface is then shifted to isocenter and converted from Cartesian to cylindrical coordinates. A slab models the treatment couch. A cylinder with a radius equal to the normal distance from isocenter to the collimator plate, and a height defined by the collimator diameter is used to estimate collisions. Points within the cylinder clear through a full gantry rotation with the treatment couch at 0° , while points outside of it collide. The angles of collision are reported. This methodology was experimentally verified using a mannequin positioned in an alpha cradle with both arms up. A planning CT scan of the mannequin was performed, two isocenters were marked in PINNACLE, and this information was exported to AlignRT (VisionRT, London, UK)—a surface imaging system for patient positioning. This was used to ensure accurate positioning of the mannequin in the treatment room, when available. Collision calculations were performed for the two treatment isocenters and the results compared to the collisions detected the room. The accuracy of the Kinect-Skanect surface was evaluated by comparing it to the external surface of the planning CT scan. Results: Experimental verification results showed that the predicted angles of collision matched those recorded in the room within 0.5°, in most cases (largest deviation

  9. Predicting Software Suitability Using a Bayesian Belief Network

    Science.gov (United States)

    Beaver, Justin M.; Schiavone, Guy A.; Berrios, Joseph S.

    2005-01-01

    The ability to reliably predict the end quality of software under development presents a significant advantage for a development team. It provides an opportunity to address high risk components earlier in the development life cycle, when their impact is minimized. This research proposes a model that captures the evolution of the quality of a software product, and provides reliable forecasts of the end quality of the software being developed in terms of product suitability. Development team skill, software process maturity, and software problem complexity are hypothesized as driving factors of software product quality. The cause-effect relationships between these factors and the elements of software suitability are modeled using Bayesian Belief Networks, a machine learning method. This research presents a Bayesian Network for software quality, and the techniques used to quantify the factors that influence and represent software quality. The developed model is found to be effective in predicting the end product quality of small-scale software development efforts.

  10. Cancer Risk Prediction and Assessment

    Science.gov (United States)

    Cancer prediction models provide an important approach to assessing risk and prognosis by identifying individuals at high risk, facilitating the design and planning of clinical cancer trials, fostering the development of benefit-risk indices, and enabling estimates of the population burden and cost of cancer.

  11. Predicting Vulnerability Risks Using Software Characteristics

    Science.gov (United States)

    Roumani, Yaman

    2012-01-01

    Software vulnerabilities have been regarded as one of the key reasons for computer security breaches that have resulted in billions of dollars in losses per year (Telang and Wattal 2005). With the growth of the software industry and the Internet, the number of vulnerability attacks and the ease with which an attack can be made have increased. From…

  12. Software Speeds Up Analysis of Breast Cancer Risk

    Science.gov (United States)

    ... page: https://medlineplus.gov/news/fullstory_161117.html Software Speeds Up Analysis of Breast Cancer Risk: Study ... 22, 2016 THURSDAY, Sept. 22, 2016 (HealthDay News) -- Software that quickly analyzes mammograms and patient history to ...

  13. An Approach to Early Prediction of Software Quality

    Institute of Scientific and Technical Information of China (English)

    YAO Lan; YANG Bo

    2007-01-01

    Due to the rapid development of computers and their applications, early software quality prediction in software industry becomes more and more crucial. Software quality prediction model is very helpful for decision-makings such as the allocation of resource in module verification and validation. Nevertheless, due to the complicated situations of software development process in the early stage, the applicability and accuracy of these models are still under research. In this paper, a software quality prediction model based on a fuzzy neural network is presented, which takes into account both the internal factors and external factors of software. With hybrid-learning algorithm, the proposed model can deal with multiple forms of data as well as incomplete information, which helps identify design errors early and avoid expensive rework.

  14. Software Defect Prediction Models for Quality Improvement: A Literature Study

    Directory of Open Access Journals (Sweden)

    Mrinal Singh Rawat

    2012-09-01

    Full Text Available In spite of meticulous planning, well documentation and proper process control during software development, occurrences of certain defects are inevitable. These software defects may lead to degradation of the quality which might be the underlying cause of failure. In todays cutting edge competition its necessary to make conscious efforts to control and minimize defects in software engineering. However, these efforts cost money, time and resources. This paper identifies causative factors which in turn suggest the remedies to improve software quality and productivity. The paper also showcases on how the various defect prediction models are implemented resulting in reduced magnitude of defects.

  15. Early software reliability prediction a fuzzy logic approach

    CERN Document Server

    Pandey, Ajeet Kumar

    2013-01-01

    The development of software system with acceptable level of reliability and quality within available time frame and budget becomes a challenging objective. This objective could be achieved to some extent through early prediction of number of faults present in the software, which reduces the cost of development as it provides an opportunity to make early corrections during development process. The book presents an early software reliability prediction model that will help to grow the reliability of the software systems by monitoring it in each development phase, i.e. from requirement phase to testing phase. Different approaches are discussed in this book to tackle this challenging issue. An important approach presented in this book is a model to classify the modules into two categories (a) fault-prone and (b) not fault-prone. The methods presented in this book for assessing expected number of faults present in the software, assessing expected number of faults present at the end of each phase and classification...

  16. An Integrated Software Package to Enable Predictive Simulation Capabilities

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Yousu; Fitzhenry, Erin B.; Jin, Shuangshuang; Palmer, Bruce J.; Sharma, Poorva; Huang, Zhenyu

    2016-08-11

    The power grid is increasing in complexity due to the deployment of smart grid technologies. Such technologies vastly increase the size and complexity of power grid systems for simulation and modeling. This increasing complexity necessitates not only the use of high-performance-computing (HPC) techniques, but a smooth, well-integrated interplay between HPC applications. This paper presents a new integrated software package that integrates HPC applications and a web-based visualization tool based on a middleware framework. This framework can support the data communication between different applications. Case studies with a large power system demonstrate the predictive capability brought by the integrated software package, as well as the better situational awareness provided by the web-based visualization tool in a live mode. Test results validate the effectiveness and usability of the integrated software package.

  17. Investigating Effort Prediction of Software Projects on the ISBSG Dataset

    Directory of Open Access Journals (Sweden)

    Sanaa Elyassami

    2012-04-01

    Full Text Available Many cost estimation models have been proposed over the last three decades. In this study, we investigatefuzzy ID3 decision tree as a method for software effort estimation. Fuzzy ID software effort estimationmodel is designed by incorporating the principles of ID3 decision tree and the concepts of the fuzzy settheoretic; permitting the model to handle uncertain and imprecise data when presenting the softwareprojects.MMRE (Mean Magnitude of Relative Error and Pred(l (Prediction at level l are used, as measures ofprediction accuracy, for this study. A series of experiments is reported using ISBSG software projectsdataset. Fuzzy trees are grown using different fuzziness control thresholds.Results showed that optimizing the fuzzy ID3 parameters can improve greatly the accuracy of the generatedsoftware cost estimate.

  18. Is Three-Dimensional Soft Tissue Prediction by Software Accurate?

    Science.gov (United States)

    Nam, Ki-Uk; Hong, Jongrak

    2015-11-01

    The authors assessed whether virtual surgery, performed with a soft tissue prediction program, could correctly simulate the actual surgical outcome, focusing on soft tissue movement. Preoperative and postoperative computed tomography (CT) data for 29 patients, who had undergone orthognathic surgery, were obtained and analyzed using the Simplant Pro software. The program made a predicted soft tissue image (A) based on presurgical CT data. After the operation, we obtained actual postoperative CT data and an actual soft tissue image (B) was generated. Finally, the 2 images (A and B) were superimposed and analyzed differences between the A and B. Results were grouped in 2 classes: absolute values and vector values. In the absolute values, the left mouth corner was the most significant error point (2.36 mm). The right mouth corner (2.28 mm), labrale inferius (2.08 mm), and the pogonion (2.03 mm) also had significant errors. In vector values, prediction of the right-left side had a left-sided tendency, the superior-inferior had a superior tendency, and the anterior-posterior showed an anterior tendency. As a result, with this program, the position of points tended to be located more left, anterior, and superior than the "real" situation. There is a need to improve the prediction accuracy for soft tissue images. Such software is particularly valuable in predicting craniofacial soft tissues landmarks, such as the pronasale. With this software, landmark positions were most inaccurate in terms of anterior-posterior predictions.

  19. Predictive and Stochastic Approach for Software Effort Estimation

    Directory of Open Access Journals (Sweden)

    Srinivasa Rao T.

    2013-01-01

    Full Text Available Software cost Estimation is the process of predicting the amount of time (Effort required to build a software system. The primary reason for cost estimation is to enable the client or the developer to perform a cost-benefit analysis. Effort Estimations are determined in terms of person-months, which can be translated into actual dollar cost. The accuracy of the estimate will be depending on the amount of accurate information of the final product. Specification with uncertainty represents a range of possible final products, and not one precisely defined product. The input for the effort estimation is size of the project and cost driver parameters. A number of models have been proposed to construct a relation between software size and Effort but no model consistently and effectively predict the Effort. Accurate software effort estimation is a challenge in the software Industry. In this paper a Particle Swarm Optimization technique is proposed which operates on data sets which are clustered using the K-means clustering algorithm. PSO has been employed to generate parameters of the COCOMO model for each cluster of data values. The clusters and effort parameters are then trained to a Neural Network by using Back propagation technique, for classification of data. Testing of this model has been carried out on the COCOMO 81 dataset and also the results have been compared with standard COCOMO model and as well as the neuro fuzzy model. It is concluded from the results that the neural networks with efficient tuning of parameters by PSO operating on clusters, can generate better results and hence it can function efficiently on ever larger data sets.

  20. Improving Software Quality Prediction by Noise Filtering Techniques

    Institute of Scientific and Technical Information of China (English)

    Taghi M. Khoshgoftaar; Pierre Rebours

    2007-01-01

    Accuracy of machine learners is affected by quality of the data the learners are induced on. In this paper,quality of the training dataset is improved by removing instances detected as noisy by the Partitioning Filter. The fit datasetis first split into subsets, and different base learners are induced on each of these splits. The predictions are combined insuch a way that an instance is identified as noisy if it is misclassified by a certain number of base learners. Two versionsof the Partitioning Filter are used: Multiple-Partitioning Filter and Iterative-Partitioning Filter. The number of instancesremoved by the filters is tuned by the voting scheme of the filter and the number of iterations. The primary aim of thisstudy is to compare the predictive performances of the final models built on the filtered and the un-filtered training datasets.A case study of software measurement data of a high assurance software project is performed. It is shown that predictiveperformances of models built on the filtered fit datasets and evaluated on a noisy test dataset are generally better than thosebuilt on the noisy (un-filtered) fit dataset. However, predictive performance based on certain aggressive filters is affected bypresence of noise in the evaluation dataset.

  1. CRITICAL REVIEW OF PROSTATE CANCER PREDICTIVE TOOLS

    OpenAIRE

    Shahrokh F. Shariat; Michael W Kattan; Vickers, Andrew J; Karakiewicz, Pierre I; Scardino, Peter T.

    2009-01-01

    Prostate cancer is a very complex disease, and the decision-making process requires the clinician to balance clinical benefits, life expectancy, comorbidities, and potential treatment related side effects. Accurate prediction of clinical outcomes may help in the difficult process of making decisions related to prostate cancer. In this review, we discuss attributes of predictive tools and systematically review those available for prostate cancer. Types of tools include probability formulas, lo...

  2. Verification of FAC prediction model in pipe wall thinning prediction software 'FALSET'

    International Nuclear Information System (INIS)

    Flow accelerated corrosion (FAC) and liquid droplet impingement erosion (LDI) are the main pipe wall thinning phenomena in piping system of power plants. At present, the management is based on thinning rate and residual lifetime evaluation using pipe wall thickness measurement results. For future improvement of the management, introduction of domestic prediction code is expected. Yoneda et al. have developed original prediction software for pipe wall thinning 'FALSET', which is one-dimensional prediction for maximum thinning rate in each element in pipelines by simplifying their prediction models for local thinning rate of FAC/LDI. In this study, FAC prediction model in FALSET was verified with FAC data in domestic PWR secondary system, and prediction accuracy at present was discussed. (author)

  3. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  4. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  5. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  6. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Predictive and therapeutic markers in ovarian cancer

    Science.gov (United States)

    Gray, Joe W.; Guan, Yinghui; Kuo, Wen-Lin; Fridlyand, Jane; Mills, Gordon B.

    2013-03-26

    Cancer markers may be developed to detect diseases characterized by increased expression of apoptosis-suppressing genes, such as aggressive cancers. Genes in the human chromosomal regions, 8q24, 11q13, 20q11-q13, were found to be amplified indicating in vivo drug resistance in diseases such as ovarian cancer. Diagnosis and assessment of amplification levels certain genes shown to be amplified, including PVT1, can be useful in prediction of poor outcome of patient's response and drug resistance in ovarian cancer patients with low survival rates. Certain genes were found to be high priority therapeutic targets by the identification of recurrent aberrations involving genome sequence, copy number and/or gene expression are associated with reduced survival duration in certain diseases and cancers, specifically ovarian cancer. Therapeutics to inhibit amplification and inhibitors of one of these genes, PVT1, target drug resistance in ovarian cancer patients with low survival rates is described.

  15. Grey Prediction Based Software Stage-Effort Estimation

    Institute of Scientific and Technical Information of China (English)

    WANG Yong; SONG Qinbao; SHEN Junyi

    2007-01-01

    The software stage-effort estimation can be used to dynamically adjust software project schedule, further to help make the project finished on budget. This paper presents a grey model Verhulst based method for stage-effort estimation during software development process, a bias correction technology was used to improve the estimation accuracy. The proposed method was evaluated with a large-scale industrial software engineering database. The results are very encouraging and indicate the method has considerable potential.

  16. Predicting death from surgery for lung cancer

    DEFF Research Database (Denmark)

    O'Dowd, Emma L; Lüchtenborg, Margreet; Baldwin, David R;

    2016-01-01

    OBJECTIVES: Current British guidelines advocate the use of risk prediction scores such as Thoracoscore to estimate mortality prior to radical surgery for non-small cell lung cancer (NSCLC). A recent publication used the National Lung Cancer Audit (NLCA) to produce a score to predict 90day mortality...... (NLCA score). The aim of this study is to validate the NLCA score, and compare its performance with Thoracoscore. MATERIALS AND METHODS: We performed an internal validation using 2858 surgical patients from NLCA and an external validation using 3191 surgical patients from the Danish Lung Cancer Registry...

  17. Clinical prediction rule for nonmelanoma skin cancer

    Directory of Open Access Journals (Sweden)

    John Alexander Nova

    2015-01-01

    Full Text Available Background: Skin cancer is the most frequent neoplasia in the world. Even though ultraviolet radiation is the main cause, established prevention campaigns have not proved to be effective for controlling the incidence of this disease. Objective: To develop clinical prediction rules based on medical consultation and a questionnaire to estimate the risk of developing nonmelanoma skin cancer. Methods: This study was developed in several steps. They were: Identifying risk factors that could be possible predictors of nonmelanoma skin cancer; their clinical validation; developing a prediction rule using logistic regression; and collecting information from 962 patients in a case and control design (481 cases and 481 controls. We developed independent prediction rules for basal cell and squamous cell carcinomas. Finally, we evaluated reliability for each of the variables. Results: The variables that made up the final prediction rule were: Family history of skin cancer, history of outdoor work, age, phototypes 1-3 and the presence of poikiloderma of civatte, actinic keratosis and conjunctivitis in band. Prediction rules specificity was 87% for basal cell carcinomas and 92% for squamous cell carcinomas. Inter- and intra-observer reliability was good except for the conjunctivitis in band variable. Conclusions: The prediction rules let us calculate the individual risk of developing basal cell carcinoma and squamous cell carcinoma. This is an economic easy-to-apply tool that could be useful in primary and secondary prevention of skin cancer.

  18. Predictive Assay For Cancer Targets

    Energy Technology Data Exchange (ETDEWEB)

    Suess, A; Nguyen, C; Sorensen, K; Montgomery, J; Souza, B; Kulp, K; Dugan, L; Christian, A

    2005-09-19

    Early detection of cancer is a key element in successful treatment of the disease. Understanding the particular type of cancer involved, its origins and probable course, is also important. PhIP (2-amino-1-methyl-6 phenylimidazo [4,5-b]pyridine), a heterocyclic amine produced during the cooking of meat at elevated temperatures, has been shown to induce mammary cancer in female, Sprague-Dawley rats. Tumors induced by PhIP have been shown to contain discreet cytogenetic signature patterns of gains and losses using comparative genomic hybridization (CGH). To determine if a protein signature exists for these tumors, we are analyzing expression levels of the protein products of the above-mentioned tumors in combination with a new bulk protein subtractive assay. This assay produces a panel of antibodies against proteins that are either on or off in the tumor. Hybridization of the antibody panel onto a 2-D gel of tumor or control protein will allow for identification of a distinct protein signature in the tumor. Analysis of several gene databases has identified a number of rat homologs of human cancer genes located in these regions of gain and loss. These genes include the oncogenes c-MYK, ERBB2/NEU, THRA and tumor suppressor genes EGR1 and HDAC3. The listed genes have been shown to be estrogen-responsive, suggesting a possible link between delivery of bio-activated PhIP to the cell nucleus via estrogen receptors and gene-specific PhIP-induced DNA damage, leading to cell transformation. All three tumors showed similar silver staining patterns compared to each other, while they all were different than the control tissue. Subsequent screening of these genes against those from tumors know to be caused by other agents may produce a protein signature unique to PhIP, which can be used as a diagnostic to augment optical and radiation-based detection schemes.

  19. Target prediction and verification of miR-27a in pancreatic cancer

    Institute of Scientific and Technical Information of China (English)

    张婷婷

    2013-01-01

    Objective To predict and verify the target gene of miR-27a in pancreatic cancer by combining the result of comparative proteome analysis.Methods The bioinformatics softwares of TargetScan,PicTar and miRanda were used to predict the possible target genes of

  20. Software cost estimation, benchmarking, and risk assessment the software decision-makers' guide to predictable software development

    CERN Document Server

    Trendowicz, Adam

    2012-01-01

    Software effort estimation is a key element of software project planning and management. Yet, in industrial practice, the important role of effort estimation is often underestimated and/or misunderstood. In this book, Adam Trendowicz presents the CoBRA method (an abbreviation for Cost Estimation, Benchmarking, and Risk Assessment) for estimating the effort required to successfully complete a software development project, which uniquely combines human judgment and measurement data in order to systematically create a custom-specific effort estimation model. CoBRA goes far beyond simply predictin

  1. Static code metrics vs. process metrics for software fault prediction using Bayesian network learners

    OpenAIRE

    Stanic, Biljana

    2015-01-01

    Software fault prediction (SFP) has an important role in the process of improving software product quality by identifying fault-prone modules. Constructing quality models includes a usage of metrics that describe real world entities defined by numbers or attributes. Examining the nature of machine learning (ML), researchers proposed its algorithms as suitable for fault prediction. Moreover, information that software metrics contain will be used as statistical data necessary to build models fo...

  2. Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer.

    Science.gov (United States)

    Xu, Haoming; Moni, Mohammad Ali; Liò, Pietro

    2015-12-01

    In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease-gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git. PMID:26611766

  3. Advanced quality prediction model for software architectural knowledge sharing

    NARCIS (Netherlands)

    Liang, Peng; Jansen, Anton; Avgeriou, Paris; Tang, Antony; Xu, Lai

    2011-01-01

    In the field of software architecture, a paradigm shift is occurring from describing the outcome of architecting process to describing the Architectural Knowledge (AK) created and used during architecting. Many AK models have been defined to represent domain concepts and their relationships, and the

  4. Fuzzy Logic Based Group Maturity Rating for Software Performance Prediction

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Driven by market requirements, software services organizations have adopted various software engineering process models (such as capability maturity model (CMM), capability maturity model integration (CMMI), ISO 9001:2000, etc.) and practice of the project management concepts defined in the project management body of knowledge. While this has definitely helped organizations to bring some methods into the software development madness, there always exists a demand for comparing various groups within the organization in terms of the practice of these defined process models. Even though there exist many metrics for comparison, considering the variety of projects in terms of technology, life cycle, etc., finding a single metric that caters to this is a difficult task. This paper proposes a model for arriving at a rating on group maturity within the organization. Considering the linguistic or imprecise and uncertain nature of software measurements, fuzzy logic approach is used for the proposed model. Without the barriers like technology or life cycle difference, the proposed model helps the organization to compare different groups within it with reasonable precision.

  5. Approximator: Predicting Interruptibility in Software Development with Commodity Computers

    DEFF Research Database (Denmark)

    Tell, Paolo; Jalaliniya, Shahram; Andersen, Kristian S. M.;

    2015-01-01

    Assessing the presence and availability of a remote colleague is key in coordination in global software development but is not easily done using existing computer-mediated channels. Previous research has shown that automated estimation of interruptibility is feasible and can achieve a precision...

  6. Two Genes Might Help Predict Breast Cancer Survival

    Science.gov (United States)

    ... https://medlineplus.gov/news/fullstory_160503.html Two Genes Might Help Predict Breast Cancer Survival Research suggests ... 18, 2016 (HealthDay News) -- The activity of two genes may help predict certain breast cancer patients' chances ...

  7. Neural network based approach for time to crash prediction to cope with software aging

    Institute of Scientific and Technical Information of China (English)

    Moona Yakhchi; Javier Alonso; Mahdi Fazeli; Amir Akhavan Bitaraf; Ahmad Patooghy

    2015-01-01

    Recent studies have shown that software is one of the main reasons for computer systems unavailability. A growing ac-cumulation of software errors with time causes a phenomenon cal ed software aging. This phenomenon can result in system per-formance degradation and eventual y system hang/crash. To cope with software aging, software rejuvenation has been proposed. Software rejuvenation is a proactive technique which leads to re-moving the accumulated software errors by stopping the system, cleaning up its internal state, and resuming its normal operation. One of the main chal enges of software rejuvenation is accurately predicting the time to crash due to aging factors such as me-mory leaks. In this paper, different machine learning techniques are compared to accurately predict the software time to crash un-der different aging scenarios. Final y, by comparing the accuracy of different techniques, it can be concluded that the multilayer per-ceptron neural network has the highest prediction accuracy among al techniques studied.

  8. Quantitative hardware prediction modeling for hardware/software co-design

    NARCIS (Netherlands)

    Meeuws, R.J.

    2012-01-01

    Hardware estimation is an important factor in Hardware/Software Co-design. In this dissertation, we present the Quipu Modeling Approach, a high-level quantitative prediction model for HW/SW Partitioning using statistical methods. Our approach uses linear regression between software complexity metric

  9. Predictive Software Measures based on Z Specifications - A Case Study

    Directory of Open Access Journals (Sweden)

    Andreas Bollin

    2012-07-01

    Full Text Available Estimating the effort and quality of a system is a critical step at the beginning of every software project. It is necessary to have reliable ways of calculating these measures, and, it is even better when the calculation can be done as early as possible in the development life-cycle. Having this in mind, metrics for formal specifications are examined with a view to correlations to complexity and quality-based code measures. A case study, based on a Z specification and its implementation in ADA, analyzes the practicability of these metrics as predictors.

  10. Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects

    OpenAIRE

    Chavoya, Arturo; Lopez-Martin, Cuauhtemoc; Andalon-Garcia, Irma R.; M.E. Meda-Campaña

    2012-01-01

    Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, wher...

  11. Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction

    Science.gov (United States)

    Venkatesan, R.

    2016-01-01

    Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.

  12. Development and Application of Intelligent Prediction Software for Broken Rock Zone Thickness of Drifts

    Institute of Scientific and Technical Information of China (English)

    XU Guo-an; JING Hong-wen; LI Kai-ge; CHEN Kun-fu

    2005-01-01

    In order to seek the economical, practical and effective method of obtaining the thickness of broken rock zone, an emerging intelligent prediction method with adaptive neuro-fuzzy inference system (ANFIS) was introduced into the thickness prediction. And the software with functions of creating and applying prediction systems was developed on the platform of MATLAB6.5. The software was used to predict the broken rock zone thickness of drifts at Liangbei coal mine, Xinlong Company of Coal Industry in Xuchang city of Henan province. The results show that the predicted values accord well with the in situ measured ones. Thereby the validity of the software is validated and it provides a new approach to obtaining the broken zone thickness.

  13. Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care

    Science.gov (United States)

    Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care, a 2010 workshop sponsored by the Epidemiology and Genomics Research Program.

  14. Conceptual Software Reliability Prediction Models for Nuclear Power Plant Safety Systems

    International Nuclear Information System (INIS)

    The objective of this project is to develop a method to predict the potential reliability of software to be used in a digital system instrumentation and control system. The reliability prediction is to make use of existing measures of software reliability such as those described in IEEE Std 982 and 982.2. This prediction must be of sufficient accuracy to provide a value for uncertainty that could be used in a nuclear power plant probabilistic risk assessment (PRA). For the purposes of the project, reliability was defined to be the probability that the digital system will successfully perform its intended safety function (for the distribution of conditions under which it is expected to respond) upon demand with no unintended functions that might affect system safety. The ultimate objective is to use the identified measures to develop a method for predicting the potential quantitative reliability of a digital system. The reliability prediction models proposed in this report are conceptual in nature. That is, possible prediction techniques are proposed and trial models are built, but in order to become a useful tool for predicting reliability, the models must be tested, modified according to the results, and validated. Using methods outlined by this project, models could be constructed to develop reliability estimates for elements of software systems. This would require careful review and refinement of the models, development of model parameters from actual experience data or expert elicitation, and careful validation. By combining these reliability estimates (generated from the validated models for the constituent parts) in structural software models, the reliability of the software system could then be predicted. Modeling digital system reliability will also require that methods be developed for combining reliability estimates for hardware and software. System structural models must also be developed in order to predict system reliability based upon the reliability

  15. Comparative Study of Commercial CFD Software Performance for Prediction of Reactor Internal Flow

    International Nuclear Information System (INIS)

    Even if some CFD software developers and its users think that a state-of-the-art CFD software can be used to reasonably solve at least single-phase nuclear reactor safety problems, there remain limitations and uncertainties in the calculation result. From a regulatory perspective, the Korea Institute of Nuclear Safety (KINS) is presently conducting the performance assessment of commercial CFD software for nuclear reactor safety problems. In this study, to examine the prediction performance of commercial CFD software with the porous model in the analysis of the scale-down APR (Advanced Power Reactor Plus) internal flow, a simulation was conducted with the on-board numerical models in ANSYS CFX R.14 and FLUENT R.14. It was concluded that depending on the CFD software, the internal flow distribution of the scale-down APR was locally somewhat different. Although there was a limitation in estimating the prediction performance of the commercial CFD software owing to the limited amount of measured data, CFX R.14 showed more reasonable prediction results in comparison with FLUENT R.14. Meanwhile, owing to the difference in discretization methodology, FLUENT R.14 required more computational memory than CFX R.14 for the same grid system. Therefore, the CFD software suitable to the available computational resource should be selected for massively parallel computations

  16. Prediction of Software Requirements Stability Based on Complexity Point Measurement Using Multi-Criteria Fuzzy Approach

    Directory of Open Access Journals (Sweden)

    D. Francis Xavier Christopher

    2012-12-01

    Full Text Available Many software projects fail due to instable requirements and lack of managing the requirements changesefficiently. Software Requirements Stability Index Metric (RSI helps to evaluate the overall stability ofrequirements and also keep track of the project status. Higher the stability, less changes tends topropagate. The existing system use Function Point modeling for measuring the Requirements Stability.However, the main drawback of the existing modeling is that the complexity of non-functional requirementshas not been measured for Requirements Stability. The Non-Functional Factors plays a vital role inassessing the Requirements Stability. Numerous Measurement methods have been proposed for measuringthe software complexity. This paper proposes Multi-criteria Fuzzy Based approach for finding out thecomplexity weight based on Requirement Complexity Attributes such as Functional RequirementComplexity, Non-Functional Requirement Complexity, Input Output Complexity, Interface and FileComplexity. Based on the complexity weight, this paper computes the software complexity point. And thenpredict the Software Requirements Stability based on Software Complexity Point changes. The advantageof this model is that it is able to estimate the software complexity early which in turn predicts the SoftwareRequirement Stability during the software development life cycle.

  17. Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model.

    Science.gov (United States)

    Saul, Katherine R; Hu, Xiao; Goehler, Craig M; Vidt, Meghan E; Daly, Melissa; Velisar, Anca; Murray, Wendy M

    2015-01-01

    Several opensource or commercially available software platforms are widely used to develop dynamic simulations of movement. While computational approaches are conceptually similar across platforms, technical differences in implementation may influence output. We present a new upper limb dynamic model as a tool to evaluate potential differences in predictive behavior between platforms. We evaluated to what extent differences in technical implementations in popular simulation software environments result in differences in kinematic predictions for single and multijoint movements using EMG- and optimization-based approaches for deriving control signals. We illustrate the benchmarking comparison using SIMM-Dynamics Pipeline-SD/Fast and OpenSim platforms. The most substantial divergence results from differences in muscle model and actuator paths. This model is a valuable resource and is available for download by other researchers. The model, data, and simulation results presented here can be used by future researchers to benchmark other software platforms and software upgrades for these two platforms. PMID:24995410

  18. Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment

    DEFF Research Database (Denmark)

    Simonsen, Anja H; Mattila, Jussi; Hejl, Anne-Mette;

    2012-01-01

    Background: The PredictAD tool integrates heterogeneous data such as imaging, cerebrospinal fluid biomarkers and results from neuropsychological tests for compact visualization in an interactive user interface. This study investigated whether the software tool could assist physicians in the early...... diagnosis of Alzheimer's disease. Methods: Baseline data from 140 patients with mild cognitive impairment were selected from the Alzheimer's Disease Neuroimaging Study. Three clinical raters classified patients into 6 categories of confidence in the prediction of early Alzheimer's disease, in 4 phases...

  19. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

    OpenAIRE

    Narayanan Manikandan; Srinivasan Subha

    2016-01-01

    Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and predi...

  20. Analyzing and Predicting Effort Associated with Finding and Fixing Software Faults

    Science.gov (United States)

    Hamill, Maggie; Goseva-Popstojanova, Katerina

    2016-01-01

    Context: Software developers spend a significant amount of time fixing faults. However, not many papers have addressed the actual effort needed to fix software faults. Objective: The objective of this paper is twofold: (1) analysis of the effort needed to fix software faults and how it was affected by several factors and (2) prediction of the level of fix implementation effort based on the information provided in software change requests. Method: The work is based on data related to 1200 failures, extracted from the change tracking system of a large NASA mission. The analysis includes descriptive and inferential statistics. Predictions are made using three supervised machine learning algorithms and three sampling techniques aimed at addressing the imbalanced data problem. Results: Our results show that (1) 83% of the total fix implementation effort was associated with only 20% of failures. (2) Both safety critical failures and post-release failures required three times more effort to fix compared to non-critical and pre-release counterparts, respectively. (3) Failures with fixes spread across multiple components or across multiple types of software artifacts required more effort. The spread across artifacts was more costly than spread across components. (4) Surprisingly, some types of faults associated with later life-cycle activities did not require significant effort. (5) The level of fix implementation effort was predicted with 73% overall accuracy using the original, imbalanced data. Using oversampling techniques improved the overall accuracy up to 77%. More importantly, oversampling significantly improved the prediction of the high level effort, from 31% to around 85%. Conclusions: This paper shows the importance of tying software failures to changes made to fix all associated faults, in one or more software components and/or in one or more software artifacts, and the benefit of studying how the spread of faults and other factors affect the fix implementation

  1. Gene Expression Profiling Predicts the Development of Oral Cancer

    OpenAIRE

    Saintigny, Pierre; Zhang, Li; Fan, You-Hong; El-Naggar, Adel K.; Papadimitrakopoulou, Vali; Feng, Lei; Lee, J. Jack; Kim, Edward S.; Hong, Waun Ki; Mao, Li

    2011-01-01

    Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer develo...

  2. Genetic programming as alternative for predicting development effort of individual software projects.

    Directory of Open Access Journals (Sweden)

    Arturo Chavoya

    Full Text Available Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment.

  3. Clinical value of CT-based preoperative software assisted lung lobe volumetry for predicting postoperative pulmonary function after lung surgery

    Science.gov (United States)

    Wormanns, Dag; Beyer, Florian; Hoffknecht, Petra; Dicken, Volker; Kuhnigk, Jan-Martin; Lange, Tobias; Thomas, Michael; Heindel, Walter

    2005-04-01

    This study was aimed to evaluate a morphology-based approach for prediction of postoperative forced expiratory volume in one second (FEV1) after lung resection from preoperative CT scans. Fifteen Patients with surgically treated (lobectomy or pneumonectomy) bronchogenic carcinoma were enrolled in the study. A preoperative chest CT and pulmonary function tests before and after surgery were performed. CT scans were analyzed by prototype software: automated segmentation and volumetry of lung lobes was performed with minimal user interaction. Determined volumes of different lung lobes were used to predict postoperative FEV1 as percentage of the preoperative values. Predicted FEV1 values were compared to the observed postoperative values as standard of reference. Patients underwent lobectomy in twelve cases (6 upper lobes; 1 middle lobe; 5 lower lobes; 6 right side; 6 left side) and pneumonectomy in three cases. Automated calculation of predicted postoperative lung function was successful in all cases. Predicted FEV1 ranged from 54% to 95% (mean 75% +/- 11%) of the preoperative values. Two cases with obviously erroneous LFT were excluded from analysis. Mean error of predicted FEV1 was 20 +/- 160 ml, indicating absence of systematic error; mean absolute error was 7.4 +/- 3.3% respective 137 +/- 77 ml/s. The 200 ml reproducibility criterion for FEV1 was met in 11 of 13 cases (85%). In conclusion, software-assisted prediction of postoperative lung function yielded a clinically acceptable agreement with the observed postoperative values. This method might add useful information for evaluation of functional operability of patients with lung cancer.

  4. Robust recurrent neural network modeling for software fault detection and correction prediction

    Energy Technology Data Exchange (ETDEWEB)

    Hu, Q.P. [Quality and Innovation Research Centre, Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119260 (Singapore)]. E-mail: g0305835@nus.edu.sg; Xie, M. [Quality and Innovation Research Centre, Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119260 (Singapore)]. E-mail: mxie@nus.edu.sg; Ng, S.H. [Quality and Innovation Research Centre, Department of Industrial and Systems Engineering, National University of Singapore, Singapore 119260 (Singapore)]. E-mail: isensh@nus.edu.sg; Levitin, G. [Israel Electric Corporation, Reliability and Equipment Department, R and D Division, Aaifa 31000 (Israel)]. E-mail: levitin@iec.co.il

    2007-03-15

    Software fault detection and correction processes are related although different, and they should be studied together. A practical approach is to apply software reliability growth models to model fault detection, and fault correction process is assumed to be a delayed process. On the other hand, the artificial neural networks model, as a data-driven approach, tries to model these two processes together with no assumptions. Specifically, feedforward backpropagation networks have shown their advantages over analytical models in fault number predictions. In this paper, the following approach is explored. First, recurrent neural networks are applied to model these two processes together. Within this framework, a systematic networks configuration approach is developed with genetic algorithm according to the prediction performance. In order to provide robust predictions, an extra factor characterizing the dispersion of prediction repetitions is incorporated into the performance function. Comparisons with feedforward neural networks and analytical models are developed with respect to a real data set.

  5. Weighted K-means support vector machine for cancer prediction.

    Science.gov (United States)

    Kim, SungHwan

    2016-01-01

    To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org). PMID:27512621

  6. A new methane control and prediction software suite for longwall mines

    Science.gov (United States)

    Dougherty, Heather N.; Özgen Karacan, C.

    2011-09-01

    This paper presents technical and application aspects of a new software suite, MCP (Methane Control and Prediction), developed for addressing some of the methane and methane control issues in longwall coal mines. The software suite consists of dynamic link library (DLL) extensions to MS-Access TM, written in C++. In order to create the DLLs, various statistical, mathematical approaches, prediction and classification artificial neural network (ANN) methods were used. The current version of MCP suite (version 1.3) discussed in this paper has four separate modules that (a) predict the dynamic elastic properties of coal-measure rocks, (b) predict ventilation emissions from longwall mines, (c) determine the type of degasification system that needs to be utilized for given situations and (d) assess the production performance of gob gas ventholes that are used to extract methane from longwall gobs. These modules can be used with the data from basic logs, mining, longwall panel, productivity, and coal bed characteristics. The applications of these modules separately or in combination for methane capture and control related problems will help improve the safety of mines. The software suite's version 1.3 is discussed in this paper. Currently, it's new version 2.0 is available and can be downloaded from http://www.cdc.gov/niosh/mining/products/product180.htm free of charge. The models discussed in this paper can be found under "ancillary models" and under "methane prediction models" for specific U.S. conditions in the new version.

  7. A method to predict breast cancer stage using Medicare claims

    OpenAIRE

    Smith, Grace L.; Shih, Ya-Chen T.; Giordano, Sharon H.; Smith, Benjamin D.; Buchholz, Thomas A.

    2010-01-01

    Background In epidemiologic studies, cancer stage is an important predictor of outcomes. However, cancer stage is typically unavailable in medical insurance claims datasets, thus limiting the usefulness of such data for epidemiologic studies. Therefore, we sought to develop an algorithm to predict cancer stage based on covariates available from claims-based data. Methods We identified a cohort of 77,306 women age ≥ 66 years with stage I-IV breast cancer, using the Surveillence Epidemiology an...

  8. Factors That Predict Persistent Smoking of Cancer Survivors

    OpenAIRE

    Kim, Hyoeun; Kim, Mi-Hyun; Park, Yong-Soon; Shin, Jin Young; Song, Yun-Mi

    2015-01-01

    We conducted this cross-sectional study to elucidate factors that predict persistent smoking of the Korean cancer survivors. The subjects were 130 adult (≥19 yr old) cancer survivors who were smokers at the diagnosis of cancer and have participated in the Korean National Health and Nutrition Examination Surveys conducted from 2007 to 2011. We categorized them into the persistent smokers and the quitters, according to change in smoking status between the time of cancer diagnosis and the time o...

  9. Molecular Markers for Breast Cancer: Prediction on Tumor Behavior

    OpenAIRE

    Bruna Karina Banin Hirata; Julie Massayo Maeda Oda; Roberta Losi Guembarovski; Carolina Batista Ariza; Carlos Eduardo Coral de Oliveira; Maria Angelica Ehara Watanabe

    2014-01-01

    Breast cancer is one of the most common cancers with greater than 1,300,000 cases and 450,000 deaths each year worldwide. The development of breast cancer involves a progression through intermediate stages until the invasive carcinoma and finally into metastatic disease. Given the variability in clinical progression, the identification of markers that could predict the tumor behavior is particularly important in breast cancer. The determination of tumor markers is a useful tool for clinical m...

  10. A Comparative Study of Three Machine Learning Methods for Software Fault Prediction

    Institute of Scientific and Technical Information of China (English)

    WANG Qi; ZHU Jie; YU Bo

    2005-01-01

    The contribution of this paper is comparing three popular machine learning methods for software fault prediction. They are classification tree, neural network and case-based reasoning. First, three different classifiers are built based on these three different approaches. Second, the three different classifiers utilize the same product metrics as predictor variables to identify the fault-prone components. Third, the predicting results are compared on two aspects, how good prediction capabilities these models are, and how the models support understanding a process represented by the data.

  11. Predicting toxicity in radiotherapy for prostate cancer.

    Science.gov (United States)

    Landoni, Valeria; Fiorino, Claudio; Cozzarini, Cesare; Sanguineti, Giuseppe; Valdagni, Riccardo; Rancati, Tiziana

    2016-03-01

    This comprehensive review addresses most organs at risk involved in planning optimization for prostate cancer. It can be considered an update of a previous educational review that was published in 2009 (Fiorino et al., 2009). The literature was reviewed based on PubMed and MEDLINE database searches (from January 2009 up to September 2015), including papers in press; for each section/subsection, key title words were used and possibly combined with other more general key-words (such as radiotherapy, dose-volume effects, NTCP, DVH, and predictive model). Publications generally dealing with toxicity without any association with dose-volume effects or correlations with clinical risk factors were disregarded, being outside the aim of the review. A focus was on external beam radiotherapy, including post-prostatectomy, with conventional fractionation or moderate hypofractionation (<4Gy/fraction); extreme hypofractionation is the topic of another paper in this special issue. Gastrointestinal and urinary toxicity are the most investigated endpoints, with quantitative data published in the last 5years suggesting both a dose-response relationship and the existence of a number of clinical/patient related risk factors acting as dose-response modifiers. Some results on erectile dysfunction, bowel toxicity and hematological toxicity are also presented. PMID:27068274

  12. Accuracy of Dolphin visual treatment objective (VTO) prediction software on class III patients treated with maxillary advancement and mandibular setback

    OpenAIRE

    Peterman, Robert J.; Jiang, Shuying; Johe, Rene; Mukherjee, Padma M.

    2016-01-01

    Background Dolphin® visual treatment objective (VTO) prediction software is routinely utilized by orthodontists during the treatment planning of orthognathic cases to help predict post-surgical soft tissue changes. Although surgical soft tissue prediction is considered to be a vital tool, its accuracy is not well understood in tow-jaw surgical procedures. The objective of this study was to quantify the accuracy of Dolphin Imaging’s VTO soft tissue prediction software on class III patients tre...

  13. Molecular Markers with Predictive and Prognostic Relevance in Lung Cancer

    Directory of Open Access Journals (Sweden)

    Alphy Rose-James

    2012-01-01

    Full Text Available Lung cancer accounts for the majority of cancer-related deaths worldwide of which non-small-cell lung carcinoma alone takes a toll of around 85%. Platinum-based therapy is the stronghold for lung cancer at present. The discovery of various molecular alterations that underlie lung cancer has contributed to the development of specifically targeted therapies employing specific mutation inhibitors. Targeted chemotherapy based on molecular profiling has shown great promise in lung cancer treatment. Various molecular markers with predictive and prognostic significance in lung cancer have evolved as a result of advanced research. Testing of EGFR and Kras mutations is now a common practice among community oncologists, and more recently, ALK rearrangements have been added to this group. This paper discusses various predictive and prognostic markers that are being investigated and have shown significant relevance which can be exploited for targeted treatment in lung cancer.

  14. Comparison of an Imaging Software and Manual Prediction of Soft Tissue Changes after Orthognathic Surgery

    Directory of Open Access Journals (Sweden)

    M. S. Ahmad Akhoundi

    2012-01-01

    Full Text Available Objective: Accurate prediction of the surgical outcome is important in treating dentofacial deformities. Visualized treatment objectives usually involve manual surgical simulation based on tracing of cephalometric radiographs. Recent technical advancements have led to the use of computer assisted imaging systems in treatment planning for orthognathic surgical cases. The purpose of this study was to examine and compare the ability and reliability of digitization using Dolphin Imaging Software with traditional manual techniques and to compare orthognathic prediction with actual outcomes.Materials and Methods: Forty patients consisting of 35 women and 5 men (32 class III and 8 class II with no previous surgery were evaluated by manual tracing and indirect digitization using Dolphin Imaging Software. Reliability of each method was assessed then the two techniques were compared using paired t test.Result: The nasal tip presented the least predicted error and higher reliability. The least accurate regions in vertical plane were subnasal and upper lip, and subnasal and pogonion in horizontal plane. There were no statistically significant differences between the predictions of groups with and without genioplasty.Conclusion: Computer-generated image prediction was suitable for patient education and communication. However, efforts are still needed to improve accuracy and reliability of the prediction program and to include changes in soft tissue tension and muscle strain.

  15. Prediction of breast cancer survival through knowledge discovery in databases.

    Science.gov (United States)

    Lotfnezhad Afshar, Hadi; Ahmadi, Maryam; Roudbari, Masoud; Sadoughi, Farahnaz

    2015-01-26

    The collection of large volumes of medical data has offered an opportunity to develop prediction models for survival by the medical research community. Medical researchers who seek to discover and extract hidden patterns and relationships among large number of variables use knowledge discovery in databases (KDD) to predict the outcome of a disease. The study was conducted to develop predictive models and discover relationships between certain predictor variables and survival in the context of breast cancer. This study is Cross sectional. After data preparation, data of 22,763 female patients, mean age 59.4 years, stored in the Surveillance Epidemiology and End Results (SEER) breast cancer dataset were analyzed anonymously. IBM SPSS Statistics 16, Access 2003 and Excel 2003 were used in the data preparation and IBM SPSS Modeler 14.2 was used in the model design. Support Vector Machine (SVM) model outperformed other models in the prediction of breast cancer survival. Analysis showed SVM model detected ten important predictor variables contributing mostly to prediction of breast cancer survival. Among important variables, behavior of tumor as the most important variable and stage of malignancy as the least important variable were identified. In current study, applying of the knowledge discovery method in the breast cancer dataset predicted the survival condition of breast cancer patients with high confidence and identified the most important variables participating in breast cancer survival.

  16. Evaluating the impact of software metrics on defects prediction. Part 2

    Directory of Open Access Journals (Sweden)

    Arwa Abu Asad

    2014-03-01

    Full Text Available Software metrics are used as indicators of the quality of the developed software. Metrics can be collected from any software part such as: code, design, or requirements. In this paper, we evaluated several examples of design coupling metrics. Analysis and experiments follow hereinafter to demonstrate the use and value of those metrics. This is the second part for a paper we published in Computer Science Journal of Moldova (CSJM, V.21, N.2(62, 2013 [19]. We proposed and evaluated several design and code coupling metrics. In this part, we collected source code from Scarab open source project. This open source is selected due to the availability of bug reports. We used bug reports for further analysis and association where bugs are used to form a class for classification and prediction purposes. Metrics are collected and analyzed automatically through the developed tool. Statistical and data mining methods are then used to generalize some findings related to the collected metrics. In addition classification and prediction algorithms are used to correlate collected metrics with high level quality attributes such as maintainability and defects prediction.

  17. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

    Science.gov (United States)

    Manikandan, Narayanan; Subha, Srinivasan

    2016-01-01

    Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

  18. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Narayanan Manikandan

    2016-01-01

    Full Text Available Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

  19. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

    Science.gov (United States)

    Manikandan, Narayanan; Subha, Srinivasan

    2016-01-01

    Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used. PMID:26881271

  20. Predicting risk of cancer during HIV infection

    DEFF Research Database (Denmark)

    Borges, Álvaro H; Silverberg, Michael J; Wentworth, Deborah;

    2013-01-01

    To investigate the relationship between inflammatory [interleukin-6 (IL-6) and C-reactive protein (CRP)] and coagulation (D-dimer) biomarkers and cancer risk during HIV infection.......To investigate the relationship between inflammatory [interleukin-6 (IL-6) and C-reactive protein (CRP)] and coagulation (D-dimer) biomarkers and cancer risk during HIV infection....

  1. Submission Form for Peer-Reviewed Cancer Risk Prediction Models

    Science.gov (United States)

    If you have information about a peer-reviewd cancer risk prediction model that you would like to be considered for inclusion on this list, submit as much information as possible through the form on this page.

  2. Commercial software upgrades may significantly alter Perfusion CT parameter values in colorectal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Goh, Vicky [Mount Vernon Hospital, Paul Strickland Scanner Centre, Northwood, Middlesex (United Kingdom); Shastry, Manu; Endozo, Raymondo; Groves, Ashley M. [University College Hospital, Institute of Nuclear Medicine, London (United Kingdom); Engledow, Alec; Peck, Jacqui [University College Hospital, Department of Surgery, London (United Kingdom); Reston, Jonathan; Wellsted, David M. [University of Hertfordshire, Centre for Lifespan and Chronic Illness Research (CLiCIR), Hatfield (United Kingdom); Rodriguez-Justo, Manuel [University College Hospital, Department of Histopathology, London (United Kingdom); Taylor, Stuart A.; Halligan, Steve [University College Hospital, Specialist Radiology, London (United Kingdom)

    2011-04-15

    To determine how commercial software platform upgrades impact on derived parameters for colorectal cancer. Following ethical approval, 30 patients with suspected colorectal cancer underwent Perfusion CT using integrated 64 detector PET/CT before surgery. Analysis was performed using software based on modified distributed parameter analysis (Perfusion software version 4; Perfusion 4.0), then repeated using the previous version (Perfusion software version 3; Perfusion 3.0). Tumour blood flow (BF), blood volume (BV), mean transit time (MTT) and permeability surface area product (PS) were determined for identical regions-of-interest. Slice-by-slice and 'whole tumour' variance was assessed by Bland-Altman analysis. Mean BF, BV and PS was 20.4%, 59.5%, and 106% higher, and MTT 14.3% shorter for Perfusion 4.0 than Perfusion 3.0. The mean difference (95% limits of agreement) were +13.5 (-44.9 to 72.0), +2.61 (-0.06 to 5.28), -1.23 (-6.83 to 4.36), and +14.2 (-4.43 to 32.8) for BF, BV, MTT and PS respectively. Within subject coefficient of variation was 36.6%, 38.0%, 27.4% and 60.6% for BF, BV, MTT and PS respectively indicating moderate to poor agreement. Software version upgrades of the same software platform may result in significantly different parameter values, requiring adjustments for cross-version comparison. (orig.)

  3. Predictive factors for lymph node metastasis in early gastric cancer

    Institute of Scientific and Technical Information of China (English)

    Chang-Mu; Sung; Chen-Ming; Hsu; Jun-Te; Hsu; Ta-Sen; Yeh; Chun-Jung; Lin; Tse-Ching; Chen; Cheng-Tang; Chiu

    2010-01-01

    AIM: To analyze the predictive factors for lymph node metastasis (LNM) in early gastric cancer (EGC). METHODS: Data from patients surgically treated for gastric cancers between January 1994 and December 2007 were retrospectively collected. Clinicopathological factors were analyzed to identify predictive factors for LNM. RESULTS: Of the 2936 patients who underwent gas-trectomy and lymph node dissection, 556 were diag-nosed with EGC and included in this study. Among these, 4.1% of patients had mucosal tumors ...

  4. Models for measuring and predicting shareholder value: A study of third party software service providers

    Indian Academy of Sciences (India)

    N Viswanadham; Poornima Luthra

    2005-04-01

    In this study, we use the strategic profit model (SPM) and the economic value-added (EVA to measure shareholder value). SPM measures the return on net worth (RONW) which is defined as the return on assets (ROA) multiplied by the financial leverage. EVA is defined as the firm’s net operating profit after taxes (NOPAT) minus the capital charge. Both, RONW and EVA provide an indication of how much shareholder value a firm creates for its shareholders, year on year. With the increasing focus on creation of shareholder value and core competencies, many companies are outsourcing their information technology (IT) related activities to third party software companies. Indian software companies have become leaders in providing these services. Companies from several other countries are also competing for the top slot. We use the SPM and EVA models to analyse the four listed players of the software industry using the publicly available published data. We compare the financial data obtained from the models, and use peer average data to provide customized recommendations for each company to improve their shareholder value. Assuming that the companies follow these rules, we also predict future RONW and EVA for the companies for the financial year 2005. Finally, we make several recommendations to software providers for effectively competing in the global arena.

  5. Predicting Defects Using Information Intelligence Process Models in the Software Technology Project.

    Science.gov (United States)

    Selvaraj, Manjula Gandhi; Jayabal, Devi Shree; Srinivasan, Thenmozhi; Balasubramanie, Palanisamy

    2015-01-01

    A key differentiator in a competitive market place is customer satisfaction. As per Gartner 2012 report, only 75%-80% of IT projects are successful. Customer satisfaction should be considered as a part of business strategy. The associated project parameters should be proactively managed and the project outcome needs to be predicted by a technical manager. There is lot of focus on the end state and on minimizing defect leakage as much as possible. Focus should be on proactively managing and shifting left in the software life cycle engineering model. Identify the problem upfront in the project cycle and do not wait for lessons to be learnt and take reactive steps. This paper gives the practical applicability of using predictive models and illustrates use of these models in a project to predict system testing defects thus helping to reduce residual defects. PMID:26495427

  6. Research on software development of air temperature prediction in coal face

    Institute of Scientific and Technical Information of China (English)

    QIN Yue-ping; LIU Hong-bo; WANG Ke; LIU Jiang-yue

    2011-01-01

    With ever-increasing depth of coal mine and the continuous improvement of mechanization,heat damage has become one of the major disasters in coal mine exploitation.Established the temperature prediction models suitable for different kinds of tunnels through analysis of the heat of shafts,roadways and working faces.The average annual air temperature prediction equation from the inlets of shafts to the working faces was derived.The formula was deduced using combine method of iteration and direct calculation.The method can improve the precision of air temperature prediction,so we could establish the whole pathway air temperature prediction model with high precision.Emphasizing on the effects of leakage air to air temperature of working face and using the ideology of the finite difference method and considering the differential equation of inlet and outlet at different stages,this method can significantly improve the accuracy of temperature prediction.Program development uses Visual Basic 6.0 Language,and the Origin software was used to fit the relevant data.The predicted results shows that the air temperature generally tends to rapidly increase in the air inlet,then changes slowly on working face,and finally increases sharply in air outlet in the condition of goaf air leakage.The condition is in general consistent with the air temperature change tendency of working face with U-type ventilation system.The software can provide reliable scientific basis for reasonable ventilation,cooling measures and management of coal mine thermal hazards.

  7. Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling.

    Science.gov (United States)

    Flobak, Åsmund; Baudot, Anaïs; Remy, Elisabeth; Thommesen, Liv; Thieffry, Denis; Kuiper, Martin; Lægreid, Astrid

    2015-08-01

    Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.

  8. Parameter definition using vibration prediction software leads to significant drilling performance improvements

    Energy Technology Data Exchange (ETDEWEB)

    Amorim, Dalmo; Hanley, Chris Hanley; Fonseca, Isaac; Santos, Juliana [National Oilwell Varco, Houston TX (United States); Leite, Daltro J.; Borella, Augusto; Gozzi, Danilo [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

    2012-07-01

    field monitoring. Vibration prediction diminishes the importance of trial-and-error procedures such as drill-off tests, which are valid only for short sections. It also solves an existing lapse in Mechanical Specific Energy (MSE) real-time drilling control programs applying the theory of Teale, which states that a drilling system is perfectly efficient when it spends the exact energy to overcome the in situ rock strength. Using the proprietary software tool this paper will examine the resonant vibration modes that may be initiated while drilling with different BHA's and drill string designs, showing that the combination of a proper BHA design along with the correct selection of input parameters results in an overall improvement to drilling efficiency. Also, being the BHA predictively analyzed, it will be reduced the potential for vibration or stress fatigue in the drill string components, leading to a safer operation. In the recent years there has been an increased focus on vibration detection, analysis, and mitigation techniques, where new technologies, like the Drilling Dynamics Data Recorders (DDDR), may provide the capability to capture high frequency dynamics data at multiple points along the drilling system. These tools allow the achievement of drilling performance improvements not possible before, opening a whole new array of opportunities for optimization and for verification of predictions calculated by the drill string dynamics modeling software tool. The results of this study will identify how the dynamics from the drilling system, interacting with formation, directly relate to inefficiencies and to the possible solutions to mitigate drilling vibrations in order to improve drilling performance. Software vibration prediction and downhole measurements can be used for non-drilling operations like drilling out casing or reaming, where extremely high vibration levels - devastating to the cutting structure of the bit before it has even touched bottom - have

  9. Operational assessment of Target Acquisitions Weapons Software (TAWS) prediction performance at Nellis AFB, NV

    OpenAIRE

    Hernandez, Jerome H.

    2006-01-01

    Target Acquisition Weapons Software (TAWS) Version 3.4 is a joint Tactical Decision Aid (TDA) used to predict performance of electro-optic and electro-magnetic (EM/EO) munitions and navigation systems. TAWS is the USAF and USN mission-planning standard for laser-guided, infrared, and TV munitions and navigation systems TDAs. As TAWS continues to deploy through the mission planning community there is a need to establish a systematic approach to assessing TAWS accuracy. This study was an op...

  10. Introduction to Software Quality Prediction Technology%软件质量预测技术概述

    Institute of Scientific and Technical Information of China (English)

    高岩; 杨春晖; 熊婧

    2015-01-01

    软件质量预测是对软件质量进行早期预测和控制的方法,其原理是在软件开发的早期根据与软件质量有关的度量数据,使用机器学习或者统计学方法来构建软件质量模型,通过分析计算得到软件质量的预测值,从而对软件系统中潜在的错误进行预测和预警。从软件质量预测的概念、模型框架、应用、发展前景和面临的挑战等方面对软件质量预测进行了系统的概述。%Software quality prediction is the method to predict and control software quality in early stage.The principle of software quality prediction is to build software quality modules through machine learning and then obtain the predication by analyzing and calculating so as to forecast and monitor the potential errors in the software system according to the metrics data related to software quality in the early stage of software development. In this article, the concept, framework, application, prospects and challenges of software quality prediction are overviewed systematically.

  11. An epigenetic signature in peripheral blood predicts active ovarian cancer.

    Directory of Open Access Journals (Sweden)

    Andrew E Teschendorff

    Full Text Available BACKGROUND: Recent studies have shown that DNA methylation (DNAm markers in peripheral blood may hold promise as diagnostic or early detection/risk markers for epithelial cancers. However, to date no study has evaluated the diagnostic and predictive potential of such markers in a large case control cohort and on a genome-wide basis. PRINCIPAL FINDINGS: By performing genome-wide DNAm profiling of a large ovarian cancer case control cohort, we here demonstrate that active ovarian cancer has a significant impact on the DNAm pattern in peripheral blood. Specifically, by measuring the methylation levels of over 27,000 CpGs in blood cells from 148 healthy individuals and 113 age-matched pre-treatment ovarian cancer cases, we derive a DNAm signature that can predict the presence of active ovarian cancer in blind test sets with an AUC of 0.8 (95% CI (0.74-0.87. We further validate our findings in another independent set of 122 post-treatment cases (AUC = 0.76 (0.72-0.81. In addition, we provide evidence for a significant number of candidate risk or early detection markers for ovarian cancer. Furthermore, by comparing the pattern of methylation with gene expression data from major blood cell types, we here demonstrate that age and cancer elicit common changes in the composition of peripheral blood, with a myeloid skewing that increases with age and which is further aggravated in the presence of ovarian cancer. Finally, we show that most cancer and age associated methylation variability is found at CpGs located outside of CpG islands. SIGNIFICANCE: Our results underscore the potential of DNAm profiling in peripheral blood as a tool for detection or risk-prediction of epithelial cancers, and warrants further in-depth and higher CpG coverage studies to further elucidate this role.

  12. Technique for Early Reliability Prediction of Software Components Using Behaviour Models

    Science.gov (United States)

    Ali, Awad; N. A. Jawawi, Dayang; Adham Isa, Mohd; Imran Babar, Muhammad

    2016-01-01

    Behaviour models are the most commonly used input for predicting the reliability of a software system at the early design stage. A component behaviour model reveals the structure and behaviour of the component during the execution of system-level functionalities. There are various challenges related to component reliability prediction at the early design stage based on behaviour models. For example, most of the current reliability techniques do not provide fine-grained sequential behaviour models of individual components and fail to consider the loop entry and exit points in the reliability computation. Moreover, some of the current techniques do not tackle the problem of operational data unavailability and the lack of analysis results that can be valuable for software architects at the early design stage. This paper proposes a reliability prediction technique that, pragmatically, synthesizes system behaviour in the form of a state machine, given a set of scenarios and corresponding constraints as input. The state machine is utilized as a base for generating the component-relevant operational data. The state machine is also used as a source for identifying the nodes and edges of a component probabilistic dependency graph (CPDG). Based on the CPDG, a stack-based algorithm is used to compute the reliability. The proposed technique is evaluated by a comparison with existing techniques and the application of sensitivity analysis to a robotic wheelchair system as a case study. The results indicate that the proposed technique is more relevant at the early design stage compared to existing works, and can provide a more realistic and meaningful prediction. PMID:27668748

  13. Onco-Regulon: an integrated database and software suite for site specific targeting of transcription factors of cancer genes.

    Science.gov (United States)

    Tomar, Navneet; Mishra, Akhilesh; Mrinal, Nirotpal; Jayaram, B

    2016-01-01

    Transcription factors (TFs) bind at multiple sites in the genome and regulate expression of many genes. Regulating TF binding in a gene specific manner remains a formidable challenge in drug discovery because the same binding motif may be present at multiple locations in the genome. Here, we present Onco-Regulon (http://www.scfbio-iitd.res.in/software/onco/NavSite/index.htm), an integrated database of regulatory motifs of cancer genes clubbed with Unique Sequence-Predictor (USP) a software suite that identifies unique sequences for each of these regulatory DNA motifs at the specified position in the genome. USP works by extending a given DNA motif, in 5'→3', 3' →5' or both directions by adding one nucleotide at each step, and calculates the frequency of each extended motif in the genome by Frequency Counter programme. This step is iterated till the frequency of the extended motif becomes unity in the genome. Thus, for each given motif, we get three possible unique sequences. Closest Sequence Finder program predicts off-target drug binding in the genome. Inclusion of DNA-Protein structural information further makes Onco-Regulon a highly informative repository for gene specific drug development. We believe that Onco-Regulon will help researchers to design drugs which will bind to an exclusive site in the genome with no off-target effects, theoretically.Database URL: http://www.scfbio-iitd.res.in/software/onco/NavSite/index.htm. PMID:27515825

  14. Emerging markers of cachexia predict survival in cancer patients

    OpenAIRE

    MONDELLO, PATRIZIA; Lacquaniti, Antonio; Mondello, Stefania; Bolignano, Davide; Pitini, Vincenzo; Aloisi, Carmela; Buemi, Michele

    2014-01-01

    Background Cachexia may occur in 40% of cancer patients, representing the major cause of death in more than 20% of them. The aim of this study was to investigate the role of leptin, ghrelin and obestatin as diagnostic and predictive markers of cachexia in oncologic patients. Their impact on patient survival was also evaluated. Methods 140 adults with different cancer diagnoses were recruited. Thirty healthy volunteers served as control. Serum ghrelin, obestatin and leptin were tested at basel...

  15. Predicting delayed anxiety and depression in patients with gastrointestinal cancer

    OpenAIRE

    Nordin, K; Glimelius, B.

    1999-01-01

    The aim of this study was to examine the possibility of predicting anxiety and depression 6 months after a cancer diagnosis on the basis of measures of anxiety, depression, coping and subjective distress associated with the diagnosis and to explore the possibility of identifying individual patients with high levels of delayed anxiety and depression associated with the diagnosis. A consecutive series of 159 patients with gastrointestinal cancer were interviewed in connection with the diagnosis...

  16. Predictive images of postoperative levator resection outcome using image processing software

    Directory of Open Access Journals (Sweden)

    Mawatari Y

    2016-09-01

    Full Text Available Yuki Mawatari,1 Mikiko Fukushima2 1Igo Ophthalmic Clinic, Kagoshima, 2Department of Ophthalmology, Faculty of Life Science, Kumamoto University, Chuo-ku, Kumamoto, Japan Purpose: This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection.Methods: Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller’s muscle complex (levator resection. Predictive images were prepared from preoperative photographs using the image processing software (Adobe Photoshop®. Images of selected eyes were digitally enlarged in an appropriate manner and shown to patients prior to surgery.Results: Approximately 1 month postoperatively, we surveyed our patients using questionnaires. Fifty-six patients (89.2% were satisfied with their postoperative appearances, and 55 patients (84.8% positively responded to the usefulness of processed images to predict postoperative appearance.Conclusion: Showing processed images that predict postoperative appearance to patients prior to blepharoptosis surgery can be useful for those patients concerned with their postoperative appearance. This approach may serve as a useful tool to simulate blepharoptosis surgery. Keywords: levator resection, blepharoptosis, image processing, Adobe Photoshop® 

  17. Predictability of enantiomeric chromatographic behavior on various chiral stationary phases using typical reversed phase modeling software.

    Science.gov (United States)

    Wagdy, Hebatallah A; Hanafi, Rasha S; El-Nashar, Rasha M; Aboul-Enein, Hassan Y

    2013-09-01

    Pharmaceutical companies worldwide tend to apply chiral chromatographic separation techniques in their mass production strategy rather than asymmetric synthesis. The present work aims to investigate the predictability of chromatographic behavior of enantiomers using DryLab HPLC method development software, which is typically used to predict the effect of changing various chromatographic parameters on resolution in the reversed phase mode. Three different types of chiral stationary phases were tested for predictability: macrocyclic antibiotics-based columns (Chirobiotic V and T), polysaccharide-based chiral column (Chiralpak AD-RH), and protein-based chiral column (Ultron ES-OVM). Preliminary basic runs were implemented, then exported to DryLab after peak tracking was accomplished. Prediction of the effect of % organic mobile phase on separation was possible for separations on Chirobiotic V for several probes: racemic propranolol with 97.80% accuracy; mixture of racemates of propranolol and terbutaline sulphate, as well as, racemates of propranolol and salbutamol sulphate with average 90.46% accuracy for the effect of percent organic mobile phase and average 98.39% for the effect of pH; and racemic warfarin with 93.45% accuracy for the effect of percent organic mobile phase and average 99.64% for the effect of pH. It can be concluded that Chirobiotic V reversed phase retention mechanism follows the solvophobic theory. PMID:23775938

  18. Combining gene signatures improves prediction of breast cancer survival.

    Directory of Open Access Journals (Sweden)

    Xi Zhao

    Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves

  19. Machine learning applications in cancer prognosis and prediction

    Directory of Open Access Journals (Sweden)

    Konstantina Kourou

    2015-01-01

    Full Text Available Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs, Bayesian Networks (BNs, Support Vector Machines (SVMs and Decision Trees (DTs have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

  20. Implementation of Prediction Model for Object Oriented Software Development Effort Estimation using One Hidden Layer Neural Network

    Directory of Open Access Journals (Sweden)

    Chandra Shekhar Yadav

    2014-03-01

    Full Text Available The prediction model for object-oriented software development effort estimation using one hidden layer neural network has been implemented in this paper. This prediction model has been empirically validated on PROMISE software engineering repository dataset. Accurate prediction of software development effort and schedule is still a challenging job in software industry. This prediction model has been implemented through programming in MATLAB using one hidden layer feed forward neural network(OHFNN and results obtained from this program are compared with existing algorithms like traingda and traingdm of NNTool. By a large number of simulation work OHFNN 16-19-1 is found optimal structure for this prediction model. OHFNN 16-19-1 means 16 neurons in input layer, 19 neurons in hidden layer and 1 in output layer. Training of the neural network has been done by using back propagation with a gradient descent method. Performance of predictor is better in terms of accuracy than existing well established constructive cost estimation model (COCOMO. In this network, convergence is obtained by minimizing the root mean square error of the input patterns and optimal weight vector is determined to predict the software development effort.

  1. Parameter definition using vibration prediction software leads to significant drilling performance improvements

    Energy Technology Data Exchange (ETDEWEB)

    Amorim, Dalmo; Hanley, Chris Hanley; Fonseca, Isaac; Santos, Juliana [National Oilwell Varco, Houston TX (United States); Leite, Daltro J.; Borella, Augusto; Gozzi, Danilo [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

    2012-07-01

    field monitoring. Vibration prediction diminishes the importance of trial-and-error procedures such as drill-off tests, which are valid only for short sections. It also solves an existing lapse in Mechanical Specific Energy (MSE) real-time drilling control programs applying the theory of Teale, which states that a drilling system is perfectly efficient when it spends the exact energy to overcome the in situ rock strength. Using the proprietary software tool this paper will examine the resonant vibration modes that may be initiated while drilling with different BHA's and drill string designs, showing that the combination of a proper BHA design along with the correct selection of input parameters results in an overall improvement to drilling efficiency. Also, being the BHA predictively analyzed, it will be reduced the potential for vibration or stress fatigue in the drill string components, leading to a safer operation. In the recent years there has been an increased focus on vibration detection, analysis, and mitigation techniques, where new technologies, like the Drilling Dynamics Data Recorders (DDDR), may provide the capability to capture high frequency dynamics data at multiple points along the drilling system. These tools allow the achievement of drilling performance improvements not possible before, opening a whole new array of opportunities for optimization and for verification of predictions calculated by the drill string dynamics modeling software tool. The results of this study will identify how the dynamics from the drilling system, interacting with formation, directly relate to inefficiencies and to the possible solutions to mitigate drilling vibrations in order to improve drilling performance. Software vibration prediction and downhole measurements can be used for non-drilling operations like drilling out casing or reaming, where extremely high vibration levels - devastating to the cutting structure of the bit before it has even touched bottom - have

  2. Using Heuristic Value Prediction and Dynamic Task Granularity Resizing to Improve Software Speculation

    Directory of Open Access Journals (Sweden)

    Fan Xu

    2014-01-01

    Full Text Available Exploiting potential thread-level parallelism (TLP is becoming the key factor to improving performance of programs on multicore or many-core systems. Among various kinds of parallel execution models, the software-based speculative parallel model has become a research focus due to its low cost, high efficiency, flexibility, and scalability. The performance of the guest program under the software-based speculative parallel execution model is closely related to the speculation accuracy, the control overhead, and the rollback overhead of the model. In this paper, we first analyzed the conventional speculative parallel model and presented an analytic model of its expectation of the overall overhead, then optimized the conventional model based on the analytic model, and finally proposed a novel speculative parallel model named HEUSPEC. The HEUSPEC model includes three key techniques, namely, the heuristic value prediction, the value based correctness checking, and the dynamic task granularity resizing. We have implemented the runtime system of the model in ANSI C language. The experiment results show that when the speedup of the HEUSPEC model can reach 2.20 on the average (15% higher than conventional model when depth is equal to 3 and 4.51 on the average (12% higher than conventional model when speculative depth is equal to 7. Besides, it shows good scalability and lower memory cost.

  3. Towards early software reliability prediction for computer forensic tools (case study).

    Science.gov (United States)

    Abu Talib, Manar

    2016-01-01

    Versatility, flexibility and robustness are essential requirements for software forensic tools. Researchers and practitioners need to put more effort into assessing this type of tool. A Markov model is a robust means for analyzing and anticipating the functioning of an advanced component based system. It is used, for instance, to analyze the reliability of the state machines of real time reactive systems. This research extends the architecture-based software reliability prediction model for computer forensic tools, which is based on Markov chains and COSMIC-FFP. Basically, every part of the computer forensic tool is linked to a discrete time Markov chain. If this can be done, then a probabilistic analysis by Markov chains can be performed to analyze the reliability of the components and of the whole tool. The purposes of the proposed reliability assessment method are to evaluate the tool's reliability in the early phases of its development, to improve the reliability assessment process for large computer forensic tools over time, and to compare alternative tool designs. The reliability analysis can assist designers in choosing the most reliable topology for the components, which can maximize the reliability of the tool and meet the expected reliability level specified by the end-user. The approach of assessing component-based tool reliability in the COSMIC-FFP context is illustrated with the Forensic Toolkit Imager case study. PMID:27386276

  4. Risk Prediction Model for Colorectal Cancer: National Health Insurance Corporation Study, Korea

    OpenAIRE

    Aesun Shin; Jungnam Joo; Hye-Ryung Yang; Jeongin Bak; Yunjin Park; Jeongseon Kim; Jae Hwan Oh; Byung-Ho Nam

    2014-01-01

    PURPOSE: Incidence and mortality rates of colorectal cancer have been rapidly increasing in Korea during last few decades. Development of risk prediction models for colorectal cancer in Korean men and women is urgently needed to enhance its prevention and early detection. METHODS: Gender specific five-year risk prediction models were developed for overall colorectal cancer, proximal colon cancer, distal colon cancer, colon cancer and rectal cancer. The model was developed using data from a po...

  5. Formalized prediction of clinically significant prostate cancer: is it possible?

    Institute of Scientific and Technical Information of China (English)

    Carvell T Nguyen; Michael W Kattan

    2012-01-01

    Greater understanding of the biology and epidemiology of prostate cancer in the last several decades have led to significant advances in its management.Prostate cancer is now detected in greater numbers at lower stages of disease and is amenable to multiple forms of efficacious treatment.However,there is a lack of conclusive data demonstrating a definitive mortality benefit from this earlier diagnosis and treatment of prostate cancer.It is likely due to the treatment of a large proportion of indolent cancers that would have had little adverse impact on health or lifespan if left alone.Due to this overtreatment phenomenon,active surveillance with delayed intervention is gaining traction as a viable management approach in contemporary practice.The ability to distinguish clinically insignificant cancers from those with a high risk of progression and/or lethality is critical to the appropriate selection of patients for surveillance protocols versus immediate intervention.This chapter will review the ability of various prediction models,including risk groupings and nomograms,to predict indolent disease and determine their role in the contemporary management of clinically localized prostate cancer.

  6. Predictive value of prostate-specific antigen for prostate cancer

    DEFF Research Database (Denmark)

    Shepherd, Leah; Borges, Alvaro Humberto; Ravn, Lene;

    2014-01-01

    INTRODUCTION: Although prostate cancer (PCa) incidence is lower in HIV+ men than in HIV- men, the usefulness of prostate-specific antigen (PSA) screening in this population is not well defined and may have higher false negative rates than in HIV- men. We aimed to describe the kinetics and predict...

  7. AGR2 Predicts Tamoxifen Resistance in Postmenopausal Breast Cancer Patients

    Directory of Open Access Journals (Sweden)

    Roman Hrstka

    2013-01-01

    Full Text Available Endocrine resistance is a significant problem in breast cancer treatment. Thus identification and validation of novel resistance determinants is important to improve treatment efficacy and patient outcome. In our work, AGR2 expression was determined by qRT-PCR in Tru-Cut needle biopsies from tamoxifen-treated postmenopausal breast cancer patients. Our results showed inversed association of AGR2 mRNA levels with primary treatment response (P=0.0011 and progression-free survival (P=0.0366 in 61 ER-positive breast carcinomas. As shown by our experimental and clinical evaluations, elevated AGR2 expression predicts decreased efficacy of tamoxifen treatment. From this perspective, AGR2 is a potential predictive biomarker enabling selection of an optimal algorithm for adjuvant hormonal therapy in postmenopausal ER-positive breast cancer patients.

  8. Applications of Machine Learning in Cancer Prediction and Prognosis

    Directory of Open Access Journals (Sweden)

    Joseph A. Cruz

    2006-01-01

    Full Text Available Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25% improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

  9. Predicting the fault-proneness of class hierarchy in object-oriented software using a layered kernel

    Institute of Scientific and Technical Information of China (English)

    Peng HUANG; Jie ZHU

    2008-01-01

    A novel kernel learning method for object-oriented (OO) software fault prediction is proposed in this paper. With this method, each set of classes that has inheritance relation named class hierarchy, is treated as an elemental software model. A layered kernel is introduced to handle the tree data structure corresponding to the class hierarchy models. This method was vali-dated using both an artificial dataset and a case of industrial software from the optical communication field. Preliminary experi-ments showed that our approach is very effective in learning structured data and outperforms the traditional support vector learning methods in accurately and correctly predicting the fault-prone class hierarchy model in real-life OO software.

  10. KRAS Genomic Status Predicts the Sensitivity of Ovarian Cancer Cells to Decitabine | Office of Cancer Genomics

    Science.gov (United States)

    Decitabine, a cancer therapeutic that inhibits DNA methylation, produces variable antitumor response rates in patients with solid tumors that might be leveraged clinically with identification of a predictive biomarker. In this study, we profiled the response of human ovarian, melanoma, and breast cancer cells treated with decitabine, finding that RAS/MEK/ERK pathway activation and DNMT1 expression correlated with cytotoxic activity. Further, we showed that KRAS genomic status predicted decitabine sensitivity in low-grade and high-grade serous ovarian cancer cells.

  11. Predictive and Prognostic Factors in Colorectal Cancer: A Personalized Approach

    Directory of Open Access Journals (Sweden)

    Timothy A. Rockall

    2011-03-01

    Full Text Available It is an exciting time for all those engaged in the treatment of colorectal cancer. The advent of new therapies presents the opportunity for a personalized approach to the patient. This approach considers the complex genetic mechanisms involved in tumorigenesis in addition to classical clinicopathological staging. The potential predictive and prognostic biomarkers which have stemmed from the study of the genetic basis of colorectal cancer and therapeutics are discussed with a focus on mismatch repair status, KRAS, BRAF, 18qLOH, CIMP and TGF-β.

  12. Dynamic modularity in protein interaction networks predicts breast cancer outcome

    DEFF Research Database (Denmark)

    Taylor, Ian W; Linding, Rune; Warde-Farley, David;

    2009-01-01

    in biochemical structure were observed between the two types of hubs. Signaling domains were found more often in intermodular hub proteins, which were also more frequently associated with oncogenesis. Analysis of two breast cancer patient cohorts revealed that altered modularity of the human interactome may...... to predict patient outcome. An analysis of hub proteins identified intermodular hub proteins that are co-expressed with their interacting partners in a tissue-restricted manner and intramodular hub proteins that are co-expressed with their interacting partners in all or most tissues. Substantial differences...... be useful as an indicator of breast cancer prognosis....

  13. Predictive Modeling: A New Paradigm for Managing Endometrial Cancer.

    Science.gov (United States)

    Bendifallah, Sofiane; Daraï, Emile; Ballester, Marcos

    2016-03-01

    With the abundance of new options in diagnostic and treatment modalities, a shift in the medical decision process for endometrial cancer (EC) has been observed. The emergence of individualized medicine and the increasing complexity of available medical data has lead to the development of several prediction models. In EC, those clinical models (algorithms, nomograms, and risk scoring systems) have been reported, especially for stratifying and subgrouping patients, with various unanswered questions regarding such things as the optimal surgical staging for lymph node metastasis as well as the assessment of recurrence and survival outcomes. In this review, we highlight existing prognostic and predictive models in EC, with a specific focus on their clinical applicability. We also discuss the methodologic aspects of the development of such predictive models and the steps that are required to integrate these tools into clinical decision making. In the future, the emerging field of molecular or biochemical markers research may substantially improve predictive and treatment approaches. PMID:26577116

  14. Multitask learning improves prediction of cancer drug sensitivity

    Science.gov (United States)

    Yuan, Han; Paskov, Ivan; Paskov, Hristo; González, Alvaro J.; Leslie, Christina S.

    2016-01-01

    Precision oncology seeks to predict the best therapeutic option for individual patients based on the molecular characteristics of their tumors. To assess the preclinical feasibility of drug sensitivity prediction, several studies have measured drug responses for cytotoxic and targeted therapies across large collections of genomically and transcriptomically characterized cancer cell lines and trained predictive models using standard methods like elastic net regression. Here we use existing drug response data sets to demonstrate that multitask learning across drugs strongly improves the accuracy and interpretability of drug prediction models. Our method uses trace norm regularization with a highly efficient ADMM (alternating direction method of multipliers) optimization algorithm that readily scales to large data sets. We anticipate that our approach will enhance efforts to exploit growing drug response compendia in order to advance personalized therapy. PMID:27550087

  15. Predicting human genetic interactions from cancer genome evolution.

    Directory of Open Access Journals (Sweden)

    Xiaowen Lu

    Full Text Available Synthetic Lethal (SL genetic interactions play a key role in various types of biological research, ranging from understanding genotype-phenotype relationships to identifying drug-targets against cancer. Despite recent advances in empirical measuring SL interactions in human cells, the human genetic interaction map is far from complete. Here, we present a novel approach to predict this map by exploiting patterns in cancer genome evolution. First, we show that empirically determined SL interactions are reflected in various gene presence, absence, and duplication patterns in hundreds of cancer genomes. The most evident pattern that we discovered is that when one member of an SL interaction gene pair is lost, the other gene tends not to be lost, i.e. the absence of co-loss. This observation is in line with expectation, because the loss of an SL interacting pair will be lethal to the cancer cell. SL interactions are also reflected in gene expression profiles, such as an under representation of cases where the genes in an SL pair are both under expressed, and an over representation of cases where one gene of an SL pair is under expressed, while the other one is over expressed. We integrated the various previously unknown cancer genome patterns and the gene expression patterns into a computational model to identify SL pairs. This simple, genome-wide model achieves a high prediction power (AUC = 0.75 for known genetic interactions. It allows us to present for the first time a comprehensive genome-wide list of SL interactions with a high estimated prediction precision, covering up to 591,000 gene pairs. This unique list can potentially be used in various application areas ranging from biotechnology to medical genetics.

  16. Postoperative Nomogram for Predicting Cancer-Specific Mortality in Medullary Thyroid Cancer

    Science.gov (United States)

    Ho, Allen S.; Wang, Lu; Palmer, Frank L.; Yu, Changhong; Toset, Arnbjorn; Patel, Snehal; Kattan, Michael W.; Tuttle, R. Michael; Ganly, Ian

    2016-01-01

    Background Medullary thyroid cancer (MTC) is a rare thyroid cancer accounting for 5 % of all thyroid malignancies. The purpose of our study was to design a predictive nomogram for cancer-specific mortality (CSM) utilizing clinical, pathological, and biochemical variables in patients with MTC. Methods MTC patients managed entirely at Memorial Sloan-Kettering Cancer Center between 1986 and 2010 were identified. Patient, tumor, and treatment characteristics were recorded, and variables predictive of CSM were identified by univariable analyses. A multivariable competing risk model was then built to predict the 10-year cancer specific mortality of MTC. All predictors of interest were added in the starting full model before selection, including age, gender, pre- and postoperative serum calcitonin, pre- and postoperative CEA, RET mutation status, perivascular invasion, margin status, pathologic T status, pathologic N status, and M status. Stepdown method was used in model selection to choose predictive variables. Results Of 249 MTC patients, 22.5 % (56/249) died from MTC, whereas 6.4 % (16/249) died secondary to other causes. Mean follow-up period was 87 ± 67 months. The seven variables with the highest predictive accuracy for cancer specific mortality included age, gender, postoperative calcitonin, perivascular invasion, pathologic T status, pathologic N status, and M status. These variables were used to create the final nomogram. Discrimination from the final nomogram was measured at 0.77 with appropriate calibration. Conclusions We describe the first nomogram that estimates cause-specific mortality in individual patients with MTC. This predictive nomogram will facilitate patient counseling in terms of prognosis and subsequent clinical follow up. PMID:25366585

  17. Software reliability

    CERN Document Server

    Bendell, A

    1986-01-01

    Software Reliability reviews some fundamental issues of software reliability as well as the techniques, models, and metrics used to predict the reliability of software. Topics covered include fault avoidance, fault removal, and fault tolerance, along with statistical methods for the objective assessment of predictive accuracy. Development cost models and life-cycle cost models are also discussed. This book is divided into eight sections and begins with a chapter on adaptive modeling used to predict software reliability, followed by a discussion on failure rate in software reliability growth mo

  18. Telomerase-Independent Paths to Immortality in Predictable Cancer Subtypes

    Directory of Open Access Journals (Sweden)

    Stephen T Durant

    2012-01-01

    Full Text Available The vast majority of cancers commandeer the activity of telomerase - the remarkable enzyme responsible for prolonging cellular lifespan by maintaining the length of telomeres at the ends of chromosomes. Telomerase is only normally active in embryonic and highly proliferative somatic cells. Thus, targeting telomerase is an attractive anti-cancer therapeutic rationale currently under investigation in various phases of clinical development. However, previous reports suggest that an average of 10-15% of all cancers lose the functional activity of telomerase and most of these turn to an Alternative Lengthening of Telomeres pathway (ALT. ALT-positive tumours will therefore not respond to anti-telomerase therapies and there is a real possibility that such drugs would be toxic to normal telomerase-utilising cells and ultimately select for resistant cells that activate an ALT mechanism. ALT exploits certain DNA damage response (DDR components to counteract telomere shortening and rapid trimming. ALT has been reported in many cancer subtypes including sarcoma, gastric carcinoma, central nervous system malignancies, subtypes of kidney (Wilm's Tumour and bladder carcinoma, mesothelioma, malignant melanoma and germ cell testicular cancers to name but a few. A recent heroic study that analysed ALT in over six thousand tumour samples supports this historical spread, although only reporting an approximate 4% prevalence. This review highlights the various methods of ALT detection, unravels several molecular ALT models thought to promote telomere maintenance and elongation, spotlights the DDR components known to facilitate these and explores why certain tissues are more likely to subvert DDR away from its usually protective functions, resulting in a predictive pattern of prevalence in specific cancer subsets.

  19. Development of Interpretable Predictive Models for BPH and Prostate Cancer

    Science.gov (United States)

    Bermejo, Pablo; Vivo, Alicia; Tárraga, Pedro J; Rodríguez-Montes, JA

    2015-01-01

    BACKGROUND Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. METHODS An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. RESULTS Statistical dependence with PC and BPH was found for prostate volume (P-value < 0.001), PSA (P-value < 0.001), international prostate symptom score (IPSS; P-value < 0.001), digital rectal examination (DRE; P-value < 0.001), age (P-value < 0.002), antecedents (P-value < 0.006), and meat consumption (P-value < 0.08). The two predictive models that were constructed selected a subset of these, namely, volume, PSA, DRE, and IPSS, obtaining an area under the ROC curve (AUC) between 72% and 80% for both PC and BPH prediction. CONCLUSION PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced. PMID:25780348

  20. Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.

    Directory of Open Access Journals (Sweden)

    Mary E Sehl

    Full Text Available Cancer stem cells (CSCs possess capacity to both self-renew and generate all cells within a tumor, and are thought to drive tumor recurrence. Targeting the stem cell niche to eradicate CSCs represents an important area of therapeutic development. The complex nature of many interacting elements of the stem cell niche, including both intracellular signals and microenvironmental growth factors and cytokines, creates a challenge in choosing which elements to target, alone or in combination. Stochastic stimulation techniques allow for the careful study of complex systems in biology and medicine and are ideal for the investigation of strategies aimed at CSC eradication. We present a mathematical model of the breast cancer stem cell (BCSC niche to predict population dynamics during carcinogenesis and in response to treatment. Using data from cell line and mouse xenograft experiments, we estimate rates of interconversion between mesenchymal and epithelial states in BCSCs and find that EMT/MET transitions occur frequently. We examine bulk tumor growth dynamics in response to alterations in the rate of symmetric self-renewal of BCSCs and find that small changes in BCSC behavior can give rise to the Gompertzian growth pattern observed in breast tumors. Finally, we examine stochastic reaction kinetic simulations in which elements of the breast cancer stem cell niche are inhibited individually and in combination. We find that slowing self-renewal and disrupting the positive feedback loop between IL-6, Stat3 activation, and NF-κB signaling by simultaneous inhibition of IL-6 and HER2 is the most effective combination to eliminate both mesenchymal and epithelial populations of BCSCs. Predictions from our model and simulations show excellent agreement with experimental data showing the efficacy of combined HER2 and Il-6 blockade in reducing BCSC populations. Our findings will be directly examined in a planned clinical trial of combined HER2 and IL-6 targeted

  1. Development of an artificial neural network-based software for prediction of power plant canal water discharge temperature

    Energy Technology Data Exchange (ETDEWEB)

    Romero, C.E.; Shan, J.F. [Lehigh University, Bethlehem, PA (United States). Energy Research Center

    2005-11-01

    Power plant cooling water systems that interact with nearby effluents are complex non-linear, large-time-delay systems. A neural network-based software tool was developed for prediction of the canal water discharge temperature at a coal-fired power plant as a function of plant operating parameters and local weather conditions, including tide information. The plant has four units totaling an installed capacity of 1550 MW and its water thermal discharge is environmentally regulated. In the summer months, when the price of electricity is very profitable and the risk of exceeding the canal temperature limit is greater, the tradeoff between maximum generation and environmental compliance violations is financially significant. The software is a predictive tool to assist in scheduling load generation among the plant's four units without exceeding a thermal discharge limit of 95{sup o}F. Back propagation neural network architectures were trained using plant operating data with an 'off-set' component. The artificial intelligence models produced reasonable trends for year-round prediction and different operational scenarios. Comparison of measured and predicted canal temperatures indicated an accuracy of less than 0.3{sup o}F over the range between 90 and 95{sup o}F. The software tool was developed as an Object Linking and Embedding (OLE) for Process Control (OPC) client, with real-time communication and interface with the plant Distributed Control System (DCS).

  2. Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

    Science.gov (United States)

    Madhu, B.; Ashok, N. C.; Balasubramanian, S.

    2014-11-01

    Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

  3. SeqAPASS (Sequence Alignment to Predict Across Species Susceptibility) software and documentation

    Science.gov (United States)

    SeqAPASS is a software application facilitates rapid and streamlined, yet transparent, comparisons of the similarity of toxicologically-significant molecular targets across species. The present application facilitates analysis of primary amino acid sequence similarity (including ...

  4. Applying a radiomics approach to predict prognosis of lung cancer patients

    Science.gov (United States)

    Emaminejad, Nastaran; Yan, Shiju; Wang, Yunzhi; Qian, Wei; Guan, Yubao; Zheng, Bin

    2016-03-01

    Radiomics is an emerging technology to decode tumor phenotype based on quantitative analysis of image features computed from radiographic images. In this study, we applied Radiomics concept to investigate the association among the CT image features of lung tumors, which are either quantitatively computed or subjectively rated by radiologists, and two genomic biomarkers namely, protein expression of the excision repair cross-complementing 1 (ERCC1) genes and a regulatory subunit of ribonucleotide reductase (RRM1), in predicting disease-free survival (DFS) of lung cancer patients after surgery. An image dataset involving 94 patients was used. Among them, 20 had cancer recurrence within 3 years, while 74 patients remained DFS. After tumor segmentation, 35 image features were computed from CT images. Using the Weka data mining software package, we selected 10 non-redundant image features. Applying a SMOTE algorithm to generate synthetic data to balance case numbers in two DFS ("yes" and "no") groups and a leave-one-case-out training/testing method, we optimized and compared a number of machine learning classifiers using (1) quantitative image (QI) features, (2) subjective rated (SR) features, and (3) genomic biomarkers (GB). Data analyses showed relatively lower correlation among the QI, SR and GB prediction results (with Pearson correlation coefficients 0.5). Among them, using QI yielded the highest performance.

  5. Predictive value of the official cancer alarm symptoms in general practice

    DEFF Research Database (Denmark)

    Krasnik Huggenberger, Ivan; Andersen, John Sahl

    2015-01-01

    Introduction: The objective of this study was to investigate the evidence for positive predictive value (PPV) of alarm symptoms and combinations of symptoms for colorectal cancer, breast cancer, prostate cancer and lung cancer in general practice. Methods: This study is based on a literature sear...

  6. Breast cancer biomarkers predict weight loss after gastric bypass surgery

    Directory of Open Access Journals (Sweden)

    Sauter Edward R

    2012-01-01

    Full Text Available Abstract Background Obesity has long been associated with postmenopausal breast cancer risk and more recently with premenopausal breast cancer risk. We previously observed that nipple aspirate fluid (n levels of prostate specific antigen (PSA were associated with obesity. Serum (s levels of adiponectin are lower in women with higher body mass index (BMI and with breast cancer. We conducted a prospective study of obese women who underwent gastric bypass surgery to determine: 1 change in n- and s-adiponectin and nPSA after surgery and 2 if biomarker change is related to change in BMI. Samples (30-s, 28-n and BMI were obtained from women 0, 3, 6 and 12 months after surgery. Findings There was a significant increase after surgery in pre- but not postmenopausal women at all time points in s-adiponectin and at 3 and 6 months in n-adiponectin. Low n-PSA and high s-adiponectin values were highly correlated with decrease in BMI from baseline. Conclusions Adiponectin increases locally in the breast and systemically in premenopausal women after gastric bypass. s-adiponectin in pre- and nPSA in postmenopausal women correlated with greater weight loss. This study provides preliminary evidence for biologic markers to predict weight loss after gastric bypass surgery.

  7. Assays for predicting and monitoring responses to lung cancer immunotherapy

    Institute of Scientific and Technical Information of China (English)

    Cristina Teixid; Niki Karachaliou; Maria Gonzlez-Cao; Daniela Morales-Espinosa; Rafael Rosell

    2015-01-01

    AbstrAct Immunotherapy has become a key strategy for cancer treatment, and two immune checkpoints, namely, programmed cell death 1 (PD-1) and its ligand (PD-L1), have recently emerged as important targets. hTe interaction blockade of PD-1 and PD-L1 demonstrated promising activity and antitumor effcacy in early phase clinical trials for advanced solid tumors such as non-small cell lung cancer (NSCLC). Many cell types in multiple tissues express PD-L1 as well as several tumor types, thereby suggesting that the ligand may play important roles in inhibiting immune responses throughout the body. hTerefore, PD-L1 is a critical immunomodulating component within the lung microenvironment, but the correlation between PD-L1 expression and prognosis is controversial. More evidence is required to support the use of PD-L1 as a potential predictive biomarker. Clinical trials have measured PD-L1 in tumor tissues by immunohistochemistry (IHC) with different antibodies, but the assessment of PD-L1 is not yet standardized. Some commercial antibodies lack speciifcity and their reproducibility has not been fully evaluated. Further studies are required to clarify the optimal IHC assay as well as to predict and monitor the immune responses of the PD-1/PD-L1 pathway.

  8. Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics | Office of Cancer Genomics

    Science.gov (United States)

    High cancer death rates indicate the need for new anticancer therapeutic agents. Approaches to discovering new cancer drugs include target-based drug discovery and phenotypic screening. Here, we identified phosphodiesterase 3A modulators as cell-selective cancer cytotoxic compounds through phenotypic compound library screening and target deconvolution by predictive chemogenomics.

  9. Breathing adapted radiotherapy: a 4D gating software for lung cancer

    International Nuclear Information System (INIS)

    Physiological respiratory motion of tumors growing in the lung can be corrected with respiratory gating when treated with radiotherapy (RT). The optimal respiratory phase for beam-on may be assessed with a respiratory phase optimizer (RPO), a 4D image processing software developed with this purpose. Fourteen patients with lung cancer were included in the study. Every patient underwent a 4D-CT providing ten datasets of ten phases of the respiratory cycle (0-100% of the cycle). We defined two morphological parameters for comparison of 4D-CT images in different respiratory phases: tumor-volume to lung-volume ratio and tumor-to-spinal cord distance. The RPO automatized the calculations (200 per patient) of these parameters for each phase of the respiratory cycle allowing to determine the optimal interval for RT. Lower lobe lung tumors not attached to the diaphragm presented with the largest motion with breathing. Maximum inspiration was considered the optimal phase for treatment in 4 patients (28.6%). In 7 patients (50%), however, the RPO showed a most favorable volumetric and spatial configuration in phases other than maximum inspiration. In 2 cases (14.4%) the RPO showed no benefit from gating. This tool was not conclusive in only one case. The RPO software presented in this study can help to determine the optimal respiratory phase for gated RT based on a few simple morphological parameters. Easy to apply in daily routine, it may be a useful tool for selecting patients who might benefit from breathing adapted RT

  10. A Panel of Cancer Testis Antigens and Clinical Risk Factors to Predict Metastasis in Colorectal Cancer

    Directory of Open Access Journals (Sweden)

    Ramyar Molania

    2014-01-01

    Full Text Available Colorectal cancer (CRC is the third common carcinoma with a high rate of mortality worldwide and several studies have investigated some molecular and clinicopathological markers for diagnosis and prognosis of its malignant phenotypes. The aim of this study is to evaluate expression frequency of PAGE4, SCP-1, and SPANXA/D cancer testis antigen (CTA genes as well as some clinical risk markers to predict liver metastasis of colorectal cancer patients. The expression frequency of PAGE4, SCP-1, and SPANXA/D cancer/testis antigen (CTA genes was obtained using reverse transcription polymerase chain reaction (RT-PCR assay in 90 colorectal tumor samples including both negative and positive liver metastasis tumors. Statistical analysis was performed to assess the association of three studied genes and clinical risk factors with CRC liver metastasis. The frequency of PAGE4 and SCP-1 genes expression was significantly higher in the primary tumours with liver metastasis when statistically compared with primary tumors with no liver metastasis (P<0.05. Among all clinical risk factors studied, the lymph node metastasis and the depth of invasion were statistically correlated with liver metastasis of CRC patients. In addition, using multiple logistic regression, we constructed a model based on PAGE4 and lymph node metastasis to predict liver metastasis of CRC.

  11. A Serum Protein Profile Predictive of the Resistance to Neoadjuvant Chemotherapy in Advanced Breast Cancers*

    OpenAIRE

    Hyung, Seok-Won; Lee, Min Young; Yu, Jong-Han; Shin, Byunghee; Jung, Hee-Jung; Park, Jong-Moon; Han, Wonshik; Lee, Kyung-min; Moon, Hyeong-Gon; Zhang, Hui; Aebersold, Ruedi; Hwang, Daehee; Lee, Sang-Won; Yu, Myeong-Hee; Noh, Dong-Young

    2011-01-01

    Prediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to N...

  12. Prediction of epigenetically regulated genes in breast cancer cell lines

    Energy Technology Data Exchange (ETDEWEB)

    Loss, Leandro A; Sadanandam, Anguraj; Durinck, Steffen; Nautiyal, Shivani; Flaucher, Diane; Carlton, Victoria EH; Moorhead, Martin; Lu, Yontao; Gray, Joe W; Faham, Malek; Spellman, Paul; Parvin, Bahram

    2010-05-04

    panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.

  13. Computational protein biomarker prediction: a case study for prostate cancer

    Directory of Open Access Journals (Sweden)

    Adam Bao-Ling

    2004-03-01

    Full Text Available Abstract Background Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates. Results Thorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably. Conclusions Modern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions.

  14. Quantitative imaging features to predict cancer status in lung nodules

    Science.gov (United States)

    Liu, Ying; Balagurunathan, Yoganand; Atwater, Thomas; Antic, Sanja; Li, Qian; Walker, Ronald; Smith, Gary T.; Massion, Pierre P.; Schabath, Matthew B.; Gillies, Robert J.

    2016-03-01

    Background: We propose a systematic methodology to quantify incidentally identified lung nodules based on observed radiological traits on a point scale. These quantitative traits classification model was used to predict cancer status. Materials and Methods: We used 102 patients' low dose computed tomography (LDCT) images for this study, 24 semantic traits were systematically scored from each image. We built a machine learning classifier in cross validation setting to find best predictive imaging features to differentiate malignant from benign lung nodules. Results: The best feature triplet to discriminate malignancy was based on long axis, concavity and lymphadenopathy with average AUC of 0.897 (Accuracy of 76.8%, Sensitivity of 64.3%, Specificity of 90%). A similar semantic triplet optimized on Sensitivity/Specificity (Youden's J index) included long axis, vascular convergence and lymphadenopathy which had an average AUC of 0.875 (Accuracy of 81.7%, Sensitivity of 76.2%, Specificity of 95%). Conclusions: Quantitative radiological image traits can differentiate malignant from benign lung nodules. These semantic features along with size measurement enhance the prediction accuracy.

  15. The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment.

    Science.gov (United States)

    Eisenhaber, Birgit; Kuchibhatla, Durga; Sherman, Westley; Sirota, Fernanda L; Berezovsky, Igor N; Wong, Wing-Cheong; Eisenhaber, Frank

    2016-01-01

    As biomolecular sequencing is becoming the main technique in life sciences, functional interpretation of sequences in terms of biomolecular mechanisms with in silico approaches is getting increasingly significant. Function prediction tools are most powerful for protein-coding sequences; yet, the concepts and technologies used for this purpose are not well reflected in bioinformatics textbooks. Notably, protein sequences typically consist of globular domains and non-globular segments. The two types of regions require cardinally different approaches for function prediction. Whereas the former are classic targets for homology-inspired function transfer based on remnant, yet statistically significant sequence similarity to other, characterized sequences, the latter type of regions are characterized by compositional bias or simple, repetitive patterns and require lexical analysis and/or empirical sequence pattern-function correlations. The recipe for function prediction recommends first to find all types of non-globular segments and, then, to subject the remaining query sequence to sequence similarity searches. We provide an updated description of the ANNOTATOR software environment as an advanced example of a software platform that facilitates protein sequence-based function prediction. PMID:27115649

  16. The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment.

    Science.gov (United States)

    Eisenhaber, Birgit; Kuchibhatla, Durga; Sherman, Westley; Sirota, Fernanda L; Berezovsky, Igor N; Wong, Wing-Cheong; Eisenhaber, Frank

    2016-01-01

    As biomolecular sequencing is becoming the main technique in life sciences, functional interpretation of sequences in terms of biomolecular mechanisms with in silico approaches is getting increasingly significant. Function prediction tools are most powerful for protein-coding sequences; yet, the concepts and technologies used for this purpose are not well reflected in bioinformatics textbooks. Notably, protein sequences typically consist of globular domains and non-globular segments. The two types of regions require cardinally different approaches for function prediction. Whereas the former are classic targets for homology-inspired function transfer based on remnant, yet statistically significant sequence similarity to other, characterized sequences, the latter type of regions are characterized by compositional bias or simple, repetitive patterns and require lexical analysis and/or empirical sequence pattern-function correlations. The recipe for function prediction recommends first to find all types of non-globular segments and, then, to subject the remaining query sequence to sequence similarity searches. We provide an updated description of the ANNOTATOR software environment as an advanced example of a software platform that facilitates protein sequence-based function prediction.

  17. microProtein Prediction Program (miP3): A Software for Predicting microProteins and Their Target Transcription Factors.

    Science.gov (United States)

    de Klein, Niek; Magnani, Enrico; Banf, Michael; Rhee, Seung Yon

    2015-01-01

    An emerging concept in transcriptional regulation is that a class of truncated transcription factors (TFs), called microProteins (miPs), engages in protein-protein interactions with TF complexes and provides feedback controls. A handful of miP examples have been described in the literature but the extent of their prevalence is unclear. Here we present an algorithm that predicts miPs and their target TFs from a sequenced genome. The algorithm is called miP prediction program (miP3), which is implemented in Python. The software will help shed light on the prevalence, biological roles, and evolution of miPs. Moreover, miP3 can be used to predict other types of miP-like proteins that may have evolved from other functional classes such as kinases and receptors. The program is freely available and can be applied to any sequenced genome. PMID:26060811

  18. microProtein Prediction Program (miP3: A Software for Predicting microProteins and Their Target Transcription Factors

    Directory of Open Access Journals (Sweden)

    Niek de Klein

    2015-01-01

    Full Text Available An emerging concept in transcriptional regulation is that a class of truncated transcription factors (TFs, called microProteins (miPs, engages in protein-protein interactions with TF complexes and provides feedback controls. A handful of miP examples have been described in the literature but the extent of their prevalence is unclear. Here we present an algorithm that predicts miPs and their target TFs from a sequenced genome. The algorithm is called miP prediction program (miP3, which is implemented in Python. The software will help shed light on the prevalence, biological roles, and evolution of miPs. Moreover, miP3 can be used to predict other types of miP-like proteins that may have evolved from other functional classes such as kinases and receptors. The program is freely available and can be applied to any sequenced genome.

  19. Positive Predictive Values in Diagnosis of Incidental Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Caner Ediz

    2016-03-01

    Full Text Available Objective: Although the incidence of incidental prostate cancer (IPCa decreases in recent years; for patients who performed by transurethral resection of the prostate (TURP due to bladder outlet obstruction with or without prostatism symptoms (BPH, it is still can be seen. This article purposes to answer two questions a for urologist, which clinical parameters including obesity and smoking have positive predictive value. b for pathologists; which materials are wholly sampled for reducing the cancer ? Methods: We evaluated 1315 cases who were per­formed by TURP due to bladder outlet obstruction with or without prostatism symptoms the years 2006-2015. The ages of the patients, smoking, body mass index (BMI, digital rectal examination (DRE findings, preoperative prostate specific antigen (PSA levels, uroflow values, to­tal prostate volume determined by suprapubic ultrasound and Gleason score were recorded. We analyzed the re­lationship between these parameters and IPCa. These situation compared with benign prostate tissue materials. Results: Totally 31 cases (2.35% were found in the IPCa. While the cases of 24 were pT1a, 7 cases were pT1b. Age, body mass index, PSA, peak current speed and mean flow rate parameters respectively 8.887, 5.668, 9.660, 4.814 and 3.716 times as an incidental effect in detecting prostate cancer has been concluded. Conclusion: Older patient age, over the 25 kg/m2 of BMI, over the 4 ng/dl of PSA levels, the peak flow rate less than 10 ml/sec and the mean flow rate less than 5 ml/sec might be independent risk factors for detecting IPCa. More ex­ternal validation is needed for confirming our results.

  20. CORAL software: prediction of carcinogenicity of drugs by means of the Monte Carlo method.

    Science.gov (United States)

    Toropova, Alla P; Toropov, Andrey A

    2014-02-14

    Methodology of building up and validation of models for carcinogenic potentials of drugs by means of the CORAL software is described. The QSAR analysis by the CORAL software includes three phases: (i) definition of preferable parameters for the optimization procedure that gives maximal correlation coefficient between endpoint and an optimal descriptor that is calculated with so-called correlation weights of various molecular features; (ii) detection of molecular features with stable positive correlation weights or vice versa stable negative correlation weights (molecular features which are characterized by solely positive or solely negative correlation weights obtained for several starts of the Monte Carlo optimization are a basis for mechanistic interpretations of the model); and (iii) building up the model that is satisfactory from point of view of reliable probabilistic criteria and OECD principles. The methodology is demonstrated for the case of carcinogenicity of a large set (n = 1464) of organic compounds which are potential or actual pharmaceutical agents.

  1. Colon cancer prediction with genetic profiles using intelligent techniques

    Science.gov (United States)

    Alladi, Subha Mahadevi; P, Shinde Santosh; Ravi, Vadlamani; Murthy, Upadhyayula Suryanarayana

    2008-01-01

    Micro array data provides information of expression levels of thousands of genes in a cell in a single experiment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. In our present study we have used the benchmark colon cancer data set for analysis. Feature selection is done using t‐statistic. Comparative study of class prediction accuracy of 3 different classifiers viz., support vector machine (SVM), neural nets and logistic regression was performed using the top 10 genes ranked by the t‐statistic. SVM turned out to be the best classifier for this dataset based on area under the receiver operating characteristic curve (AUC) and total accuracy. Logistic Regression ranks as the next best classifier followed by Multi Layer Perceptron (MLP). The top 10 genes selected by us for classification are all well documented for their variable expression in colon cancer. We conclude that SVM together with t-statistic based feature selection is an efficient and viable alternative to popular techniques. PMID:19238250

  2. A Case Study of Software Product Line for Business Applications Changeability Prediction

    OpenAIRE

    Roško, Zdravko; Strahonja, Vjeran

    2014-01-01

    The changeability, a sub-characteristic of maintainability, refers to the level of effort which is required to do modifications to a software product line (SPL) application component. Assuming dependencies between SPL application components and reference architecture implementation (a platform), this paper empirically investigates the relationship between 7 design metrics and changeability of 46 server components of a product line for business applications. In addition, we investigated the us...

  3. Three-dimensional verification of prostate cancer patients treated with VMAT by Matrixx detector and COMPASS software IBA; Verificacion tridimensional de pacientes con cancer de prostata tratados con VMAT mediante el detector Matrixx y software COMPASS de IBA

    Energy Technology Data Exchange (ETDEWEB)

    Mateos, J. C.; Luis, F. J.; Cabrera, P.; Carrasco, M.; Sanchez, G.; Herrador, M.

    2011-07-01

    Described in this paper the verification of prostate cancer patients treated with VMAT planned in our hospital, with a prescribed dose of 76 Gy. The ability to simultaneously analyze the patient by any plane COMPASS software (IBA, Germany), together with the detector array Matrixx-Evolution, this system gives a particularly interesting feature. The aim of this paper is to describe the operation of this equipment and validated for patient dosimetry in IMRT and VMAT treatments.

  4. Models for predicting objective function weights in prostate cancer IMRT

    International Nuclear Information System (INIS)

    Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR

  5. Models for predicting objective function weights in prostate cancer IMRT

    Energy Technology Data Exchange (ETDEWEB)

    Boutilier, Justin J., E-mail: j.boutilier@mail.utoronto.ca; Lee, Taewoo [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8 (Canada); Craig, Tim [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9, Canada and Department of Radiation Oncology, University of Toronto, 148 - 150 College Street, Toronto, Ontario M5S 3S2 (Canada); Sharpe, Michael B. [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9 (Canada); Department of Radiation Oncology, University of Toronto, 148 - 150 College Street, Toronto, Ontario M5S 3S2 (Canada); Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5 (Canada); Chan, Timothy C. Y. [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8, Canada and Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5 (Canada)

    2015-04-15

    Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR

  6. PREDICTIVE ANALYSIS SOFTWARE FOR MODELING THE ALTMAN Z-SCORE FINANCIAL DISTRESS STATUS OF COMPANIES

    Directory of Open Access Journals (Sweden)

    ILIE RĂSCOLEAN

    2012-10-01

    Full Text Available Literature shows some bankruptcy methods for determining the financial distress status of companies and based on this information we chosen Altman statistical model because it has been used a lot in the past and like that it has become a benchmark for other methods. Based on this financial analysis flowchart, programming software was developed that allows the calculation and determination of the bankruptcy probability for a certain rate of failure Z-score, corresponding to a given interval that is equal to the ratio of the number of bankrupt companies and the total number of companies (bankrupt and healthy interval.

  7. Delivered dose to scrotum in rectal cancer radiotherapy by thermoluminescence dosimetry comparing to dose calculated by planning software

    Directory of Open Access Journals (Sweden)

    Peiman Haddad

    2014-02-01

    Conclusion: In this study, the mean testis dose of radiation was 3.77 Gy, similar to the dose calculated by the planning software (4.11 Gy. This dose could be significantly harmful for spermatogenesis, though low doses of scattered radiation to the testis in fractionated radiotherapy might be followed with better recovery. Based on above findings, careful attention to testicular dose in radiotherapy of rectal cancer for the males desiring continued fertility seems to be required.

  8. Nomogram prediction for overall survival of patients diagnosed with cervical cancer

    OpenAIRE

    Polterauer, S; Grimm, C.; Hofstetter, G; Concin, N; Natter, C; Sturdza, A.; R. Pötter; Marth, C.; Reinthaller, A.; Heinze, G.

    2012-01-01

    Background: Nomograms are predictive tools that are widely used for estimating cancer prognosis. The aim of this study was to develop a nomogram for the prediction of overall survival (OS) in patients diagnosed with cervical cancer. Methods: Cervical cancer databases of two large institutions were analysed. Overall survival was defined as the clinical endpoint and OS probabilities were estimated using the Kaplan–Meier method. Based on the results of survival analyses and previous studies, rel...

  9. A nomogram to predict Gleason sum upgrading of clinically diagnosed localized prostate cancer among Chinese patients

    OpenAIRE

    Jin-You Wang; Yao Zhu; Chao-Fu Wang; Shi-Lin Zhang; Bo Dai; Ding-Wei Ye

    2014-01-01

    Although several models have been developed to predict the probability of Gleason sum upgrading between biopsy and radical prostatectomy specimens, most of these models are restricted to prostate-specific antigen screening-detected prostate cancer. This study aimed to build a nomogram for the prediction of Gleason sum upgrading in clinically diagnosed prostate cancer. The study cohort comprised 269 Chinese prostate cancer patients who underwent prostate biopsy with a minimum of 10 cores and w...

  10. An epidemiologic risk prediction model for ovarian cancer in Europe : The EPIC study

    OpenAIRE

    Li, K; Huesing, A.; Fortner, R. T.; Tjonneland, A.; Hansen, L.; Dossus, L; Chang-Claude, J; Bergmann, M.; A. Steffen; Bamia, C.; Trichopoulos, D; Trichopoulou, A; Palli, D; Mattiello, A; Agnoli, C

    2015-01-01

    Background: Ovarian cancer has a high case-fatality ratio, largely due to late diagnosis. Epidemiologic risk prediction models could help identify women at increased risk who may benefit from targeted prevention measures, such as screening or chemopreventive agents. Methods: We built an ovarian cancer risk prediction model with epidemiologic risk factors from 202 206 women in the European Prospective Investigation into Cancer and Nutrition study. Results: Older age at menopause, longer durati...

  11. LiverTox: Advanced QSAR and Toxicogeomic Software for Hepatotoxicity Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Lu, P-Y.; Yuracko, K. (YAHSGS, LLC)

    2011-02-25

    YAHSGS LLC and Oak Ridge National Laboratory (ORNL) established a CRADA in an attempt to develop a predictive system using a pre-existing ORNL computational neural network and wavelets format. This was in the interest of addressing national needs for toxicity prediction system to help overcome the significant drain of resources (money and time) being directed toward developing chemical agents for commerce. The research project has been supported through an STTR mechanism and funded by the National Institute of Environmental Health Sciences beginning Phase I in 2004 (CRADA No. ORNL-04-0688) and extending Phase II through 2007 (ORNL NFE-06-00020). To attempt the research objectives and aims outlined under this CRADA, state-of-the-art computational neural network and wavelet methods were used in an effort to design a predictive toxicity system that used two independent areas on which to base the system’s predictions. These two areas were quantitative structure-activity relationships and gene-expression data obtained from microarrays. A third area, using the new Massively Parallel Signature Sequencing (MPSS) technology to assess gene expression, also was attempted but had to be dropped because the company holding the rights to this promising MPSS technology went out of business. A research-scale predictive toxicity database system called Multi-Intelligent System for Toxicogenomic Applications (MISTA) was developed and its feasibility for use as a predictor of toxicological activity was tested. The fundamental focus of the CRADA was an attempt and effort to operate the MISTA database using the ORNL neural network. This effort indicated the potential that such a fully developed system might be used to assist in predicting such biological endpoints as hepatotoxcity and neurotoxicity. The MISTA/LiverTox approach if eventually fully developed might also be useful for automatic processing of microarray data to predict modes of action. A technical paper describing the

  12. External validation of nomograms for predicting cancer-specific mortality in penile cancer patients treated with definitive surgery

    OpenAIRE

    Yao Zhu; Wei-Jie Gu; Ding-Wei Ye; Xu-Dong Yao; Shi-Lin Zhang; Bo Dai; Hai-Liang Zhang; Yi-Jun Shen

    2014-01-01

    Using a population-based cancer registry, Thuret et al. developed 3 nomograms for estimating cancer-specific mortality in men with penile squamous cell carcinoma. In the initial cohort, only 23.0% of the patients were treated with inguinal lymphadenectomy and had pN stage. To generalize the prediction models in clinical practice, we evaluated the performance of the 3 nomograms in a series of penile cancer patients who were treated with definitive surgery. Clinicopathologic information was obt...

  13. Postoperative Prognosis of Breast Cancer Patients Predicted by p53 Gene Mutation in Cancer Cells Obtained by Aspiration Biopsy

    OpenAIRE

    Takashi, SATO; Hideji, Masuoka; Kazunori, Toda; Kosho, Watabe; Yukio, Nakamura; Tatsuya, Ito; Makoto, Meguro; Masaaki, Yamamoto; Tousei, Ohmura

    2007-01-01

    The method of cytological examination by fine needle aspiration biopsy (FNAB) was developed clinically in breast cancer and enabled us to prepare cancer cell nuclei for the detection of p53 gene mutation. In the expectation that this method would improve the prediction of postoperative prognosis, the observation of 10 year survival for breast cancer patients with p53 gene mutations was done. The DNA of the aspirated cells was examined preoperatively for gene alterations in 53 patients with br...

  14. Genomic Copy Number Variations in the Genomes of Leukocytes Predict Prostate Cancer Clinical Outcomes.

    Directory of Open Access Journals (Sweden)

    Yan P Yu

    Full Text Available Accurate prediction of prostate cancer clinical courses remains elusive. In this study, we performed whole genome copy number analysis on leukocytes of 273 prostate cancer patients using Affymetrix SNP6.0 chip. Copy number variations (CNV were found across all chromosomes of the human genome. An average of 152 CNV fragments per genome was identified in the leukocytes from prostate cancer patients. The size distributions of CNV in the genome of leukocytes were highly correlative with prostate cancer aggressiveness. A prostate cancer outcome prediction model was developed based on large size ratio of CNV from the leukocyte genomes. This prediction model generated an average prediction rate of 75.2%, with sensitivity of 77.3% and specificity of 69.0% for prostate cancer recurrence. When combined with Nomogram and the status of fusion transcripts, the average prediction rate was improved to 82.5% with sensitivity of 84.8% and specificity of 78.2%. In addition, the leukocyte prediction model was 62.6% accurate in predicting short prostate specific antigen doubling time. When combined with Gleason's grade, Nomogram and the status of fusion transcripts, the prediction model generated a correct prediction rate of 77.5% with 73.7% sensitivity and 80.1% specificity. To our knowledge, this is the first study showing that CNVs in leukocyte genomes are predictive of clinical outcomes of a human malignancy.

  15. Chromosomal aberration frequency in lymphocytes predicts the risk of cancer

    DEFF Research Database (Denmark)

    Bonassi, Stefano; Norppa, Hannu; Ceppi, Marcello;

    2008-01-01

    incidence and/or mortality for an average of 10.1 years; 368 cancer deaths and 675 incident cancer cases were observed. Subjects were classified within each laboratory according to tertiles of CA frequency. The relative risk (RR) of cancer was increased for subjects in the medium [RR = 1.31, 95% confidence...... for stomach cancer [RR(medium) = 1.17 (95% CI = 0.37-3.70), RR(high) = 3.13 (95% CI = 1.17-8.39)]. Exposure to carcinogens did not modify the effect of CA levels on overall cancer risk. These results reinforce the evidence of a link between CA frequency and cancer risk and provide novel information......Mechanistic evidence linking chromosomal aberration (CA) to early stages of cancer has been recently supported by the results of epidemiological studies that associated CA frequency in peripheral lymphocytes of healthy individuals to future cancer incidence. To overcome the limitations of single...

  16. Methods to Predict and Lower the Risk of Prostate Cancer

    OpenAIRE

    Barbara Ercole; Dipen J Parekh

    2011-01-01

    Chemoprevention for prostate cancer (PCa) continues to generate interest from both physicians and the patient population. The goal of chemoprevention is to stop the malignant transformation of prostate cells into cancer. Multiple studies on different substances ranging from supplements to medical therapy have been undertaken. Thus far, only the studies on 5α-reductase inhibitors (the Prostate Cancer Prevention Trial [PCPT] and Reduction by Dutasteride of Prostate Cancer Events [REDUCE] trial)...

  17. DNA Repair Gene Patterns as Prognostic and Predictive Factors in Molecular Breast Cancer Subtypes

    OpenAIRE

    Santarpia, Libero; Iwamoto, Takayuki; Di Leo, Angelo; Hayashi, Naoki; Bottai, Giulia; Stampfer, Martha; André, Fabrice; Turner, Nicholas C.; Symmans, W Fraser; Hortobágyi, Gabriel N.; Pusztai, Lajos; Bianchini, Giampaolo

    2013-01-01

    DNA repair pathways can enable tumor cells to survive DNA damage induced by chemotherapy and thus provide prognostic and/or predictive value. In this study, the authors sought to assess the differential expression, bimodal distribution, and prognostic and predictive role of DNA repair genes in individual breast cancer molecular subtypes including estrogen receptor-positive/ HER2-negative, estrogen receptor-negative/HER2-negative, and HER2-positive cancers. The predictive value of DNA repair g...

  18. Predicting Defects Using Information Intelligence Process Models in the Software Technology Project

    OpenAIRE

    Manjula Gandhi Selvaraj; Devi Shree Jayabal; Thenmozhi Srinivasan; Palanisamy Balasubramanie

    2015-01-01

    A key differentiator in a competitive market place is customer satisfaction. As per Gartner 2012 report, only 75%–80% of IT projects are successful. Customer satisfaction should be considered as a part of business strategy. The associated project parameters should be proactively managed and the project outcome needs to be predicted by a technical manager. There is lot of focus on the end state and on minimizing defect leakage as much as possible. Focus should be on proactively managing and sh...

  19. Cariogram – A Multi-factorial Risk Assessment Software for Risk Prediction of Dental Caries

    OpenAIRE

    Anup N; Preeti Vishnani

    2014-01-01

    For years, Swedish researchers have recognized caries risk assessment as an important part of routine dental practice for caries management. This paper reviews a new way of illustrating the caries risk profi le of an individual through a computer program, the Cariogram, which was described by Professor Bratthal in 1976. Cariogram is a risk as well as a prediction model. It presents a ‘weighted’ of the input data related to caries such as caries experience, related disease, diet, fl u...

  20. The use of available chemical equilibria software for the prediction of the performance of EKR

    OpenAIRE

    Villén Guzmán, María; Gómez-Lahoz, C.; García Rubio, Ana; Paz García, José Manuel; Vereda Alonso, Carlos; García Herruzo, Francisco; Rodríguez Maroto, José Miguel

    2014-01-01

    Risk assessment aims for the prediction of the mobility of contaminants, and these are usually based in lab essays together with mathematical modelling. Also the feasibility studies of most techniques, require similar tools. Frequently the lab characterization is based in the chemical fractionation of the contaminants based on their mobility under different chemical reagents. Probably the most frequent fractionation technique for heavy metal contaminated soils is the BCR [1]. The use of ch...

  1. The role of brain/behavioural systems in prediction of quality of life and coping strategies in cancer patients

    Directory of Open Access Journals (Sweden)

    Shala Jangi Goujeh Biglou

    2014-03-01

    Full Text Available Background: It seems that individual differences in personality characteristics are implicated in the incidence and progress of physical diseases and socio-psychological consequences. However, there are a few studies about the role of personality in the prediction of socio-psychological consequences of cancer. The aim of this research was to survey the role of personality in the prediction of socio-psychosocial factors: quality of life and coping strategies. Methods: This research was a descriptive-correlational study in which the sample included fifty cancer patients who were selected through convenience sampling method. To assess the personality differences, quality of life and coping strategies, the Carver and White (1994 BIS/BAS Scales, SF-12 Health Survey and Coping Inventory for Stressful Situation (CISS were used, respectively. The data were analysed by SPSS software using Pearson correlation coefficient and stepwise regression. Results: The findings showed that Both BIS and BAS systems could predict the quality of life (P<0.001, BIS system could explain the emotion-oriented coping strategy (P<0.05 and avoidance-oriented coping stratesy (P<0.01 and BAS system could explain the problem-oriented coping strategy (P<0.001. Conclusion: The findings of this study showed that brain/behavioural systems can predict the quality of life and coping strategies in cancer patients. The identification of these systems in cancer patients can help recognize the persons that are under the risk of poor quality of life or have a higher chance of using inconsistent coping strategies, and execute preventive measures about them.

  2. Methods to Predict and Lower the Risk of Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Barbara Ercole

    2011-01-01

    Full Text Available Chemoprevention for prostate cancer (PCa continues to generate interest from both physicians and the patient population. The goal of chemoprevention is to stop the malignant transformation of prostate cells into cancer. Multiple studies on different substances ranging from supplements to medical therapy have been undertaken. Thus far, only the studies on 5α-reductase inhibitors (the Prostate Cancer Prevention Trial [PCPT] and Reduction by Dutasteride of Prostate Cancer Events [REDUCE] trial have demonstrated a reduction in the risk of PCa, while results from the Selenium and Vitamin E Cancer Prevention Trial (SELECT concluded no decreased risk for PCa with selenium or vitamin E.

  3. Use of molecular markers for predicting therapy response in cancer patients.

    LENUS (Irish Health Repository)

    Duffy, Michael J

    2012-02-01

    Predictive markers are factors that are associated with upfront response or resistance to a particular therapy. Predictive markers are important in oncology as tumors of the same tissue of origin vary widely in their response to most available systemic therapies. Currently recommended oncological predictive markers include both estrogen and progesterone receptors for identifying patients with breast cancers likely to benefit from hormone therapy, HER-2 for the identification of breast cancer patients likely to benefit from trastuzumab, specific K-RAS mutations for the identification of patients with advanced colorectal cancer unlikely to benefit from either cetuximab or panitumumab and specific EGFR mutations for selecting patients with advanced non-small-cell lung cancer for treatment with tyrosine kinase inhibitors such as gefitinib and erlotinib. The availability of predictive markers should increase drug efficacy and decrease toxicity, thus leading to a more personalized approach to cancer treatment.

  4. Developing highly predictable system behavior in real-time battle-management software

    OpenAIRE

    Michael, James Bret

    2003-01-01

    Given that battle-management solutions in system-of-systems environment are separately designed and developed on various operating platforms in different languages, predicting battle-management behavior of system-of-systems is not possible. As a rule, battle-management is executed at the system level rather than the desired system-of-systems level. Typically, C4 systems are non-real-time systems. Traditionally, weapon systems are real-time systems. If we are to match the performance of weapon...

  5. DATA MINING FOR PREDICTION OF HUMAN PERFORMANCE CAPABILITY IN THE SOFTWARE-INDUSTRY

    Directory of Open Access Journals (Sweden)

    Gaurav Singh Thakur1

    2015-03-01

    Full Text Available The recruitment of new personnel is one of the most essential business processes which affect the quality of human capital within any company. It is highly essential for the companies to ensure the recruitment of right talent to maintain a competitive edge over the others in the market. However IT companies often face a problem while recruiting new people for their ongoing projects due to lack of a proper framework that defines a criteria for the selection process. In this paper we aim to develop a framework that would allow any project manager to take the right decision for selecting new talent by correlating performance parameters with the other domain-specific attributes of the candidates. Also, another important motivation behind this project is to check the validity of the selection procedure often followed by various big companies in both public and private sectors which focus only on academic scores, GPA/grades of students from colleges and other academic backgrounds. We test if such a decision will produce optimal results in the industry or is there a need for change that offers a more holistic approach to recruitment of new talent in the software companies. The scope of this work extends beyond the IT domain and a similar procedure can be adopted to develop a recruitment framework in other fields as well. Data-mining techniques provide useful information from the historical projects depending on which the hiring-manager can make decisions for recruiting high-quality workforce. This study aims to bridge this hiatus by developing a data-mining framework based on an ensemble-learning technique to refocus on the criteria for personnel selection. The results from this research clearly demonstrated that there is a need to refocus on the selection-criteria for quality objectives.

  6. The study of unfoldable self-avoiding walks - Application to protein structure prediction software.

    Science.gov (United States)

    Guyeux, Christophe; Nicod, Jean-Marc; Philippe, Laurent; Bahi, Jacques M

    2015-08-01

    Self-avoiding walks (SAWs) are the source of very difficult problems in probability and enumerative combinatorics. They are of great interest as, for example, they are the basis of protein structure prediction (PSP) in bioinformatics. The authors of this paper have previously shown that, depending on the prediction algorithm, the sets of obtained walk conformations differ: For example, all the SAWs can be generated using stretching-based algorithms whereas only the unfoldable SAWs can be obtained with methods that iteratively fold the straight line. A deeper study of (non-)unfoldable SAWs is presented in this paper. The contribution is first a survey of what is currently known about these sets. In particular, we provide clear definitions of various subsets of SAWs related to pivot moves (unfoldable and non-unfoldable SAWs, etc.) and the first results that we have obtained, theoretically or computationally, on these sets. Then a new theorem on the number of non-unfoldable SAWs is demonstrated. Finally, a list of open questions is provided and the consequences on the PSP problem is proposed.

  7. A Review of Current Machine Learning Methods Used for Cancer Recurrence Modeling and Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Hemphill, Geralyn M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-09-27

    Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type has become a necessity in cancer research. A major challenge in cancer management is the classification of patients into appropriate risk groups for better treatment and follow-up. Such risk assessment is critically important in order to optimize the patient’s health and the use of medical resources, as well as to avoid cancer recurrence. This paper focuses on the application of machine learning methods for predicting the likelihood of a recurrence of cancer. It is not meant to be an extensive review of the literature on the subject of machine learning techniques for cancer recurrence modeling. Other recent papers have performed such a review, and I will rely heavily on the results and outcomes from these papers. The electronic databases that were used for this review include PubMed, Google, and Google Scholar. Query terms used include “cancer recurrence modeling”, “cancer recurrence and machine learning”, “cancer recurrence modeling and machine learning”, and “machine learning for cancer recurrence and prediction”. The most recent and most applicable papers to the topic of this review have been included in the references. It also includes a list of modeling and classification methods to predict cancer recurrence.

  8. Methods and software for predicting germanium detector absolute full-energy peak efficiencies

    International Nuclear Information System (INIS)

    High-purity germanium (HPGe) and lithium drifted germanium (Ge(Li)) detectors have been the detector of choice for high resolution gamma-ray spectroscopy for many years. This is primarily due to the superior energy resolution that germanium detectors present over other gamma-ray detectors. In order to perform quantitative analyses with germanium detectors, such as activity determination or nuclide identification, one must know the absolute full-energy peak efficiency at the desired gamma-ray energy. Many different methods and computer codes have been developed throughout history in an effort to predict these efficiencies using minimal or no experimental observations. A review of these methods and the computer codes that utilize them is presented. (author)

  9. Prediction of near-term breast cancer risk using a Bayesian belief network

    Science.gov (United States)

    Zheng, Bin; Ramalingam, Pandiyarajan; Hariharan, Harishwaran; Leader, Joseph K.; Gur, David

    2013-03-01

    Accurately predicting near-term breast cancer risk is an important prerequisite for establishing an optimal personalized breast cancer screening paradigm. In previous studies, we investigated and tested the feasibility of developing a unique near-term breast cancer risk prediction model based on a new risk factor associated with bilateral mammographic density asymmetry between the left and right breasts of a woman using a single feature. In this study we developed a multi-feature based Bayesian belief network (BBN) that combines bilateral mammographic density asymmetry with three other popular risk factors, namely (1) age, (2) family history, and (3) average breast density, to further increase the discriminatory power of our cancer risk model. A dataset involving "prior" negative mammography examinations of 348 women was used in the study. Among these women, 174 had breast cancer detected and verified in the next sequential screening examinations, and 174 remained negative (cancer-free). A BBN was applied to predict the risk of each woman having cancer detected six to 18 months later following the negative screening mammography. The prediction results were compared with those using single features. The prediction accuracy was significantly increased when using the BBN. The area under the ROC curve increased from an AUC=0.70 to 0.84 (pbreast cancer risk than with a single feature.

  10. Optimism and prostate cancer-specific expectations predict better quality of life after robotic prostatectomy.

    Science.gov (United States)

    Thornton, Andrea A; Perez, Martin A; Oh, Sindy; Crocitto, Laura

    2012-06-01

    We examined the relations among generalized positive expectations (optimism), prostate-cancer specific expectations, and prostate cancer-related quality of life in a prospective sample of 83 men who underwent robotic assisted laparoscopic prostatectomy (RALP) for prostate cancer. Optimism was significantly associated with higher prostate cancer-specific expectations, β = .36, p predictors of better scores on the following prostate cancer-related quality of life scales: Sexual Intimacy and Sexual Confidence; Masculine Self-Esteem (specific expectations only), Health Worry, Cancer Control, and Informed Decision Making (βs > .21, ps predictor of better Sexual Intimacy and Sexual Confidence scores, and specific expectations uniquely predicted higher scores on Informed Decision Making. Although optimism and prostate-cancer specific expectations are related, they contribute uniquely to several prostate cancer-related quality of life outcomes following RALP and may be important targets for quality of life research with this population. PMID:22051931

  11. Comparison of two software versions of a commercially available computer-aided detection (CAD) system for detecting breast cancer

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Seung Ja; Moon, Woo Kyung; Kim, Soo-Yeon; Chang, Jung Min; Cho, Nariya (Dept. of Radiology, Seoul National Univ. Hospital, Seoul (Korea)), e-mail: moonwk@radcom.snu.ac.kr; Kim, Sun Mi (Dept. of Radiology and Bundang Seoul National Univ. Hospital, Seoul (Korea))

    2010-06-15

    Background: The performance of the computer-aided detection (CAD) system can be determined by the sensitivity and false-positive marks rate, therefore these factors should be improved by upgrading the software version of the CAD system. Purpose: To compare retrospectively the performances of two software versions of a commercially available CAD system when applied to full-field digital mammograms for the detection of breast cancers in a screening group. Material and Methods: Versions 3.1 and 8.3 of a CAD software system (ImageChecker, R2 Technology) were applied to the full-field digital mammograms of 130 women (age range 36-80, mean age 53 years) with 130 breast cancers detected by screening. Results: The overall sensitivities of the version 3.1 and 8.3 CAD systems were 92.3% (120 of 130) and 96.2% (125 of 130) (P=0.025), respectively, and sensitivities for masses were 78.3% (36 of 46) and 89.1% (41 of 46) (P=0.024) and for microcalcifications 100% (84 of 84) and 100% (84 of 84), respectively. Version 8.3 correctly marked five lesions of invasive ductal carcinoma that were missed by version 3.1. Average numbers of false-positive marks per image were 0.38 (0.15 for calcifications, 0.23 for masses) for version 3.1 and 0.46 (0.13 for calcifications, 0.33 for masses) for version 8.3 (P=0.1420). Conclusion: The newer version 8.3 of the CAD system showed better overall sensitivity for the detection of breast cancer than version 3.1 due to its improved sensitivity for masses when applied to full-field digital mammograms

  12. Predicting Municipal Solid Waste Generation through Time Series Method (ARMA Technique and System Dynamics Modeling (Vensim Software

    Directory of Open Access Journals (Sweden)

    A Ebrahimi

    2016-06-01

    Full Text Available Background and Objective: Predicting municipal solid waste generation has an important role in solid waste management. The aim of this study was to predict municipal solid waste generation in Isfahan through time series method and system dynamics modeling. Materials and Methods: Verified data of solid waste generation was collected from Waste Management Organization and population information was collected from the National Statistics Center, Iran for the period 1996-2011. Next, the effect of   factors on solid waste generation such as population, urbanization, gross domestic product was investigated. Moreover, the relationship between each of these factors was identified using generalized estimating equation  model. Finally, the quantity of the solid waste generated in Isfahan city was predicted using system dynamics modeling by Vensim software and time series method by ARMA technique. Results: It was found that population and gross domestic product have a significant relationship with the amount of solid waste with P value 0.026 and 0 respectively. The annual average of municipal solid waste generation would be 1501.4 ton/day in 2021 estimated by the time series method and 1436 ton/day estimated by the system dynamics modeling. In addition, average annual growth rate achieved was 3.44%. Conclusion: According to the results obtained, population and gross domestic product have a significant effect on MSW generation. Municipal solid waste generation will increase in future. Increasing solid waste is not the same in different areas and methods. The prediction of the time series method by ARMA technique gives precise results compared with other methods.

  13. Risk Prediction Models for Other Cancers or Multiple Sites

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing other multiple cancers over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Procalcitonin Levels Predict Clinical Course and Progression-Free Survival in Patients With Medullary Thyroid Cancer

    NARCIS (Netherlands)

    Walter, Martin A.; Meier, Christian; Radimerski, Tanja; Iten, Fabienne; Kraenzlin, Marius; Mueller-Brand, Jan; de Groot, Jan Willem B.; Kema, Ido P.; Links, Thera P.; Mueller, Beat

    2010-01-01

    BACKGROUND: Procalcitonin has been well established as an important marker of sepsis and systemic infection. The authors evaluated the diagnostic and predictive value of calcitonin and its prohormone procalcitonin in medullary thyroid cancer. METHODS: The authors systematically explored the ability

  15. Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model

    OpenAIRE

    Georgina Cosma; Giovanni Acampora; David Brown; Rees, Robert C.; Masood Khan; Graham Pockley, A.

    2016-01-01

    The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the...

  16. Proteomic biomarkers predicting lymph node involvement in serum of cervical cancer patients. Limitations of SELDI-TOF MS

    Directory of Open Access Journals (Sweden)

    Van Gorp Toon

    2012-06-01

    Full Text Available Abstract Background Lymph node status is not part of the staging system for cervical cancer, but provides important information for prognosis and treatment. We investigated whether lymph node status can be predicted with proteomic profiling. Material & methods Serum samples of 60 cervical cancer patients (FIGO I/II were obtained before primary treatment. Samples were run through a HPLC depletion column, eliminating the 14 most abundant proteins ubiquitously present in serum. Unbound fractions were concentrated with spin filters. Fractions were spotted onto CM10 and IMAC30 surfaces and analyzed with surface-enhanced laser desorption time of flight (SELDI-TOF mass spectrometry (MS. Unsupervised peak detection and peak clustering was performed using MASDA software. Leave-one-out (LOO validation for weighted Least Squares Support Vector Machines (LSSVM was used for prediction of lymph node involvement. Other outcomes were histological type, lymphvascular space involvement (LVSI and recurrent disease. Results LSSVM models were able to determine LN status with a LOO area under the receiver operating characteristics curve (AUC of 0.95, based on peaks with m/z values 2,698.9, 3,953.2, and 15,254.8. Furthermore, we were able to predict LVSI (AUC 0.81, to predict recurrence (AUC 0.92, and to differentiate between squamous carcinomas and adenocarcinomas (AUC 0.88, between squamous and adenosquamous carcinomas (AUC 0.85, and between adenocarcinomas and adenosquamous carcinomas (AUC 0.94. Conclusions Potential markers related with lymph node involvement were detected, and protein/peptide profiling support differentiation between various subtypes of cervical cancer. However, identification of the potential biomarkers was hampered by the technical limitations of SELDI-TOF MS.

  17. CD44 Expression Predicts Local Recurrence after Radiotherapy in Larynx Cancer

    NARCIS (Netherlands)

    de Jong, Monique C.; Pramana, Jimmy; van der Wal, Jacqueline E.; Lacko, Martin; Peutz-Kootstra, Carine J.; Takes, Robert P.; Kaanders, Johannes H.; van der Laan, Bernard F.; Wachters, Jasper; Jansen, Jeroen C.; Rasch, Coen R.; van Velthuysen, Marie-Louise F.; Grenman, Reidar; Hoebers, Frank J.; Schuuring, Ed; van den Brekel, Michiel W.; Begg, Adrian C.; de Jong, Johan

    2010-01-01

    Purpose: To find molecular markers from expression profiling data to predict recurrence of laryngeal cancer after radiotherapy. Experimental Design: We generated gene expression data on pre-treatment biopsies from 52 larynx cancer patients. Patients developing a local recurrence were matched for T-s

  18. Persistence of disseminated tumor cells after neoadjuvant treatment for locally advanced breast cancer predicts poor survival

    OpenAIRE

    Mathiesen, Randi R.; Borgen, Elin; Renolen, Anne; Løkkevik, Erik; Nesland, Jahn M; Anker, Gun; Østenstad, Bjørn; Lundgren, Steinar; Risberg, Terje; Mjaaland, Ingvil; Kvalheim, Gunnar; Lønning, Per E.; Naume, Bjørn

    2012-01-01

    Introduction Presence of disseminated tumor cells (DTCs) in bone marrow (BM) and circulating tumor cells (CTC) in peripheral blood (PB) predicts reduced survival in early breast cancer. The aim of this study was to determine the presence of and alterations in DTC- and CTC-status in locally advanced breast cancer patients undergoing neoadjuvant chemotherapy (NACT) and to evaluate their prognostic impact. Methods ...

  19. Predicting brain metastases of breast cancer based on serum S100B and serum HER2

    DEFF Research Database (Denmark)

    Bechmann, Troels; Madsen, Jonna Skov; Brandslund, Ivan;

    2013-01-01

    Brain metastases are a major cause of morbidity and mortality in breast cancer. The aim of the current study was to evaluate the prediction of brain metastases based on serum S100B and human epidermal growth factor receptor 2 (HER2). A total of 107 breast cancer patients were included...

  20. Predicted trends in long-term breast cancer survival in England and Wales

    OpenAIRE

    Woods, L. M.; Rachet, B; Cooper, N.; Coleman, M P

    2007-01-01

    Trends in long-term relative survival from breast cancer are examined for women diagnosed in England and Wales up to 2001, using both period and hybrid approaches. Large improvements in long-term survival are predicted. Women with breast cancer still experience persistent excess mortality up to at least 20 years after diagnosis.

  1. An epidemiological model for prediction of endometrial cancer risk in Europe

    NARCIS (Netherlands)

    Hüsing, Anika; Dossus, Laure; Ferrari, Pietro; Tjønneland, Anne; Hansen, Louise; Fagherazzi, Guy; Baglietto, Laura; Schock, Helena; Chang-Claude, Jenny; Boeing, Heiner; Steffen, Annika; Trichopoulou, Antonia; Bamia, Christina; Katsoulis, Michalis; Krogh, Vittorio; Palli, Domenico; Panico, Salvatore; Onland-Moret, N. Charlotte; Peeters, Petra H.; Bueno-de-Mesquita, H. Bas; Weiderpass, Elisabete; Gram, Inger T.; Ardanaz, Eva; Obón-Santacana, Mireia; Navarro, Carmen; Sánchez-Cantalejo, Emilio; Etxezarreta, Nerea; Allen, Naomi E.; Khaw, Kay Tee; Wareham, Nick; Rinaldi, Sabina; Romieu, Isabelle; Merritt, Melissa A.; Gunter, Marc; Riboli, Elio; Kaaks, Rudolf

    2016-01-01

    Endometrial cancer (EC) is the fourth most frequent cancer in women in Europe, and as its incidence is increasing, prevention strategies gain further pertinence. Risk prediction models can be a useful tool for identifying women likely to benefit from targeted prevention measures. On the basis of dat

  2. Nomograms for the Prediction of Pathologic Stage of Clinically Localized Prostate Cancer in Korean Men

    OpenAIRE

    Song, Cheryn; Kang, Taejin; Ro, Jae Y.; Lee, Moo-Song; Kim, Choung-Soo; Ahn, Hanjong

    2005-01-01

    We analyzed the prostate cancer data of 317 Korean men with clinically localized prostate cancer who underwent radical prostatectomy at Asan Medical Center between June 1990 and November 2003 to construct nomograms predicting the pathologic stage of these tumors, and compared the outcome with preexisting nomograms. Multinomial log-linear regression was performed for the simultaneous prediction of organ-confined disease (OCD), extracapsular extension (ECE), seminal vesicle invasion (SVI) and l...

  3. Prognostic Nomograms for Predicting Survival and Distant Metastases in Locally Advanced Rectal Cancers

    OpenAIRE

    Junjie Peng; Ying Ding; Shanshan Tu; Debing Shi; Liang Sun; Xinxiang Li; Hongbin Wu; Sanjun Cai

    2014-01-01

    Aim To develop prognostic nomograms for predicting outcomes in patients with locally advanced rectal cancers who do not receive preoperative treatment. Materials and Methods A total of 883 patients with stage II–III rectal cancers were retrospectively collected from a single institution. Survival analyses were performed to assess each variable for overall survival (OS), local recurrence (LR) and distant metastases (DM). Cox models were performed to develop a predictive model for each endpoint...

  4. A Nomogram to Predict Prognostic Value of Red Cell Distribution Width in Patients with Esophageal Cancer

    OpenAIRE

    Gui-Ping Chen; Ying Huang; Xun Yang; Ji-Feng Feng

    2015-01-01

    Objectives. The prognostic value of inflammatory index in esophageal cancer (EC) was not established. In the present study, we initially used a nomogram to predict prognostic value of red cell distribution width (RDW) in patients with esophageal squamous cell carcinoma (ESCC). Methods. A total of 277 ESCC patients were included in this retrospective study. Kaplan-Meier method was used to calculate the cancer-specific survival (CSS). A nomogram was established to predict the prognosis for CSS....

  5. Risk prediction models for colorectal cancer in people with symptoms: a systematic review

    OpenAIRE

    Williams, Tom G. S.; Cubiella, Joaquín; Griffin, Simon J; Walter, Fiona M.; Usher-Smith, Juliet A.

    2016-01-01

    Background Colorectal cancer (CRC) is the fourth leading cause of cancer-related death in Europe and the United States. Detecting the disease at an early stage improves outcomes. Risk prediction models which combine multiple risk factors and symptoms have the potential to improve timely diagnosis. The aim of this review is to systematically identify and compare the performance of models that predict the risk of primary CRC among symptomatic individuals. Methods We searched Medline and EMBASE ...

  6. SOFTWARE FAILURE PREDICTION BASED ON LARGEST LYAPUNOV EXPONENT%基于最大Lyapunov指数的软件失效预测

    Institute of Scientific and Technical Information of China (English)

    葛君伟; 任伟; 方义秋

    2013-01-01

    现有的大部分软件可靠性模型都将软件失效过程看作是随机过程,但已证明软件失效过程具有混沌特性,不是单纯的随机行为.混沌预测方法通常只能做短中期的预测,只有在其有效预测时间段内,它的预测才是可信的;但现有的基于混沌的软件可靠性模型均没有指明其有效预测时间段的长度,只能做单步预测.为解决以上问题,建立了基于最大Lyapunov指数的软件失效预测模型,该模型明确指出了有效预测时长,可以做多步预测.将其应用于从模拟法庭教学软件系统采集到的实测软件失效数据,取得了较好的预测效果.同时,预测结果还表明:在有效预测时间段内,预测精度较高;反之,预测误差很大.%Most of the existing software reliability models regard software failure process as the stochastic process. But it has been proved that the software failure process has chaotic characteristic rather than a simple random behaviour. Usually the chaotic prediction method can only predict in short and medium period, and only within its effective prediction period the results are credible. However, all the existing software reliability models based on chaos don' t indicate the length of their effective prediction period, so they can only predict single step. To solve the above problem, a software failure prediction model based on largest Lyapunov exponent is established, the model clearly suggests the effective prediction period length, therefore is able to make multi-step prediction. The model has been applied to real software failure data collected from the Moot Court Teaching software system, and obtains a preferable good prediction result. Moreover, the result also shows that the prediction accuracy is higher in effective prediction period, and in contrary, the error will be very large.

  7. Bioinformatics resources for cancer research with an emphasis on gene function and structure prediction tools

    Directory of Open Access Journals (Sweden)

    Daisuke Kihara

    2006-01-01

    Full Text Available The immensely popular fields of cancer research and bioinformatics overlap in many different areas, e.g. large data repositories that allow for users to analyze data from many experiments (data handling, databases, pattern mining, microarray data analysis, and interpretation of proteomics data. There are many newly available resources in these areas that may be unfamiliar to most cancer researchers wanting to incorporate bioinformatics tools and analyses into their work, and also to bioinformaticians looking for real data to develop and test algorithms. This review reveals the interdependence of cancer research and bioinformatics, and highlight the most appropriate and useful resources available to cancer researchers. These include not only public databases, but general and specific bioinformatics tools which can be useful to the cancer researcher. The primary foci are function and structure prediction tools of protein genes. The result is a useful reference to cancer researchers and bioinformaticians studying cancer alike.

  8. Caspase-3 activity predicts local recurrence in rectal cancer.

    NARCIS (Netherlands)

    Heer, P. de; Bruin, E.C. de; Klein-Kranenbarg, E.; Aalbers, R.I.; Marijnen, C.A.M.; Putter, H.; Bont, H.J. de; Nagelkerke, J.F.; Krieken, J.H.J.M. van; Verspaget, H.W.; Velde, C.J. van de; Kuppen, P.J.

    2007-01-01

    PURPOSE: Radiotherapy followed by total mesorectal excision surgery has been shown to significantly reduce local recurrence rates in rectal cancer patients. Radiotherapy, however, is associated with considerable morbidity. The present study evaluated the use of biochemical detection of enzymatic cas

  9. Nomograms to predict late urinary toxicity after prostate cancer radiotherapy.

    OpenAIRE

    Mathieu, Romain; Arango, Juan David Ospina; Beckendorf, Véronique; Delobel, Jean-Bernard; Messai, Taha; Chira, Ciprian; Bossi, Alberto; Le Prisé, Elisabeth; Guerif, Stéphane; Simon, Jean-Marc; Dubray, Bernard; Zhu, Jian; Lagrange, Jean-Léon; Pommier, Pascal; Gnep, Khemara

    2013-01-01

    International audience OBJECTIVE: To analyze late urinary toxicity after prostate cancer radiotherapy (RT): symptom description and identification of patient characteristics or treatment parameters allowing for the generation of nomograms. METHODS: Nine hundred and sixty-five patients underwent RT in seventeen French centers for localized prostate cancer. Median total dose was 70 Gy (range, 65-80 Gy), using different fractionations (2 or 2.5 Gy/day) and techniques. Late urinary toxicity an...

  10. AGR2 Predicts Tamoxifen Resistance in Postmenopausal Breast Cancer Patients

    OpenAIRE

    Roman Hrstka; Veronika Brychtova; Pavel Fabian; Borivoj Vojtesek; Marek Svoboda

    2013-01-01

    Endocrine resistance is a significant problem in breast cancer treatment. Thus identification and validation of novel resistance determinants is important to improve treatment efficacy and patient outcome. In our work, AGR2 expression was determined by qRT-PCR in Tru-Cut needle biopsies from tamoxifen-treated postmenopausal breast cancer patients. Our results showed inversed association of AGR2 mRNA levels with primary treatment response (P = 0.0011) and progression-free survival (P = 0.0366)...

  11. External validation of nomograms for predicting cancer-specific mortality in penile cancer patients treated with definitive surgery

    Institute of Scientific and Technical Information of China (English)

    Yao Zhu; Wei-Jie Gu; Ding-Wei Ye; Xu-Dong Yao; Shi-Lin Zhang; Bo Dai; Hai-Liang Zhang; Yi-Jun Shen

    2014-01-01

    Using a population-based cancer registry, Thuret et al. developed 3 nomograms for estimating cancer-specific mortality in men with penile squamous cell carcinoma. In the initial cohort, only 23.0% of the patients were treated with inguinal lymphadenectomy and had pN stage. To generalize the prediction models in clinical practice, we evaluated the performance of the 3 nomograms in a series of penile cancer patients who were treated with definitive surgery. Clinicopathologic information was obtained from 160 M0 penile cancer patients who underwent primary tumor excision and regional lymphadenectomy between 1990 and 2008. The predicted probabilities of cancer-specific mortality were calculated from 3 nomograms that were based on different disease stage definitions and tumor grade. Discrimination, calibration, and clinical usefulness were assessed to compare model performance. The discrimination ability was similar in nomograms using the TNM classification or American Joint Committee on Cancer staging (Harrell’s concordance index = 0.817 and 0.832, respectively), whereas it was inferior for the Surveillance, Epidemiology and End Results staging (Harrel ’s concordance index = 0.728). Better agreement with the observed cancer-specific mortality was shown for the model consisting of TNM classification and tumor grade, which also achieved favorable clinical net benefit, with a threshold probability in the range of 0 to 42%. The nomogram consisting of TNM classification and tumor grading was shown to have better performance for predicting cancer-specific mortality in penile cancer patients who underwent definitive surgery. Our data support the integration of this model in decision-making and trial design.

  12. PBK/TOPK Expression Predicts Prognosis in Oral Cancer

    Directory of Open Access Journals (Sweden)

    Chin-Fang Chang

    2016-06-01

    Full Text Available Oral cancer is a common cancer with poor prognosis. We evaluated the expression of PBK/TOPK (PDZ-binding kinase/T-LAK cell-originated protein kinase and its prognostic significance in oral cancer. PBK/TOPK expression was measured by immunohistochemical staining of samples from 287 patients with oral cancer. The association between PBK/TOPK expression and clinicopathological features was analyzed. The prognostic value of PBK/TOPK for overall survival was determined by Kaplan-Meier analysis and Cox proportional hazard models. A high PBK/TOPK expression level was correlated with long overall survival. The prognostic role of PBK/TOPK expression was significant in young patients (p < 0.05, patients with smoking habits (p < 0.05, and late stage disease (p < 0.05. Our results suggest that PBK/TOPK expression is enhanced in oral cancer. High PBK/TOPK expression, either alone or in subgroups according to clinicopathological features, may serve as a favorable prognostic marker for patients with oral cancer.

  13. Clinical utility and limitations of tumor-feeder detection software for liver cancer embolization

    Energy Technology Data Exchange (ETDEWEB)

    Iwazawa, Jin, E-mail: iwazawa.jin@nissay-hp.or.jp [Department of Radiology, Nissay Hospital, 6-3-8 Itachibori, Nishiku, Osaka 550-0012 (Japan); Ohue, Shoichi, E-mail: ponpoko_tanuki.1125@softbank.ne.jp [Department of Radiology, Komatsu Hospital, 11-6 Kawakatsucho, Neyagawa 572-8567 (Japan); Hashimoto, Naoko, E-mail: nhashimoto0316@gmail.com [Department of Radiology, Nissay Hospital, 6-3-8 Itachibori, Nishiku, Osaka 550-0012 (Japan); Muramoto, Osamu, E-mail: muramoto.osamu@nissay-hp.or.jp [Department of Gastroenterology, Nissay Hospital, 6-3-8 Itachibori, Nishiku, Osaka 550-0012 (Japan); Mitani, Takashi, E-mail: mitani.takashi@nissay-hp.or.jp [Department of Radiology, Nissay Hospital, 6-3-8 Itachibori, Nishiku, Osaka 550-0012 (Japan)

    2013-10-01

    Purpose: To evaluate the clinical utility and limitations of a computer software program for detecting tumor feeders of hepatocellular carcinoma (HCC) during transarterial chemoembolization (TACE). Materials and methods: Forty-six patients with 59 HCC nodules underwent nonselective digital subtraction angiography (DSA) and C-arm computed tomography (CT) in the same hepatic artery. C-arm CT data sets were analyzed using the software to identify potential tumor feeders during each TACE session. For DSA analysis, 3 radiologists were independently assigned to identify tumor feeders using the DSA images in a separate session. The sensitivity of the 2 techniques in detecting tumor feeders was compared, with TACE findings as the reference standard. Factors affecting the failure of the software to detect tumor feeders were assessed by univariate and multivariate analyses. Results: We detected 65 tumor feeders supplying 59 HCC nodules during TACE sessions. The sensitivity of the software to detect tumor feeders was significantly higher than that of the manual assessment using DSA (87.7% vs. 71.8%, P < 0.001). Multivariate analysis showed that a tumor feeder diameter of <1.0 mm (hazard ratio [HR], 56.3; P = 0.003) and lipiodol accumulation adjacent to the tumor (HR, 11.4; P = 0.044) were the significant predictors for failure to detect tumor feeders. Conclusion: The software analysis was superior to manual assessment with DSA in detecting tumor feeders during TACE for HCC. However, the capability of the software to detect tumor feeders was limited by vessel caliber and by prior lipiodol accumulation to the tumor.

  14. Prediction of metastasis from low-malignant breast cancer by gene expression profiling

    DEFF Research Database (Denmark)

    Thomassen, Mads; Tan, Qihua; Eiriksdottir, Freyja;

    2007-01-01

    Promising results for prediction of outcome in breast cancer have been obtained by genome wide gene expression profiling. Some studies have suggested that an extensive overtreatment of breast cancer patients might be reduced by risk assessment with gene expression profiling. A patient group hardly...... examined in these studies is the low-risk patients for whom outcome is very difficult to predict with currently used methods. These patients do not receive adjuvant treatment according to the guidelines of the Danish Breast Cancer Cooperative Group (DBCG). In this study, 26 tumors from low-risk patients...

  15. Prediction of Candidate Drugs for Treating Pancreatic Cancer by Using a Combined Approach.

    Directory of Open Access Journals (Sweden)

    Yanfen Ma

    Full Text Available Pancreatic cancer is the leading cause of death from solid malignancies worldwide. Currently, gemcitabine is the only drug approved for treating pancreatic cancer. Developing new therapeutic drugs for this disease is, therefore, an urgent need. The C-Map project has provided a wealth of gene expression data that can be mined for repositioning drugs, a promising approach to new drug discovery. Typically, a drug is considered potentially useful for treating a disease if the drug-induced differential gene expression profile is negatively correlated with the differentially expressed genes in the target disease. However, many of the potentially useful drugs (PUDs identified by gene expression profile correlation are likely false positives because, in C-Map, the cultured cell lines to which the drug is applied are not derived from diseased tissues. To solve this problem, we developed a combined approach for predicting candidate drugs for treating pancreatic cancer. We first identified PUDs for pancreatic cancer by using C-Map-based gene expression correlation analyses. We then applied an algorithm (Met-express to predict key pancreatic cancer (KPC enzymes involved in pancreatic cancer metabolism. Finally, we selected candidates from the PUDs by requiring that their targets be KPC enzymes or the substrates/products of KPC enzymes. Using this combined approach, we predicted seven candidate drugs for treating pancreatic cancer, three of which are supported by literature evidence, and three were experimentally validated to be inhibitory to pancreatic cancer celllines.

  16. Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer.

    Science.gov (United States)

    Cook, Leah M; Araujo, Arturo; Pow-Sang, Julio M; Budzevich, Mikalai M; Basanta, David; Lynch, Conor C

    2016-01-01

    The ability to rapidly assess the efficacy of therapeutic strategies for incurable bone metastatic prostate cancer is an urgent need. Pre-clinical in vivo models are limited in their ability to define the temporal effects of therapies on simultaneous multicellular interactions in the cancer-bone microenvironment. Integrating biological and computational modeling approaches can overcome this limitation. Here, we generated a biologically driven discrete hybrid cellular automaton (HCA) model of bone metastatic prostate cancer to identify the optimal therapeutic window for putative targeted therapies. As proof of principle, we focused on TGFβ because of its known pleiotropic cellular effects. HCA simulations predict an optimal effect for TGFβ inhibition in a pre-metastatic setting with quantitative outputs indicating a significant impact on prostate cancer cell viability, osteoclast formation and osteoblast differentiation. In silico predictions were validated in vivo with models of bone metastatic prostate cancer (PAIII and C4-2B). Analysis of human bone metastatic prostate cancer specimens reveals heterogeneous cancer cell use of TGFβ. Patient specific information was seeded into the HCA model to predict the effect of TGFβ inhibitor treatment on disease evolution. Collectively, we demonstrate how an integrated computational/biological approach can rapidly optimize the efficacy of potential targeted therapies on bone metastatic prostate cancer. PMID:27411810

  17. Depth-resolved nanoscale nuclear architecture mapping for early prediction of cancer progression

    Science.gov (United States)

    Uttam, Shikhar; Pham, Hoa V.; LaFace, Justin; Hartman, Douglas J.; Liu, Yang

    2016-03-01

    Effective management of patients who are at risk of developing invasive cancer is a primary challenge in early cancer detection. Techniques that can help establish clear-cut protocols for successful triaging of at-risk patients have the potential of providing critical help in improving patient care while simultaneously reducing patient cost. We have developed such a technique for early prediction of cancer progression that uses unstained tissue sections to provide depth-resolved nanoscale nuclear architecture mapping (nanoNAM) of heterogeneity in optical density alterations manifested in precancerous lesions during cancer progression. We present nanoNAM and its application to predicting cancer progression in a well-established mouse model of spontaneous carcinogenesis: ApcMin/+ mice.

  18. Personalized in vitro cancer models to predict therapeutic response: Challenges and a framework for improvement.

    Science.gov (United States)

    Morgan, Molly M; Johnson, Brian P; Livingston, Megan K; Schuler, Linda A; Alarid, Elaine T; Sung, Kyung E; Beebe, David J

    2016-09-01

    Personalized cancer therapy focuses on characterizing the relevant phenotypes of the patient, as well as the patient's tumor, to predict the most effective cancer therapy. Historically, these methods have not proven predictive in regards to predicting therapeutic response. Emerging culture platforms are designed to better recapitulate the in vivo environment, thus, there is renewed interest in integrating patient samples into in vitro cancer models to assess therapeutic response. Successful examples of translating in vitro response to clinical relevance are limited due to issues with patient sample acquisition, variability and culture. We will review traditional and emerging in vitro models for personalized medicine, focusing on the technologies, microenvironmental components, and readouts utilized. We will then offer our perspective on how to apply a framework derived from toxicology and ecology towards designing improved personalized in vitro models of cancer. The framework serves as a tool for identifying optimal readouts and culture conditions, thus maximizing the information gained from each patient sample.

  19. Prediction of outcome after diagnosis of metachronous contralateral breast cancer

    Directory of Open Access Journals (Sweden)

    Fernö Mårten

    2011-03-01

    Full Text Available Abstract Background Although 2-20% of breast cancer patients develop a contralateral breast cancer (CBC, prognosis after CBC is still debated. Using a unique patient cohort, we have investigated whether time interval to second breast cancer (BC2 and mode of detection are associated to prognosis. Methods Information on patient-, tumour-, treatment-characteristics, and outcome was abstracted from patients' individual charts for all patients diagnosed with metachronous CBC in the Southern Healthcare Region of Sweden from 1977-2007. Distant disease-free survival (DDFS and risk of distant metastases were primary endpoints. Results The cohort included 723 patients with metachronous contralateral breast cancer as primary breast cancer event. Patients with less than three years to BC2 had a significantly impaired DDFS (p = 0.01, and in sub-group analysis, this effect was seen primarily in patients aged Conclusions In a large cohort of patients with CBC, we found the time interval to BC2 to be a strong prognostic factor for DDFS in young women and mode of detection to be related to risk of distant metastases. Future studies of tumour biology of BC2 in relation to prognostic factors found in the present study can hopefully provide biological explanations to these findings.

  20. Role of Procalcitonin and Interleukin-6 in Predicting Cancer, and Its Progression Independent of Infection.

    Directory of Open Access Journals (Sweden)

    Anne-Marie Chaftari

    Full Text Available Procalcitonin (PCT and Interleukin-6 (IL-6 have emerged as biomarkers for different inflammatory conditions. The purpose of the study was to evaluate the role of PCT and IL-6 as biomarkers of cancer and its progression in a large cohort of patients. This cross-sectional study included residual plasma samples collected from cancer patients, and control subjects without cancer. Levels of PCT and IL-6 were determined by Kryptor compact bioanalyzer. We identified 575 febrile cancer patients, 410 non-febrile cancer patients, and 79 non-cancer individuals. The median PCT level was lower in control subjects (0.029 ng/ml compared to cancer patients with stage I-III disease (0.127 ng/ml (p<0.0001 and stage IV disease (0.190 ng/ml (p<0.0001. It was also higher in febrile cancer patients (0.310 ng/ml compared to non-febrile cancer patients (0.1 ng/ml (p<0.0001. Median IL-6 level was significantly lower in the control group (0 pg/ml than in non-febrile cancer patients with stages I-III (7.376 pg/ml or stage IV (9.635 pg/ml (p<0.0001. Our results suggest a potential role for PCT and IL-6 in predicting cancer in non-febrile patients. In addition, PCT is useful in detecting progression of cancer and predicting bacteremia or sepsis in febrile cancer patients.

  1. Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria

    Directory of Open Access Journals (Sweden)

    Kimmel Marek

    2011-05-01

    Full Text Available Abstract Background Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome. Methods Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data. Results Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET. Conclusions The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.

  2. Predicting recurrence after radiotherapy in head and neck cancer

    NARCIS (Netherlands)

    Begg, A.C.

    2012-01-01

    Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Radiotherapy is a mainstay of treatment, either alone for early stage tumors or combined with chemotherapy for late stage tumors. An overall 5-year survival rate of around 50% for HNSCC demonstrates that treatme

  3. Patient factors may predict anastomotic complications after rectal cancer surgery

    Directory of Open Access Journals (Sweden)

    Dana M. Hayden

    2015-03-01

    Conclusion: Our study identifies preoperative anemia as possible risk factor for anastomotic leak and neoadjuvant chemoradiation may lead to increased risk of complications overall. Further prospective studies will help to elucidate these findings as well as identify amenable factors that may decrease risk of anastomotic complications after rectal cancer surgery.

  4. Thrombocytosis of Liver Metastasis from Colorectal Cancer as Predictive Factor

    DEFF Research Database (Denmark)

    Josa, Valeria; Krzystanek, Marcin; Vass, Tamas;

    2015-01-01

    There is increasing evidence that thrombocytosis is associated with tumor invasion and metastasis formation. It was shown in several solid tumor types that thrombocytosis prognosticates cancer progression. The aim of this study was to evaluate preoperative thrombocytosis as a potential prognostic...

  5. Preoperative Nomogram Predicting the 10-Year Probability of Prostate Cancer Recurrence After Radical Prostatectomy

    OpenAIRE

    Stephenson, Andrew J.; Scardino, Peter T.; Eastham, James A.; Bianco, Fernando J.; Dotan, Zohar A.; Fearn, Paul A.; Michael W Kattan

    2006-01-01

    An existing preoperative nomogram predicts the probability of prostate cancer recurrence, defined by prostate-specific antigen (PSA), at 5 years after radical prostatectomy based on clinical stage, serum PSA, and biopsy Gleason grade. In an updated and enhanced nomogram, we have extended the predictions to 10 years, added the prognostic information of systematic biopsy results, and enabled the predictions to be adjusted for the year of surgery. Cox regression analysis was used to model the cl...

  6. Prediction of pulmonary complications after a lobectomy in patients with non-small cell lung cancer

    OpenAIRE

    Uramoto, H; Nakanishi, R.; Fujino, Y; Imoto, H; Takenoyama, M; Yoshimatsu, T.; Oyama, T; Osaki, T.; Yasumoto, K

    2001-01-01

    BACKGROUND—Although the preoperative prediction of pulmonary complications after lung major surgery has been reported in various papers, it still remains unclear.
METHODS—Eighty nine patients with stage I-IIIA non-small cell lung cancer (NSCLC) who underwent a complete resection at our institute from 1994-8 were evaluated for the feasibility of making a preoperative prediction of pulmonary complications. All had either a predicted postoperative forced vital capacity (FVC)...

  7. Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial

    DEFF Research Database (Denmark)

    Wille, Mathilde M. Winkler; van Riel, Sarah J.; Saghir, Zaigham;

    2015-01-01

    Objectives: Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. Methods: From...... the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were...

  8. Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines

    Directory of Open Access Journals (Sweden)

    Ching Wai-Ki

    2010-10-01

    Full Text Available Abstract Background Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets. Results In this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92. Conclusions The cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs.

  9. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    Directory of Open Access Journals (Sweden)

    Hao Ke

    2011-11-01

    Full Text Available Abstract Background The prognosis of hepatocellular carcinoma (HCC varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. Methods Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. Results HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. Conclusion When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome.

  10. Current status of predictive biomarkers for neoadjuvant therapy in esophageal cancer

    Institute of Scientific and Technical Information of China (English)

    Norihisa; Uemura; Tadashi; Kondo

    2014-01-01

    Neoadjuvant therapy has been proven to be extremely valuable and is widely used for advanced esophageal cancer. However, a significant proportion of treated patients(60%-70%) does not respond well to neoadjuvant treatments and develop severe adverse effects. Therefore, predictive markers for individualization of multimodality treatments are urgently needed in esophageal cancer. Recently, molecular biomarkers that predict the response to neoadjuvant therapy have been explored in multimodal approaches in esophageal cancer and successful examples of biomarker identification have been reported. In this review, promising candidates for predictive molecular biomarkers developed by using multiple molecular approaches are reviewed. Moreover, treatment strategies based on the status of predicted biomarkers are discussed, while considering the international differences in the clinical background. However, in the absence of adequate treatment options related to the results of the biomarker test, the usefulness of these diagnostic tools is limited and new effective therapies for biomarker-identified nonresponders to cancer treatment should be concurrent with the progress of predictive technologies. Further improvement in the prognosis of esophageal cancer patients can be achieved through the introduction of novel therapeutic approaches in clinical practice.

  11. Cancer incidence predictions in the North of Portugal: keeping population-based cancer registration up to date.

    Science.gov (United States)

    Castro, Clara; Antunes, Luís; Lunet, Nuno; Bento, Maria José

    2016-09-01

    Decision making towards cancer prevention and control requires monitoring of trends in cancer incidence and accurate estimation of its burden in different settings. We aimed to estimate the number of incident cases in northern Portugal for 2015 and 2020 (all cancers except nonmelanoma skin and for the 15 most frequent tumours). Cancer cases diagnosed in 1994-2009 were collected by the North Region Cancer Registry of Portugal (RORENO) and corresponding population figures were obtained from Statistics Portugal. JoinPoint regression was used to analyse incidence trends. Population projections until 2020 were derived by RORENO. Predictions were performed using the Poisson regression models proposed by Dyba and Hakulinen. The number of incident cases is expected to increase by 18.7% in 2015 and by 37.6% in 2020, with lower increments among men than among women. For most cancers considered, the number of cases will keep rising up to 2020, although decreasing trends of age-standardized rates are expected for some tumours. Cervix was the only cancer with a decreasing number of incident cases in the entire period. Thyroid and lung cancers were among those with the steepest increases in the number of incident cases expected for 2020, especially among women. In 2020, the top five cancers are expected to account for 82 and 62% of all cases diagnosed in men and women, respectively. This study contributes to a broader understanding of cancer burden in the north of Portugal and provides the basis for keeping population-based incidence estimates up to date.

  12. Cancer incidence predictions in the North of Portugal: keeping population-based cancer registration up to date.

    Science.gov (United States)

    Castro, Clara; Antunes, Luís; Lunet, Nuno; Bento, Maria José

    2016-09-01

    Decision making towards cancer prevention and control requires monitoring of trends in cancer incidence and accurate estimation of its burden in different settings. We aimed to estimate the number of incident cases in northern Portugal for 2015 and 2020 (all cancers except nonmelanoma skin and for the 15 most frequent tumours). Cancer cases diagnosed in 1994-2009 were collected by the North Region Cancer Registry of Portugal (RORENO) and corresponding population figures were obtained from Statistics Portugal. JoinPoint regression was used to analyse incidence trends. Population projections until 2020 were derived by RORENO. Predictions were performed using the Poisson regression models proposed by Dyba and Hakulinen. The number of incident cases is expected to increase by 18.7% in 2015 and by 37.6% in 2020, with lower increments among men than among women. For most cancers considered, the number of cases will keep rising up to 2020, although decreasing trends of age-standardized rates are expected for some tumours. Cervix was the only cancer with a decreasing number of incident cases in the entire period. Thyroid and lung cancers were among those with the steepest increases in the number of incident cases expected for 2020, especially among women. In 2020, the top five cancers are expected to account for 82 and 62% of all cases diagnosed in men and women, respectively. This study contributes to a broader understanding of cancer burden in the north of Portugal and provides the basis for keeping population-based incidence estimates up to date. PMID:26317384

  13. Modified comet assay prediction of radiosensitivity in 105 nasopharyngeal cancer patients

    International Nuclear Information System (INIS)

    Objective: To evaluate the value of modified comet assay for predicting clinical radiosensitivity of nasopharyngeal cancers (NPC). Methods: Biopsy samples were collected and analyzed by alkaline comet assay in 105 NPC patients before radiotherapy. The biopsy material from the primary tumor, having been prepared as isolated cell suspension, was divided into two items: control and 5 Gy per fraction irradiation. All tumors had been examined by spiral CT or MRI before treatment and up until 50 Gy of radiation by conventional fraction, so as to measured the S0 and S50 of the maximum tumor cross-section area. Regression rate was used to evaluate the clinical tumor radiosensitivity, and expressed as regression ratio (Rs=[S0-S50 ]/S0). The tumor radiosensitivity was set as high sensitivity (Rs≥0.9), intermediate sensitivity (0.7≤Rss<0.7). According to the cell DNA photos in modified comet assay, I A, I B, II A II B graphs were classified and the radiosensitivity was decided by the value of RTM and absorption of light density (A). Statistical analysis software SPSS10.0 was used. Kappa analytical method was used for consistency test between clinical results and laboratory results. Results: In the assay of clinical radiosensitivity, 41 highly sensitive, 21 intermediate sensitive and 43 low sensitive tumors were found. In the modified comet assay, 58 sensitive and 47 insensitive tumors were found. The sensitivity, specificity and accuracy were 71.0%, 67.4% and 69.5%. The results of modified comet assay were similar to the clinical results in 73 patients. Kappa analytical result neared moderate-high consistency (Kappa=0.38) between modified comet assay and clinical radiosensitivity. Conclusions: Our study demonstrates that evident correlation is present between results of modified comet assay and clinical radiosensitivity of NPC. The modified comet assay is potentially favored in clinical application due to its convenience and short cycle of assay

  14. Color Doppler quantitative measures to predict outcome of biopsies in prostate cancer

    Energy Technology Data Exchange (ETDEWEB)

    Strigari, Lidia; Marsella, Annelisa; Canitano, Stefano; Gomellini, Sara; Arcangeli, Stefano; Genovese, Elisabetta; Saracino, Biancamaria; Petrongari, Maria Grazia; Sentinelli, Steno; Crecco, Marcello; Benassi, Marcello; Arcangeli, Giorgio [Laboratory of Medical Physics and Expert Systems, Regina Elena National Cancer Institute, Rome 00144 (Italy); Radiology Department, Regina Elena National Cancer Institute, Rome 00144 (Italy); Radiotherapy Department, Regina Elena National Cancer Institute, Rome 00144 (Italy); Laboratory of Medical Physics and Expert Systems, Regina Elena National Cancer Center, Rome 00144 (Italy); Radiotherapy Department, Regina Elena National Cancer Center, Rome 00144 (Italy); Pathology Department, Regina Elena National Cancer Center, Rome 00144 (Italy); Radiology Department, Regina Elena National Cancer Center, Rome 00144 (Italy); Laboratory of Medical Physics and Expert Systems, Regina Elena National Cancer Institute, Rome 00144 (Italy); Radiotherapy Department, Regina Elena National Cancer Institute, Rome 00144 (Italy)

    2008-11-15

    Purpose: The aim was to correlate the color Doppler flow activity pre- and postradiotherapy, using transrectal color Doppler ultrasonography (CDUS) and the 2 year positive biopsy rate after radiotherapy in patients with prostate cancer. Methods and materials: Analysis was carried out in 69 out of 160 patients who had undergone treatment with 3D-conformal radiotherapy (3D-CRT) to prostate and seminal vesicles. Patients were randomized to receive 80 Gy in 40 fractions in 8 weeks (arm A) and 62 Gy in 20 fractions in 5 weeks, 4 fractions per week (arm B). Color Doppler flow activity (CDFA) was evaluated calculating the vascularization index (VI), defined as the ratio between the colored and total pixels in the whole and peripheral prostate, delineated by a radiation oncologist on CDUS images, using EcoVasc a home-made software. The difference between the 2 year post- and pre-3D-CRT maximum VI (VI{sub max}), named {Delta}VI{sub max}, was calculated in the whole and peripheral prostate for each patient. Then, {Delta}VI{sub max} and the detected 2 year biopsy outcome were analyzed using the receiver operating characteristics (ROC) technique. Results: The VI{sub max} increased or decreased in patients with positive or negative biopsies, respectively, compared to the value before RT in both arms. The area under the ROC curve for {Delta}VI{sub max} in the whole and peripheral prostate is equal to 0.790 and 0.884, respectively. Conclusion: The {Delta}VI{sub max} index, comparing CDFA at 2 years compared to that before RT, allows the 2 year postradiotherapy positive biopsy rate to be predicted.

  15. Can erectile function be predicted after prostate cancer treatment?

    Institute of Scientific and Technical Information of China (English)

    Frances M Alba; Run Wang

    2012-01-01

    Prostate cancer (PCa) is the most common solid-organ cancer in American males and the second most common cause of cancer-related death in men.With the advent of prostate-specific antigen screening,death from PCa continues to decline.However,recent evidence suggests that there is now a trend towards increasing incidence.1 Current screening strategies result in increased incidence of low-risk PCa and importantly,the diagnosis of PCa is becoming more common in younger patients.2 The majority of early-stage PCa patients have a high likelihood of disease free survival after treatment.Localized disease is most commonly treated with radical prostatectomy,external beam radiation therapy (EBRT)or brachytherapy.Local therapy yields excellent long-term survival results in low-risk patients;however,treatments may result in a significant treatment-related morbidity,and ultimately impact patient health-related quality of life (HRQOL).3 The effects of these treatment modalities on the HRQOL have been evaluated and compared and all three are associated with increased sexual dysfunction.

  16. Estimation of volumetric breast density for breast cancer risk prediction

    Science.gov (United States)

    Pawluczyk, Olga; Yaffe, Martin J.; Boyd, Norman F.; Jong, Roberta A.

    2000-04-01

    Mammographic density (MD) has been shown to be a strong risk predictor for breast cancer. Compared to subjective assessment by a radiologist, computer-aided analysis of digitized mammograms provides a quantitative and more reproducible method for assessing breast density. However, the current methods of estimating breast density based on the area of bright signal in a mammogram do not reflect the true, volumetric quantity of dense tissue in the breast. A computerized method to estimate the amount of radiographically dense tissue in the overall volume of the breast has been developed to provide an automatic, user-independent tool for breast cancer risk assessment. The procedure for volumetric density estimation consists of first correcting the image for inhomogeneity, then performing a volume density calculation. First, optical sensitometry is used to convert all images to the logarithm of relative exposure (LRE), in order to simplify the image correction operations. The field non-uniformity correction, which takes into account heel effect, inverse square law, path obliquity and intrinsic field and grid non- uniformity is obtained by imaging a spherical section PMMA phantom. The processed LRE image of the phantom is then used as a correction offset for actual mammograms. From information about the thickness and placement of the breast, as well as the parameters of a breast-like calibration step wedge placed in the mammogram, MD of the breast is calculated. Post processing and a simple calibration phantom enable user- independent, reliable and repeatable volumetric estimation of density in breast-equivalent phantoms. Initial results obtained on known density phantoms show the estimation to vary less than 5% in MD from the actual value. This can be compared to estimated mammographic density differences of 30% between the true and non-corrected values. Since a more simplistic breast density measurement based on the projected area has been shown to be a strong indicator

  17. Deletion of Chromosome 4q Predicts Outcome in Stage II Colon Cancer Patients

    Directory of Open Access Journals (Sweden)

    R. P. M. Brosens

    2010-01-01

    Full Text Available Background: Around 30% of all stage II colon cancer patients will relapse and die of their disease. At present no objective parameters to identify high-risk stage II colon cancer patients, who will benefit from adjuvant chemotherapy, have been established. With traditional histopathological features definition of high-risk stage II colon cancer patients is inaccurate. Therefore more objective and robust markers for prediction of relapse are needed. DNA copy number aberrations have proven to be robust prognostic markers, but have not yet been investigated for this specific group of patients. The aim of the present study was to identify chromosomal aberrations that can predict relapse of tumor in patients with stage II colon cancer.

  18. Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles.

    Science.gov (United States)

    Vazquez, Ana I; Veturi, Yogasudha; Behring, Michael; Shrestha, Sadeep; Kirst, Matias; Resende, Marcio F R; de Los Campos, Gustavo

    2016-07-01

    Whole-genome multiomic profiles hold valuable information for the analysis and prediction of disease risk and progression. However, integrating high-dimensional multilayer omic data into risk-assessment models is statistically and computationally challenging. We describe a statistical framework, the Bayesian generalized additive model ((BGAM), and present software for integrating multilayer high-dimensional inputs into risk-assessment models. We used BGAM and data from The Cancer Genome Atlas for the analysis and prediction of survival after diagnosis of breast cancer. We developed a sequence of studies to (1) compare predictions based on single omics with those based on clinical covariates commonly used for the assessment of breast cancer patients (COV), (2) evaluate the benefits of combining COV and omics, (3) compare models based on (a) COV and gene expression profiles from oncogenes with (b) COV and whole-genome gene expression (WGGE) profiles, and (4) evaluate the impacts of combining multiple omics and their interactions. We report that (1) WGGE profiles and whole-genome methylation (METH) profiles offer more predictive power than any of the COV commonly used in clinical practice (e.g., subtype and stage), (2) adding WGGE or METH profiles to COV increases prediction accuracy, (3) the predictive power of WGGE profiles is considerably higher than that based on expression from large-effect oncogenes, and (4) the gain in prediction accuracy when combining multiple omics is consistent. Our results show the feasibility of omic integration and highlight the importance of WGGE and METH profiles in breast cancer, achieving gains of up to 7 points area under the curve (AUC) over the COV in some cases. PMID:27129736

  19. A nomogram to predict Gleason sum upgrading of clinically diagnosed localized prostate cancer among Chinese patients

    Institute of Scientific and Technical Information of China (English)

    Jin-You Wang; Yao Zhu; Chao-Fu Wang; Shi-Lin Zhang; Bo Dai; Ding-Wei Ye

    2014-01-01

    Although several models have been developed to predict the probability of Gleason sum upgrading between biopsy and radical prostatectomy specimens, most of these models are restricted to prostate-specific antigen screening-detected prostate cancer. This study aimed to build a nomogram for the prediction of Gleason sum upgrading in clinical y diagnosed prostate cancer. The study cohort comprised 269 Chinese prostate cancer patients who underwent prostate biopsy with a minimum of 10 cores and were subsequently treated with radical prostatectomy. Of al included patients, 220 (81.8%) were referred with clinical symptoms. The prostate-specific antigen level, primary and secondary biopsy Gleason scores, and clinical T category were used in a multivariate logistic regression model to predict the probability of Gleason sum upgrading. The developed nomogram was validated internally. Gleason sum upgrading was observed in 90 (33.5%) patients. Our nomogram showed a bootstrap-corrected concordance index of 0.789 and good calibration using 4 readily available variables. The nomogram also demonstrated satisfactory statistical performance for predicting significant upgrading. External validation of the nomogram published by Chun et al. in our cohort showed a marked discordance between the observed and predicted probabilities of Gleason sum upgrading. In summary, a new nomogram to predict Gleason sum upgrading in clinically diagnosed prostate cancer was developed, and it demonstrated good statistical performance upon internal validation.

  20. A nomogram to predict Gleason sum upgrading of clinically diagnosed localized prostate cancer among Chinese patients

    Directory of Open Access Journals (Sweden)

    Jin-You Wang

    2014-05-01

    Full Text Available Although several models have been developed to predict the probability of Gleason sum upgrading between biopsy and radical prostatectomy specimens, most of these models are restricted to prostate-specific antigen screening-detected prostate cancer. This study aimed to build a nomogram for the prediction of Gleason sum upgrading in clinically diagnosed prostate cancer. The study cohort comprised 269 Chinese prostate cancer patients who underwent prostate biopsy with a minimum of 10 cores and were subsequently treated with radical prostatectomy. Of all included patients, 220 (81.8% were referred with clinical symptoms. The prostate-specific antigen level, primary and secondary biopsy Gleason scores, and clinical T category were used in a multivariate logistic regression model to predict the probability of Gleason sum upgrading. The developed nomogram was validated internally. Gleason sum upgrading was observed in 90 (33.5% patients. Our nomogram showed a bootstrap-corrected concordance index of 0.789 and good calibration using 4 readily available variables. The nomogram also demonstrated satisfactory statistical performance for predicting significant upgrading. External validation of the nomogram published by Chun et al. in our cohort showed a marked discordance between the observed and predicted probabilities of Gleason sum upgrading. In summary, a new nomogram to predict Gleason sum upgrading in clinically diagnosed prostate cancer was developed, and it demonstrated good statistical performance upon internal validation.

  1. The Role of Psychological Hardiness and Marital Satisfaction in Predicting Posttraumatic Growth in a Sample of Women With Breast Cancer in Isfahan

    Science.gov (United States)

    Aflakseir, Abdulaziz; Nowroozi, Safoora; Mollazadeh, Javad; Goodarzi, Mohammad Ali

    2016-01-01

    Background Posttraumatic growth (PTG) refers to positive psychological change experienced as a result of the struggle with highly challenging life circumstances. PTG in cancer survivors is related to several psychosocial factors such as psychological hardiness and marital satisfaction. Objectives The purpose of this study was to examine the prediction of posttraumatic growth based on psychological hardiness and marital satisfaction. Patients and Methods A total of 120 women with breast cancer were recruited from several hospitals in Isfahan using convenience sampling. Participants completed the research questionnaires including the posttraumatic growth inventory (PTGI), the Ahvaz psychological hardiness scale and the Enrich’s marital satisfaction scale (EMS). Statistical analysis including means, standard deviation, Pearson’s correlation and multiple regression analysis were carried out using SPSS software (version 16). Results Results indicated that the majority of patients with cancer experienced posttraumatic growth. Findings also showed that psychological hardiness, marital satisfaction and longer time since diagnosis of cancer significantly predicted posttraumatic growth. Conclusions This study highlights the significant role of psychological hardiness and marital support in personal growth of breast cancer survivors.

  2. A Study of Performance and Effort Expectancy Factors among Generational and Gender Groups to Predict Enterprise Social Software Technology Adoption

    Science.gov (United States)

    Patel, Sunil S.

    2013-01-01

    Social software technology has gained considerable popularity over the last decade and has had a great impact on hundreds of millions of people across the globe. Businesses have also expressed their interest in leveraging its use in business contexts. As a result, software vendors and business consumers have invested billions of dollars to use…

  3. Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI

    OpenAIRE

    Alina Tudorica; Oh, Karen Y.; Stephen Y-C Chui; Nicole Roy; Troxell, Megan L.; Arpana Naik; Kathleen A Kemmer; Yiyi Chen; Megan L Holtorf; Aneela Afzal; Charles S Springer Jr.; Xin Li; Wei Huang

    2016-01-01

    The purpose is to compare quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) metrics with imaging tumor size for early prediction of breast cancer response to neoadjuvant chemotherapy (NACT) and evaluation of residual cancer burden (RCB). Twenty-eight patients with 29 primary breast tumors underwent DCE-MRI exams before, after one cycle of, at midpoint of, and after NACT. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Eval...

  4. Coping Strategies of Southern Italian Women Predict Distress Following Breast Cancer Surgery

    OpenAIRE

    Rossana De Feudis; Tiziana Lanciano; Stefano Rinaldi

    2015-01-01

    The present study was aimed at investigating the role of coping strategies in predicting emotional distress following breast cancer, over and above the illness severity, operationalized in terms of the type of surgery performed. In order to achieve this goal, two groups of newly diagnosed breast cancer women were selected and compared on the basis of the type of surgical treatment received. A subsample of 30 women with quadrantectomy and sentinel lymph-node biopsy (SLNB) and a subsample of 31...

  5. Nomogram to predict ypN status after chemoradiation in patients with locally advanced rectal cancer

    OpenAIRE

    Jwa, E; Kim, J. H.; HAN, S; Park, J-h; Lim, S-B; Kim, J. C.; Hong, Y S; Kim, T. W.; Yu, C. S.

    2014-01-01

    Background: Pelvic lymph node (LN) status after preoperative chemoradiotherapy (CRT) is an important indicator of oncologic outcome in patients with locally advanced rectal cancer. The purpose of this study was to develop a nomogram to predict LN status after preoperative CRT in locally advanced rectal cancer patients. Methods: The nomogram was developed in a training cohort (n=891) using logistic regression analyses and validated in a validation cohort (n=258) from a prospectively registered...

  6. Clinical Nomogram for Predicting Survival of Esophageal Cancer Patients after Esophagectomy

    OpenAIRE

    Jinlin Cao; Ping Yuan; Luming Wang; Yiqing Wang; Honghai Ma; Xiaoshuai Yuan; Wang Lv; Jian Hu

    2016-01-01

    The aim of this study was to construct an effective clinical nomogram for predicting the survival of esophageal cancer patients after esophagectomy. We identified esophageal cancer patients (n = 4,281) who underwent esophagectomy between 1988 and 2007 from the Surveillance, Epidemiology, and End Results (SEER) 18 registries database. Clinically significant parameters for survival were used to construct a nomogram based on Cox regression analyses. The model was validated using bootstrap resamp...

  7. Assessment of computational methods for predicting the effects of missense mutations in human cancers

    OpenAIRE

    Gnad, Florian; Baucom, Albion; Mukhyala, Kiran; Manning, Gerard; Zhang, Zemin

    2013-01-01

    Background Recent advances in sequencing technologies have greatly increased the identification of mutations in cancer genomes. However, it remains a significant challenge to identify cancer-driving mutations, since most observed missense changes are neutral passenger mutations. Various computational methods have been developed to predict the effects of amino acid substitutions on protein function and classify mutations as deleterious or benign. These include approaches that rely on evolution...

  8. Modifiable and fixed factors predicting quality of life in people with colorectal cancer

    OpenAIRE

    Gray, N M; Hall, S. J.; Browne, S.; Macleod, U; Mitchell, E.; Lee, A J; Johnston, M.; Wyke, S.; Samuel, L; Weller, D.; Campbell, N C

    2011-01-01

    Background: People with colorectal cancer have impaired quality of life (QoL). We investigated what factors were most highly associated with it.Methods: Four hundred and ninety-six people with colorectal cancer completed questionnaires about QoL, functioning, symptoms, co-morbidity, cognitions and personal and social factors. Disease, treatment and co-morbidity data were abstracted from case notes. Multiple linear regression identified modifiable and unmodifiable factors independently predict...

  9. Texture analysis on MR images helps predicting non-response to NAC in breast cancer.

    OpenAIRE

    Michoux, N.; Van den Broeck, S; Lacoste, L; Fellah, L.; Galant, Christine; Berlière, M.; Leconte, I

    2015-01-01

    Background To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI. Methods Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast...

  10. Predictive accuracy of the PanCan lung cancer risk prediction model - external validation based on CT from the Danish Lung Cancer Screening Trial

    International Nuclear Information System (INIS)

    Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. From the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to evaluate risk discrimination. AUCs of 0.826-0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer; in fact opposing effects of sex were observed in the two cohorts. Thus, female sex appeared to lower the risk (p = 0.047 and p = 0.040) in the DLCST. High risk discrimination was validated in the DLCST cohort, mainly determined by nodule size. Age and family history of lung cancer were significant predictors and could be included in the parsimonious model. Sex appears to be a less useful predictor. (orig.)

  11. Predictive accuracy of the PanCan lung cancer risk prediction model - external validation based on CT from the Danish Lung Cancer Screening Trial

    Energy Technology Data Exchange (ETDEWEB)

    Winkler Wille, Mathilde M.; Dirksen, Asger [Gentofte Hospital, Department of Respiratory Medicine, Hellerup (Denmark); Riel, Sarah J. van; Jacobs, Colin; Scholten, Ernst T.; Ginneken, Bram van [Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen (Netherlands); Saghir, Zaigham [Herlev Hospital, Department of Respiratory Medicine, Herlev (Denmark); Pedersen, Jesper Holst [Copenhagen University Hospital, Department of Thoracic Surgery, Rigshospitalet, Koebenhavn Oe (Denmark); Hohwue Thomsen, Laura [Hvidovre Hospital, Department of Respiratory Medicine, Hvidovre (Denmark); Skovgaard, Lene T. [University of Copenhagen, Department of Biostatistics, Koebenhavn Oe (Denmark)

    2015-10-15

    Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. From the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to evaluate risk discrimination. AUCs of 0.826-0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer; in fact opposing effects of sex were observed in the two cohorts. Thus, female sex appeared to lower the risk (p = 0.047 and p = 0.040) in the DLCST. High risk discrimination was validated in the DLCST cohort, mainly determined by nodule size. Age and family history of lung cancer were significant predictors and could be included in the parsimonious model. Sex appears to be a less useful predictor. (orig.)

  12. The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease.

    Science.gov (United States)

    Antila, Kari; Lötjönen, Jyrki; Thurfjell, Lennart; Laine, Jarmo; Massimini, Marcello; Rueckert, Daniel; Zubarev, Roman A; Orešič, Matej; van Gils, Mark; Mattila, Jussi; Hviid Simonsen, Anja; Waldemar, Gunhild; Soininen, Hilkka

    2013-04-01

    Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates for therapies are under intense research. Even though there is currently no cure for AD, its effects can be managed. Today the significance of early and precise diagnosis of AD is emphasized in order to minimize its irreversible effects on the nervous system. When new drugs and therapies enter the market it is also vital to effectively identify the right candidates to benefit from these. The main objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data, electrophysiological measurements of the brain and structural, functional and molecular brain images. We also designed and built a statistical model and a framework for exploiting these biomarkers with other available patient history and background data. We were able to discover several potential novel biomarker candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials.

  13. Risk factors predictive of atrial fibrillation after lung cancer surgery.

    Science.gov (United States)

    Iwata, Takekazu; Nagato, Kaoru; Nakajima, Takahiro; Suzuki, Hidemi; Yoshida, Shigetoshi; Yoshino, Ichiro

    2016-08-01

    Postoperative atrial fibrillation (POAF), the most frequent arrhythmia after pulmonary resection, is a cause of both morbidity and mortality. Being able to predict the risk of POAF before surgery would help us evaluate the surgical risk and plan prophylaxis. We investigated the reported preoperative risk factors associated with the incidence of POAF and found that the recommended predictive factors were quite variable. Therefore, we evaluated the previously reported preoperative risk factors for POAF using our institutional data. We discuss our findings in this short review. Male gender, resected lung volume, brain natriuretic peptide (BNP), and left ventricular early transmitral velocity/mitral annular early diastolic velocity (E/e') calculated by echocardiography were suggested as independent predictors for POAF, but the predictive values of each individual parameter were not high. The lack of definitive predictors for POAF warrants further investigations by gathering the reported knowledge, to establish an effective preoperative examination strategy. PMID:26471506

  14. Cancer-related intrusive thoughts predict behavioral symptoms following breast cancer treatment

    OpenAIRE

    Dupont, A; Bower, JE; Stanton, AL; Ganz, PA

    2014-01-01

    Objective: Behavioral symptoms are common in breast cancer survivors, including disturbances in energy, sleep, and mood, though few risk factors for these negative outcomes have been identified. Our study examined intrusive thoughts as a predictor of lingering symptoms in breast cancer survivors in the year following treatment. Method: Data come from the Moving Beyond Cancer psychoeducational intervention trial, aimed at easing the transition from patient to survivor. Women (n = 558) complete...

  15. Diffusion Weighted MRI as a predictive tool for effect of radiotherapy in locally advanced cervical cancer

    DEFF Research Database (Denmark)

    Haack, Søren; Tanderup, Kari; Fokdal, Lars;

    Diffusion weighted MRI has shown great potential in diagnostic cancer imaging and may also have value for monitoring tumor response during radiotherapy. Patients with advanced cervical cancer are treated with external beam radiotherapy followed by brachytherapy. This study evaluates the value of DW......-MRI for predicting outcome of patients with advanced cervical cancer at time of brachytherapy. Volume of hyper-intensity on highly diffusion sensitive images and resulting ADC value for treatment responders and non-responders is compared. The change of ADC and volume of hyper-intensity over time of BT is also...

  16. Gene panel model predictive of outcome in patients with prostate cancer.

    Science.gov (United States)

    Rabiau, Nadège; Dantal, Yann; Guy, Laurent; Ngollo, Marjolaine; Dagdemir, Aslihan; Kemeny, Jean-Louis; Terris, Benoît; Vieillefond, Annick; Boiteux, Jean-Paul; Bignon, Yves-Jean; Bernard-Gallon, Dominique

    2013-08-01

    In men at high risk for prostate cancer, established clinical and pathological parameters provide only limited prognostic information. Here we analyzed a French cohort of 103 prostate cancer patients and developed a gene panel model predictive of outcome in this group of patients. The model comprised of a 15-gene TaqMan Low-Density Array (TLDA) card, with gene expressions compared to a standardized reference. The RQ value for each gene was calculated, and a scoring system was developed. Summing all the binary scores (0 or 1) corresponding to the 15 genes, a global score is obtained between 0 and 15. This global score can be compared to Gleason score (0 to 10) by recalculating it into a 0-10 scaled score. A scaled score ≥2 suggested that the patient is suffering from a prostate cancer, and a scaled score ≥7 flagged aggressive cancer. Statistical analyses demonstrated a strongly significant linear correlation (p=3.50E-08) between scaled score and Gleason score for this prostate cancer cohort (N=103). These results support the capacity of this designed 15 target gene TLDA card approach to predict outcome in prostate cancer, opening up a new avenue for personalized medicine through future independent replication and applications for rapid identification of aggressive prostate cancer phenotypes for early intervention.

  17. A Novel Approach to Predict Core Residues on Cancer-Related DNA-Binding Domains

    OpenAIRE

    Ka-Chun Wong

    2016-01-01

    Protein–DNA interactions are involved in different cancer pathways. In particular, the DNA-binding domains of proteins can determine where and how gene regulatory regions are bound in different cell lines at different stages. Therefore, it is essential to develop a method to predict and locate the core residues on cancer-related DNA-binding domains. In this study, we propose a computational method to predict and locate core residues on DNA-binding domains. In particular, we have selected the ...

  18. Prediction of survival in patients with Stage IV kidney cancer

    Directory of Open Access Journals (Sweden)

    L. V. Mirilenko

    2015-01-01

    Full Text Available The efficiency of treatment was evaluated and the predictors of adjusted survival (AS were identified in patients with disseminated kidney cancer treated at the Republican Research and Practical Center for Oncology and Medical Radiology in 1999 to 2011 (A.E. Okeanov, P.I. Moiseev, L.F. Levin. Malignant tumors in Belarus, 2001–2012. Edited by O.G. Sukonko. Seven factors (regional lymph node metastases; distant bone metastases; a high-grade tumor; sarcomatous tumor differentiation; hemoglobin levels of < 125 g/l in women and < 150 g/l in men; an erythrocyte sedimentation rate of 40 mm/h; palliative surgery were found to have an independent, unfavorable impact on AS. A multidimensional model was built to define what risk group low (no more than 2 poor factors, moderate (3–4 poor factors, and high (more than 4 poor factors the patients with Stage IV kidney cancer belonged to. In these groups, the median survival was 34.7, 17.2, and 4.0 months and 3-year AS rates were 48.6, 24.6, and 3.2 %, respectively. 

  19. Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer

    Directory of Open Access Journals (Sweden)

    Yun-Xia Zhang

    2016-01-01

    Full Text Available Purpose. The objective of our study was to predicate candidate genes in cervical cancer (CC using a network-based strategy and to understand the pathogenic process of CC. Methods. A pathogenic network of CC was extracted based on known pathogenic genes (seed genes and differentially expressed genes (DEGs between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed. Results. In this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes, VIM had the highest weight value. Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism. Conclusion. Candidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application.

  20. Improving Software Reliability Forecasting

    NARCIS (Netherlands)

    Burtsy, Bernard; Albeanu, Grigore; Boros, Dragos N.; Popentiu, Florin; Nicola, Victor

    1997-01-01

    This work investigates some methods for software reliability forecasting. A supermodel is presented as a suited tool for prediction of reliability in software project development. Also, times series forecasting for cumulative interfailure time is proposed and illustrated.

  1. Mechanistic modelling of cancer: some reflections from software engineering and philosophy of science.

    Science.gov (United States)

    Cañete-Valdeón, José M; Wieringa, Roel; Smallbone, Kieran

    2012-12-01

    There is a growing interest in mathematical mechanistic modelling as a promising strategy for understanding tumour progression. This approach is accompanied by a methodological change of making research, in which models help to actively generate hypotheses instead of waiting for general principles to become apparent once sufficient data are accumulated. This paper applies recent research from philosophy of science to uncover three important problems of mechanistic modelling which may compromise its mainstream application, namely: the dilemma of formal and informal descriptions, the need to express degrees of confidence and the need of an argumentation framework. We report experience and research on similar problems from software engineering and provide evidence that the solutions adopted there can be transferred to the biological domain. We hope this paper can provoke new opportunities for further and profitable interdisciplinary research in the field.

  2. Mechanistic modelling of cancer: some reflections from software engineering and philosophy of science

    Science.gov (United States)

    Cañete-Valdeón, José M.; Wieringa, Roel; Smallbone, Kieran

    2012-12-01

    There is a growing interest in mathematical mechanistic modelling as a promising strategy for understanding tumour progression. This approach is accompanied by a methodological change of making research, in which models help to actively generate hypotheses instead of waiting for general principles to become apparent once sufficient data are accumulated. This paper applies recent research from philosophy of science to uncover three important problems of mechanistic modelling which may compromise its mainstream application, namely: the dilemma of formal and informal descriptions, the need to express degrees of confidence and the need of an argumentation framework. We report experience and research on similar problems from software engineering and provide evidence that the solutions adopted there can be transferred to the biological domain. We hope this paper can provoke new opportunities for further and profitable interdisciplinary research in the field.

  3. Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines

    Directory of Open Access Journals (Sweden)

    Chunying Zhang

    2014-01-01

    Full Text Available We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT related genes were selected by significance analysis of microarrays (SAM. Orthogonal latent variables (LVs were then extracted by the partial least squares (PLS method as the new compressive input variables. Finally, support vector machine (SVM regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2 Gy γ-ray values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a reducing the root mean square error (RMSE of the radiation sensitivity prediction model from 0.20 to 0.011; and (b improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.

  4. Evaluation of atlas-based auto-segmentation software in prostate cancer patients

    International Nuclear Information System (INIS)

    The performance and limitations of an atlas-based auto-segmentation software package (ABAS; Elekta Inc.) was evaluated using male pelvic anatomy as the area of interest. Contours from 10 prostate patients were selected to create atlases in ABAS. The contoured regions of interest were created manually to align with published guidelines and included the prostate, bladder, rectum, femoral heads and external patient contour. Twenty-four clinically treated prostate patients were auto-contoured using a randomised selection of two, four, six, eight or ten atlases. The concordance between the manually drawn and computer-generated contours were evaluated statistically using Pearson's product–moment correlation coefficient (r) and clinically in a validated qualitative evaluation. In the latter evaluation, six radiation therapists classified the degree of agreement for each structure using seven clinically appropriate categories. The ABAS software generated clinically acceptable contours for the bladder, rectum, femoral heads and external patient contour. For these structures, ABAS-generated volumes were highly correlated with ‘as treated’ volumes, manually drawn; for four atlases, for example, bladder r = 0.988 (P < 0.001), rectum r = 0.739 (P < 0.001) and left femoral head r = 0.560 (P < 0.001). Poorest results were seen for the prostate (r = 0.401, P < 0.05) (four atlases); however this was attributed to the comparison prostate volume being contoured on magnetic resonance imaging (MRI) rather than computed tomography (CT) data. For all structures, increasing the number of atlases did not consistently improve accuracy. ABAS-generated contours are clinically useful for a range of structures in the male pelvis. Clinically appropriate volumes were created, but editing of some contours was inevitably required. The ideal number of atlases to improve generated automatic contours is yet to be determined

  5. Part III: Comparing observed growth of selected test organisms in food irradiation studies with growth predictions calculated by ComBase softwares

    International Nuclear Information System (INIS)

    As a result of intensive predictive microbiological modelling activities, several computer programs and softwares became available recently for facilitating microbiological risk assessment. Among these tools, the establishment of the ComBase, an international database and its predictive modelling softwares of the Pathogen Modelling Program (PMP) set up by the USDA Eastern Regional Research Center, Wyndmore, PA, and the Food Micromodel/Growth Predictor by the United Kingdom's Institute of Food Research, Norwich, are most important. The authors have used the PMP 6.1 software version of ComBase as a preliminary trial to compare observed growth of selected test organisms in relation to their food irradiation work during recent years within the FAO/IAEA Coordinated Food Irradiation Research Projects (D6.10.23 and D6.20.07) with the predicted growth on the basis of growth models available in ComBase for the same species as those of the authors' test organisms. The results of challenge tests with Listeria monocytogenes inoculum in untreated or irradiated experimental batches of semi-prepared breaded turkey meat steaks (cordon bleu), sliced tomato, sliced watermelon, sliced cantaloupe and sous vide processed mixed vegetables, as well as Staphylococcus aureus inoculum of a pasta product, tortellini, were compared with their respective growth models under relevant environmental conditions. This comparison showed good fits in the case of non-irradiated and high moisture food samples, but growth of radiation survivors lagged behind the predicted values. (author)

  6. Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model.

    Directory of Open Access Journals (Sweden)

    Georgina Cosma

    Full Text Available The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA level, the biopsy most common tumor pattern (Primary Gleason pattern and the second most common tumor pattern (Secondary Gleason pattern in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD or Extra-Prostatic Disease (ED using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC, with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812. The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR

  7. Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model

    Science.gov (United States)

    Acampora, Giovanni; Brown, David; Rees, Robert C.

    2016-01-01

    The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR

  8. Treatment of platinum-resistant recurrent ovarian cancer using a "predictive molecule targeted routine chemotherapy" system

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xiao-dong; WEI Feng-hua; ZHANG Yi; HE Shu-rong; YANG Li

    2009-01-01

    Background Correct drug selection, the key to successful chemotherapy, is one of the most difficult clinical decisions for the treatment of platinum-resistant recurrent ovarian cancer worldwide. The exact procedures for choosing drugs are undefined, currently relying on clinical trials and personal experience, which often results in disappointing outcomes. Here, we propose a new drug selection method, the "predictive molecule targeted routine chemotherapy", to choose relatively sensitive routine drugs and avoid relatively resistant routine drugs based on the specific predictive molecule expression of the individual tumor tissue.Methods From January 2004 to June 2008,26 cases of platinum-resistant recurrent ovarian cancer were prospectively recruited. Their routine chemotherapy drug choice was based on the expression of 6 predictive molecules (including p53) as determined by immunohistochemistry (the predictive molecule targeted routine chemotherapy group). A further 18 cases of platinum-resistant recurrent ovarian cancer were treated by experience and formed the control group. The response rate and the overall survival were compared between the two groups.Results The response rate to second-line chemotherapy was 28% in the control group and 77% in the predictive molecule targeted routine chemotherapy group (P=0.002). The response rate to third-line chemotherapy was 14% in the control group and 33% in the predictive molecule targeted routine chemotherapy group (P=0.268). The median overall survival of the predictive molecule targeted routine chemotherapy group (88 weeks) was significantly longer than the median overall survival of the control group (56 weeks) (P=0.0315).Conclusion The predictive molecule targeted routine chemotherapy is a new effective protocol for choosing drugs when treating platinum-resistant recurrent ovarian cancer.

  9. Software Cost Estimation Review

    OpenAIRE

    Ongere, Alphonce

    2013-01-01

    Software cost estimation is the process of predicting the effort, the time and the cost re-quired to complete software project successfully. It involves size measurement of the soft-ware project to be produced, estimating and allocating the effort, drawing the project schedules, and finally, estimating overall cost of the project. Accurate estimation of software project cost is an important factor for business and the welfare of software organization in general. If cost and effort estimat...

  10. miR-21 Expression in Cancer Cells may Not Predict Resistance to Adjuvant Trastuzumab in Primary Breast Cancer

    DEFF Research Database (Denmark)

    Nielsen, Boye Schnack; Balslev, Eva; Poulsen, Tim Svenstrup;

    2014-01-01

    Trastuzumab is established as standard care for patients with HER2-positive breast cancer both in the adjuvant and metastatic setting. However, 50% of the patients do not respond to the trastuzumab therapy, and therefore new predictive biomarkers are highly warranted. MicroRNAs (miRs) constitute...... a new group of biomarkers and their cellular expression can be determined in tumor samples by in situ hybridization (ISH) analysis. miR-21 is highly prevalent and up-regulated in breast cancer and has been linked to drug resistance in clinical and in vitro settings. To determine expression patterns...... of miR-21 in high-grade breast cancers, we examined miR-21 expression in 22 HER2-positive tumors and 15 HER2-negative high-grade tumors by ISH. The histological examination indicated that patient samples could be divided into three major expression patterns: miR-21 predominantly in tumor stroma...

  11. Stathmin protein level, a potential predictive marker for taxane treatment response in endometrial cancer.

    Directory of Open Access Journals (Sweden)

    Henrica M J Werner

    Full Text Available Stathmin is a prognostic marker in many cancers, including endometrial cancer. Preclinical studies, predominantly in breast cancer, have suggested that stathmin may additionally be a predictive marker for response to paclitaxel. We first evaluated the response to paclitaxel in endometrial cancer cell lines before and after stathmin knock-down. Subsequently we investigated the clinical response to paclitaxel containing chemotherapy in metastatic endometrial cancer in relation to stathmin protein level in tumors. Stathmin level was also determined in metastatic lesions, analyzing changes in biomarker status on disease progression. Knock-down of stathmin improved sensitivity to paclitaxel in endometrial carcinoma cell lines with both naturally higher and lower sensitivity to paclitaxel. In clinical samples, high stathmin level was demonstrated to be associated with poor response to paclitaxel containing chemotherapy and to reduced disease specific survival only in patients treated with such combination. Stathmin level increased significantly from primary to metastatic lesions. This study suggests, supported by both preclinical and clinical data, that stathmin could be a predictive biomarker for response to paclitaxel treatment in endometrial cancer. Re-assessment of stathmin level in metastatic lesions prior to treatment start may be relevant. Also, validation in a randomized clinical trial will be important.

  12. Immunohistochemistry for myc predicts survival in colorectal cancer.

    Directory of Open Access Journals (Sweden)

    Christopher W Toon

    Full Text Available MYC over-expression as determined by molecular means has been reported as a favorable prognostic biomarker in colorectal carcinoma (CRC. However MYC expression analysis is not available in the routine clinical setting. We investigated whether immunohistochemistry (IHC for the myc protein using a novel commercially available rabbit monoclonal antibody [clone Y69] which is currently in widespread clinical use for lymphoma diagnosis could be used to predict outcome in resected CRC. Myc IHC was performed on a tissue microarray (TMA comprising a retrospective cohort of 1421 CRC patients and scored blinded as to all clinical and pathological data. IHC was also performed on a subcohort of whole section CRCs to assess staining characteristics and concordance with TMA expression. MYC over-expression was found in 980 (69% of CRCs and was associated with tumor stage and DNA mismatch repair/BRAF status. There was substantial agreement between TMA and whole section myc IHC (kappa = 0.742, p<0.01. CRCs with MYC over-expression demonstrated improved 5-year survival (93.2% vs. 57.3%, with the effect significantly modulated by the dominant effect of tumor stage, age at diagnosis and lymphovascular space invasion status on survival. We conclude that myc status as determined by IHC alone can be used to predict overall survival in patients with CRC undergoing surgical resection.

  13. Immunohistochemistry for myc predicts survival in colorectal cancer.

    Science.gov (United States)

    Toon, Christopher W; Chou, Angela; Clarkson, Adele; DeSilva, Keshani; Houang, Michelle; Chan, Joseph C Y; Sioson, Loretta L; Jankova, Lucy; Gill, Anthony J

    2014-01-01

    MYC over-expression as determined by molecular means has been reported as a favorable prognostic biomarker in colorectal carcinoma (CRC). However MYC expression analysis is not available in the routine clinical setting. We investigated whether immunohistochemistry (IHC) for the myc protein using a novel commercially available rabbit monoclonal antibody [clone Y69] which is currently in widespread clinical use for lymphoma diagnosis could be used to predict outcome in resected CRC. Myc IHC was performed on a tissue microarray (TMA) comprising a retrospective cohort of 1421 CRC patients and scored blinded as to all clinical and pathological data. IHC was also performed on a subcohort of whole section CRCs to assess staining characteristics and concordance with TMA expression. MYC over-expression was found in 980 (69%) of CRCs and was associated with tumor stage and DNA mismatch repair/BRAF status. There was substantial agreement between TMA and whole section myc IHC (kappa = 0.742, pspace invasion status on survival. We conclude that myc status as determined by IHC alone can be used to predict overall survival in patients with CRC undergoing surgical resection. PMID:24503701

  14. Towards virtual surgery in oral cancer to predict postoperative oral functions preoperatively

    NARCIS (Netherlands)

    Alphen, M.J.A.; Kreeft, A.M.; Heijden, van der F.; Smeele, L.E.; Balm, A.J.M.

    2013-01-01

    Our aim was to develop a dynamic virtual model of the oral cavity and oropharynx so that we could incorporate patient-specific factors into the prediction of functional loss after advanced resections for oral cancer. After a virtual resection, functional consequences can be assessed, and a more subs

  15. The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse

    NARCIS (Netherlands)

    Merlos-Suarez, A.; Barriga, F.M.; Jung, P.; Iglesias, M.; Cespedes, M.V.; Rossell, D.; Sevillano, M.; Hernando-Momblona, X.; da Silva-Diz, V.; Munoz, P.; Clevers, H.; Sancho, E.; Mangues, R.; Batlle, E.

    2011-01-01

    A frequent complication in colorectal cancer (CRC) is regeneration of the tumor after therapy. Here, we report that a gene signature specific for adult intestinal stem cells (ISCs) predicts disease relapse in CRC patients. ISCs are marked by high expression of the EphB2 receptor, which becomes gradu

  16. Conventional CT for the prediction of an involved circumferential resection margin in primary rectal cancer

    NARCIS (Netherlands)

    Wolberink, Steven V. R. C.; Beets-Tan, Regina G. H.; de Haas-Kock, Danielle F. M.; Span, Mark M.; van de Jagt, Eric J.; van de Velde, Cornelis J. H.; Wiggers, Theo

    2007-01-01

    Purpose: To determine the accuracy of conventional computed tomography (CT) scan in the preoperative prediction of an involved circumferential resection margin (CRM) in primary rectal cancer. Methods: 125 patients with biopsy-proven adenocarcinoma of the rectum underwent CT of the abdomen before und

  17. An epidemiologic risk prediction model for ovarian cancer in Europe : The EPIC study

    NARCIS (Netherlands)

    Li, K.; Huesing, A.; Fortner, R. T.; Tjonneland, A.; Hansen, L.; Dossus, L.; Chang-Claude, J.; Bergmann, M.; Steffen, A.; Bamia, C.; Trichopoulos, D.; Trichopoulou, A.; Palli, D.; Mattiello, A.; Agnoli, C.; Tumino, R.; Onland-Moret, N. C.; Peeters, P. H.; Bueno-de-Mesquita, H. B(as); Gram, I. T.; Weiderpass, E.; Sanchez-Cantalejo, E.; Chirlaque, M-D; Duell, E. J.; Ardanaz, E.; Idahl, A.; Lundin, E.; Khaw, K-T; Travis, R. C.; Merritt, M. A.; Gunter, M. J.; Riboli, E.; Ferrari, P.; Terry, K.; Cramer, D.; Kaaks, R.

    2015-01-01

    Background: Ovarian cancer has a high case-fatality ratio, largely due to late diagnosis. Epidemiologic risk prediction models could help identify women at increased risk who may benefit from targeted prevention measures, such as screening or chemopreventive agents. Methods: We built an ovarian canc

  18. Prediction of "BRCAness" in breast cancer by array comparative genomic hybridization

    NARCIS (Netherlands)

    Joosse, Simon Andreas

    2012-01-01

    Predicting the likelihood that an individual is a BRCA mutation carrier is the first step to genetic counseling, followed by germ-line mutation testing in many family cancer clinics. Individuals who have been diagnosed as BRCA mutation-positive are offered special medical care; however, clinical man

  19. International multicenter tool to predict the risk of nonsentinel node metastases in breast cancer

    DEFF Research Database (Denmark)

    Meretoja, Tuomo J; Leidenius, Marjut H K; Heikkilä, Päivi S;

    2012-01-01

    Background Axillary treatment of breast cancer patients is undergoing a paradigm shift, as completion axillary lymph node dissections (ALNDs) are being questioned in the treatment of patients with tumor-positive sentinel nodes. This study aims to develop a novel multi-institutional predictive too...

  20. Paper Highlight: Biomarker Identified for Predicting Early Prostate Cancer Aggressiveness — Site

    Science.gov (United States)

    A team led by Cory Abate-Shen, Michael Shen, and Andrea Califano at Columbia University found that measuring the expression levels of three genes associated with aging can be used to predict the aggressiveness of seemingly low-risk prostate cancer.

  1. Circulating microRNAs as Prognostic and Predictive Biomarkers in Patients with Colorectal Cancer

    Directory of Open Access Journals (Sweden)

    Jakob Vasehus Schou

    2016-06-01

    Full Text Available MiRNAs are suggested as promising cancer biomarkers. They are stable and extractable from a variety of clinical tissue specimens (fresh frozen or formalin fixed paraffin embedded tissue and a variety of body fluids (e.g., blood, urine, saliva. However, there are several challenges that need to be solved, considering their potential as biomarkers in cancer, such as lack of consistency between biomarker panels in independent studies due to lack of standardized sample handling and processing, use of inconsistent normalization approaches, and differences in patients populations. Focusing on colorectal cancer (CRC, divergent results regarding circulating miRNAs as prognostic or predictive biomarkers are reported in the literature. In the present review, we summarize the current data on circulating miRNAs as prognostic/predictive biomarkers in patients with localized and metastatic CRC (mCRC.

  2. Evaluation of candidate biomarkers to predict cancer cell sensitivity or resistance to PARP-1 inhibitor treatment

    DEFF Research Database (Denmark)

    Oplustilova, L.; Wolanin, K.; Bartkova, J.;

    2012-01-01

    to PARP-1i. Here we addressed these issues using PARP-1i on 20 human cell lines from carcinomas of the breast, prostate, colon, pancreas and ovary. Aberrations of the Mre11-Rad50-Nbs1 (MRN) complex sensitized cancer cells to PARP-1i, while p53 status was less predictive, even in response to PARP-1i......Impaired DNA damage response pathways may create vulnerabilities of cancer cells that can be exploited therapeutically. One such selective vulnerability is the sensitivity of BRCA1- or BRCA2-defective tumors (hence defective in DNA repair by homologous recombination, HR) to inhibitors of the poly......(ADp-ribose) polymerase-1 (PARP-1), an enzyme critical for repair pathways alternative to HR. While promising, treatment with PARP-1 inhibitors (PARP-1i) faces some hurdles, including (1) acquired resistance, (2) search for other sensitizing, non-BRCA1/2 cancer defects and (3) lack of biomarkers to predict response...

  3. Decorin in human oral cancer: A promising predictive biomarker of S-1 neoadjuvant chemosensitivity

    Energy Technology Data Exchange (ETDEWEB)

    Kasamatsu, Atsushi, E-mail: kasamatsua@faculty.chiba-u.jp [Department of Oral Science, Graduate School of Medicine, Chiba University, Chiba 260-8670 (Japan); Department of Dentistry and Oral–Maxillofacial Surgery, Chiba University Hospital, Chiba 260-8670 (Japan); Uzawa, Katsuhiro, E-mail: uzawak@faculty.chiba-u.jp [Department of Oral Science, Graduate School of Medicine, Chiba University, Chiba 260-8670 (Japan); Department of Dentistry and Oral–Maxillofacial Surgery, Chiba University Hospital, Chiba 260-8670 (Japan); Minakawa, Yasuyuki; Ishige, Shunsaku; Kasama, Hiroki [Department of Oral Science, Graduate School of Medicine, Chiba University, Chiba 260-8670 (Japan); Endo-Sakamoto, Yosuke; Ogawara, Katsunori [Department of Dentistry and Oral–Maxillofacial Surgery, Chiba University Hospital, Chiba 260-8670 (Japan); Shiiba, Masashi; Takiguchi, Yuichi [Medical Oncology, Graduate School of Medicine, Chiba University, Chiba 260-8670 (Japan); Tanzawa, Hideki [Department of Oral Science, Graduate School of Medicine, Chiba University, Chiba 260-8670 (Japan); Department of Dentistry and Oral–Maxillofacial Surgery, Chiba University Hospital, Chiba 260-8670 (Japan)

    2015-01-30

    Highlights: • DCN is significantly up-regulated in chemoresistant cancer cell lines. • DCN is a key regulator for chemoresistant mechanisms in vitro and in vivo. • DCN predicts the clinical responses to S-1 NAC for patients with oral cancer. - Abstract: We reported previously that decorin (DCN) is significantly up-regulated in chemoresistant cancer cell lines. DCN is a small leucine-rich proteoglycan that exists and functions in stromal and epithelial cells. Accumulating evidence suggests that DCN affects the biology of several types of cancer by directly/indirectly targeting the signaling molecules involved in cell growth, survival, metastasis, and angiogenesis, however, the molecular mechanisms of DCN in chemoresistance and its clinical relevance are still unknown. Here we assumed that DCN silencing cells increase chemosusceptibility to S-1, consisted of tegafur, prodrug of 5-fluorouracil. We first established DCN knockdown transfectants derived from oral cancer cells for following experiments including chemosusceptibility assay to S-1. In addition to the in vitro data, DCN knockdown zenografting tumors in nude mice demonstrate decreasing cell proliferation and increasing apoptosis with dephosphorylation of AKT after S-1 chemotherapy. We also investigated whether DCN expression predicts the clinical responses of neoadjuvant chemotherapy (NAC) using S-1 (S-1 NAC) for oral cancer patients. Immunohistochemistry data in the preoperative biopsy samples was analyzed to determine the cut-off point for status of DCN expression by receiver operating curve analysis. Interestingly, low DCN expression was observed in five (83%) of six cases with complete responses to S-1 NAC, and in one (10%) case of 10 cases with stable/progressive disease, indicating that S-1 chemosensitivity is dramatically effective in oral cancer patients with low DCN expression compared with high DCN expression. Our findings suggest that DCN is a key regulator for chemoresistant mechanisms, and

  4. Decorin in human oral cancer: A promising predictive biomarker of S-1 neoadjuvant chemosensitivity

    International Nuclear Information System (INIS)

    Highlights: • DCN is significantly up-regulated in chemoresistant cancer cell lines. • DCN is a key regulator for chemoresistant mechanisms in vitro and in vivo. • DCN predicts the clinical responses to S-1 NAC for patients with oral cancer. - Abstract: We reported previously that decorin (DCN) is significantly up-regulated in chemoresistant cancer cell lines. DCN is a small leucine-rich proteoglycan that exists and functions in stromal and epithelial cells. Accumulating evidence suggests that DCN affects the biology of several types of cancer by directly/indirectly targeting the signaling molecules involved in cell growth, survival, metastasis, and angiogenesis, however, the molecular mechanisms of DCN in chemoresistance and its clinical relevance are still unknown. Here we assumed that DCN silencing cells increase chemosusceptibility to S-1, consisted of tegafur, prodrug of 5-fluorouracil. We first established DCN knockdown transfectants derived from oral cancer cells for following experiments including chemosusceptibility assay to S-1. In addition to the in vitro data, DCN knockdown zenografting tumors in nude mice demonstrate decreasing cell proliferation and increasing apoptosis with dephosphorylation of AKT after S-1 chemotherapy. We also investigated whether DCN expression predicts the clinical responses of neoadjuvant chemotherapy (NAC) using S-1 (S-1 NAC) for oral cancer patients. Immunohistochemistry data in the preoperative biopsy samples was analyzed to determine the cut-off point for status of DCN expression by receiver operating curve analysis. Interestingly, low DCN expression was observed in five (83%) of six cases with complete responses to S-1 NAC, and in one (10%) case of 10 cases with stable/progressive disease, indicating that S-1 chemosensitivity is dramatically effective in oral cancer patients with low DCN expression compared with high DCN expression. Our findings suggest that DCN is a key regulator for chemoresistant mechanisms, and

  5. Molecular biomarkers of colorectal cancer: prognostic and predictive tools for clinical practice

    Institute of Scientific and Technical Information of China (English)

    Wei-qin JIANG; Fang-fang FU; Yang-xia LI; Wei-bin WANG; Hao-hao WANG; Hai-ping JIANG; Li-song TENG

    2012-01-01

    Colorectal cancer remains one of the most common types of cancer and leading causes of cancer death worldwide.Although we have made steady progress in chemotherapy and targeted therapy,evidence suggests that the majority of patients undergoing drug therapy experience severe,debilitating,and even lethal adverse drug events which considerably outweigh the benefits.The identification of suitable biomarkers will allow clinicians to deliver the most appropriate drugs to specific patients and spare them ineffective and expensive treatments.Prognostic and predictive biomarkers have been the subjects of many published papers,but few have been widely incorporated into clinical practice.Here,we want to review recent biomarker data related to colorectal cancer,which may have been ready for clinical use.

  6. Predicting Mean Survival Time from Reported Median Survival Time for Cancer Patients

    DEFF Research Database (Denmark)

    Lousdal, Mette L; Kristiansen, Ivar S; Møller, Bjørn;

    2016-01-01

    BACKGROUND: Mean duration of survival following treatment is a prerequisite for cost-effectiveness analyses used for assessing new and costly life-extending therapies for cancer patients. Mean survival time is rarely reported due to censoring imposed by limited follow-up time, whereas the median...... survival time often is. The empirical relationship between mean and median survival time for cancer patients is not known. AIM: To derive the empirical associations between mean and median survival time across cancer types and to validate this empirical prediction approach and compare it with the standard...... approach of fitting a Weibull distribution. METHODS: We included all patients in Norway diagnosed from 1960 to 1999 with one of the 13 most common solid tumor cancers until emigration, death, or 31 December 2011, whichever came first. Observed median, restricted mean, and mean survival times were obtained...

  7. Cervical cancer survival prediction using hybrid of SMOTE, CART and smooth support vector machine

    Science.gov (United States)

    Purnami, S. W.; Khasanah, P. M.; Sumartini, S. H.; Chosuvivatwong, V.; Sriplung, H.

    2016-04-01

    According to the WHO, every two minutes there is one patient who died from cervical cancer. The high mortality rate is due to the lack of awareness of women for early detection. There are several factors that supposedly influence the survival of cervical cancer patients, including age, anemia status, stage, type of treatment, complications and secondary disease. This study wants to classify/predict cervical cancer survival based on those factors. Various classifications methods: classification and regression tree (CART), smooth support vector machine (SSVM), three order spline SSVM (TSSVM) were used. Since the data of cervical cancer are imbalanced, synthetic minority oversampling technique (SMOTE) is used for handling imbalanced dataset. Performances of these methods are evaluated using accuracy, sensitivity and specificity. Results of this study show that balancing data using SMOTE as preprocessing can improve performance of classification. The SMOTE-SSVM method provided better result than SMOTE-TSSVM and SMOTE-CART.

  8. Predictive and prognostic effect of CD133 and cancer-testis antigens in stage Ib-IIIA non-small cell lung cancer

    OpenAIRE

    SU, CHUNXIA; Xu, Ying; Xuefei LI; Ren, Shengxiang; Zhao, Chao; Hou, Likun; Ye, Zhiwei; Zhou, Caicun

    2015-01-01

    CD133 and cancer-testis antigens (CTAs) may be potential predicted markers of adjuvant chemotherapy or immune therapy, and they may be the independent prognostic factor of NSCLC. Nowadays, there is still no predictive biomarker identified for the use of adjuvant chemotherapy in non-small cell lung cancer (NSCLC) patients. To clarify the role of CD133 and CTAs as a predictive marker for adjuvant chemotherapy or prognostic factors of overall survival, we performed a retrospective study in 159 s...

  9. Predicting multi-class responses to preoperative chemoradiotherapy in rectal cancer patients

    International Nuclear Information System (INIS)

    Preoperative chemoradiotherapy (CRT) has become a widely used treatment for improving local control of disease and increasing survival rates of rectal cancer patients. We aimed to identify a set of genes that can be used to predict responses to CRT in patients with rectal cancer. Gene expression profiles of pre-therapeutic biopsy specimens obtained from 77 rectal cancer patients were analyzed using DNA microarrays. The response to CRT was determined using the Dworak tumor regression grade: grade 1 (minimal, MI), grade 2 (moderate, MO), grade 3 (near total, NT), or grade 4 (total, TO). Top ranked genes for three different feature scores such as a p-value (pval), a rank product (rank), and a normalized product (norm) were selected to distinguish pre-defined groups such as complete responders (TO) from the MI, MO, and NT groups. Among five different classification algorithms, supporting vector machine (SVM) with the top 65 norm features performed at the highest accuracy for predicting MI using a 5-fold cross validation strategy. On the other hand, 98 pval features were selected for predicting TO by elastic net (EN). Finally we combined TO- and MI-finder models to build a three-class classification model and validated it using an independent dataset of rectal cancer mRNA expression. We identified MI- and TO-finders for predicting preoperative CRT responses, and validated these data using an independent public dataset. This stepwise prediction model requires further evaluation in clinical studies in order to develop personalized preoperative CRT in patients with rectal cancer. The online version of this article (doi:10.1186/s13014-016-0623-9) contains supplementary material, which is available to authorized users

  10. Assessment of uncertainties in radiation-induced cancer risk predictions at clinically relevant doses

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, J. [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg 69117 (Germany); Moteabbed, M.; Paganetti, H., E-mail: hpaganetti@mgh.harvard.edu [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114 and Harvard Medical School, Boston, Massachusetts 02114 (United States)

    2015-01-15

    Purpose: Theoretical dose–response models offer the possibility to assess second cancer induction risks after external beam therapy. The parameters used in these models are determined with limited data from epidemiological studies. Risk estimations are thus associated with considerable uncertainties. This study aims at illustrating uncertainties when predicting the risk for organ-specific second cancers in the primary radiation field illustrated by choosing selected treatment plans for brain cancer patients. Methods: A widely used risk model was considered in this study. The uncertainties of the model parameters were estimated with reported data of second cancer incidences for various organs. Standard error propagation was then subsequently applied to assess the uncertainty in the risk model. Next, second cancer risks of five pediatric patients treated for cancer in the head and neck regions were calculated. For each case, treatment plans for proton and photon therapy were designed to estimate the uncertainties (a) in the lifetime attributable risk (LAR) for a given treatment modality and (b) when comparing risks of two different treatment modalities. Results: Uncertainties in excess of 100% of the risk were found for almost all organs considered. When applied to treatment plans, the calculated LAR values have uncertainties of the same magnitude. A comparison between cancer risks of different treatment modalities, however, does allow statistically significant conclusions. In the studied cases, the patient averaged LAR ratio of proton and photon treatments was 0.35, 0.56, and 0.59 for brain carcinoma, brain sarcoma, and bone sarcoma, respectively. Their corresponding uncertainties were estimated to be potentially below 5%, depending on uncertainties in dosimetry. Conclusions: The uncertainty in the dose–response curve in cancer risk models makes it currently impractical to predict the risk for an individual external beam treatment. On the other hand, the ratio

  11. Convergence of mutation and epigenetic alterations identifies common genes in cancer that predict for poor prognosis.

    Directory of Open Access Journals (Sweden)

    Timothy A Chan

    2008-05-01

    -wide approach, our analysis has enabled the discovery of a number of clinically significant genes targeted by multiple modes of inactivation in breast and colon cancer. Importantly, we demonstrate that a subset of these genes predict strongly for poor clinical outcome. Our data define a set of genes that are targeted by both genetic and epigenetic events, predict for clinical prognosis, and are likely fundamentally important for cancer initiation or progression.

  12. Does Metastatic Lymph Node SUVmax Predict Survival in Patients with Esophageal Cancer?

    Directory of Open Access Journals (Sweden)

    Betül Vatankulu

    2015-10-01

    Full Text Available Objective: We aimed to investigate the SUVmax of primary tumor and metastatic lymph node in predicting survival in patients with esophageal cancer. Methods: We retrospectively analyzed patients with esophageal cancer between 2009 and 2011 who had FDG positronemission tomography (PET/computed tomography (CT. All patients were followed-up to 2013. Clinical staging, SUVmax of primary tumor and metastatic lymph node were evaluated. Results: One hundred seven patients were included in the study. All patients were followed-up between 2 and 49 months. The mean SUVmax of primary tumor and metastatic lymph node were 19.3±8.8 and 10.4±9.1, respectively. Metastatic lymph node SUVmax had an effect in predicting survival whereas primary tumor SUVmax did not have an effect (p=0.014 and p=0.262, respectively. Multivariate Cox regression analysis showed that clinical stage of the disease was the only independent factor predicting survival (p=0.001. Conclusion: Among patients with esophageal cancer, the value of primary tumor SUVmax did not have an effect on survival. Clinical stage assessed with FDG PET/CT imaging was found to predict survival in esophageal carcinoma. Additionally, lymph node SUVmax was identified as a new parameter in predicting survival in the present study

  13. Thyroid cancer in children and adolescents of Belarus irradiated as a result of Chernobyl accident: status and prediction

    International Nuclear Information System (INIS)

    Thyroid cancer incidence in the human population of Belarus irradiated in childhood for the period passed after the Chernobyl accident is analysed and potential perspectives for development of disease incidence in exposed population during life span. Thyroid cancer cases in children and adolescents of Belarus irradiated due to the Chernobyl accident are predicted using the additive model with modified parameters. Predicted values are shown to be in good agreement with the actual data on thyroid cancer cases in children aged 0-6

  14. DEVELOPMENT OF THE NOMOGRAM THAT PREDICTS PATHOLOGICAL LYMPH NODE INVOLVEMENT IN BLADDER CANCER PATIENTS BASED ON CLINICAL VARIABLES

    OpenAIRE

    L. V. Mirylenko; O. G. Sukonko; A. V. Pravorov; A. I. Rolevich; A. S. Mavrichev

    2014-01-01

    Objective: to develop nomogram based on clinical variables, that predicts pathological lymph node involvement (рN+) in bladder cancer patients.Material and methods: We used data of 511 patients with bladder cancer, that have undergone radical cystectomy between 1999 and 2008 at N.N. Alexandrov National Cancer Centre. Mono- and multivariate logistic regression analyses were used for pN+ prediction on preoperative data. Coefficients from logistic regression equation were used to construct the n...

  15. An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction.

    Science.gov (United States)

    Chen, Xi; Wang, Lily; Ishwaran, Hemant

    2010-09-01

    Prediction models that use gene expression levels are now being proposed for personalized treatment of cancer, but building accurate models that are easy to interpret remains a challenge. In this paper, we describe an integrative clinical-genomic approach that combines both genomic pathway and clinical information. First, we summarize information from genes in each pathway using Supervised Principal Components (SPCA) to obtain pathway-based genomic predictors. Next, we build a prediction model based on clinical variables and pathway-based genomic predictors using Random Survival Forests (RSF). Our rationale for this two-stage procedure is that the underlying disease process may be influenced by environmental exposure (measured by clinical variables) and perturbations in different pathways (measured by pathway-based genomic variables), as well as their interactions. Using two cancer microarray datasets, we show that the pathway-based clinical-genomic model outperforms gene-based clinical-genomic models, with improved prediction accuracy and interpretability.

  16. 代价敏感分类的软件缺陷预测方法%Using Cost-Sensitive Classification for Software Defects Prediction

    Institute of Scientific and Technical Information of China (English)

    李勇; 黄志球; 房丙午; 王勇

    2014-01-01

    软件缺陷预测是提高软件测试效率,保证软件可靠性的重要途径。考虑到软件缺陷预测模型对软件模块错误分类代价的不同,提出了代价敏感分类的软件缺陷预测模型构建方法。针对代码属性度量数据,采用Bagging方式有放回地多次随机抽取训练样本来构建代价敏感分类的决策树基分类器,然后通过投票的方式集成后进行软件模块的缺陷预测,并给出模型构建过程中代价因子最优值的判定选择方法。使用公开的NASA软件缺陷预测数据集进行仿真实验,结果表明该方法在保证缺陷预测率的前提下,误报率明显降低,综合评价指标AUC和F值均优于现有方法。%Software defects prediction is considered as an effective means for the optimization of quality assurance activities. Taking into account the different misclassification cost for unknown software modules using the software defects prediction models, this paper proposes the cost-sensitive classification method for constructing software defects prediction models. Firstly, for the code attribute metric data, decision tree algorithm is selected to construct base-classifiers using cost-sensitive classification method by sampling with replacement of Bagging. Then, the defects prediction model is constructed based on majority rule, and the approach to obtain the approximate optimal cost-factor value is researched. The experimental results on the NASA software defects prediction datasets show that the proposed method is averagely superior to the conventional methods with lower probability of false alarm and higher compre-hensive evaluation values.

  17. Neoadjuvant chemotherapy induces expression levels of breast cancer resistance protein that predict disease-free survival in breast cancer.

    Directory of Open Access Journals (Sweden)

    Baek Kim

    Full Text Available Three main xenobiotic efflux pumps have been implicated in modulating breast cancer chemotherapy responses. These are P-glycoprotein (Pgp, Multidrug Resistance-associated Protein 1 (MRP1, and Breast Cancer Resistance Protein (BCRP. We investigated expression of these proteins in breast cancers before and after neoadjuvant chemotherapy (NAC to determine whether their levels define response to NAC or subsequent survival. Formalin-fixed paraffin-embedded tissues were collected representing matched pairs of core biopsy (pre-NAC and surgical specimen (post-NAC from 45 patients with invasive ductal carcinomas. NAC regimes were anthracyclines +/- taxanes. Immunohistochemistry was performed for Pgp, MRP1 and BCRP and expression was quantified objectively using computer-aided scoring. Pgp and MRP1 were significantly up-regulated after exposure to NAC (Wilcoxon signed-rank p = 0.0024 and p<0.0001, while BCRP showed more variation in response to NAC, with frequent up- (59% of cases and down-regulation (41% contributing to a lack of significant difference overall. Pre-NAC expression of all markers, and post-NAC expression of Pgp and MRP1 did not correlate with NAC response or with disease-free survival (DFS. Post-NAC expression of BCRP did not correlate with NAC response, but correlated significantly with DFS (Log rank p = 0.007, with longer DFS in patients with low post-NAC BCRP expression. In multivariate Cox regression analyses, post-NAC BCRP expression levels proved to predict DFS independently of standard prognostic factors, with high expression associated with a hazard ratio of 4.04 (95% confidence interval 1.3-12.2; p = 0.013. We conclude that NAC-induced expression levels of BCRP predict survival after NAC for breast cancer, while Pgp and MRP1 expression have little predictive value.

  18. APEX (Aqueous Photochemistry of Environmentally occurring Xenobiotics): a free software tool to predict the kinetics of photochemical processes in surface waters.

    Science.gov (United States)

    Bodrato, Marco; Vione, Davide

    2014-04-01

    The APEX software predicts the photochemical transformation kinetics of xenobiotics in surface waters as a function of: photoreactivity parameters (direct photolysis quantum yield and second-order reaction rate constants with transient species, namely ˙OH, CO₃(-)˙, (1)O₂ and the triplet states of chromophoric dissolved organic matter, (3)CDOM*), water chemistry (nitrate, nitrite, bicarbonate, carbonate, bromide and dissolved organic carbon, DOC), and water depth (more specifically, the optical path length of sunlight in water). It applies to well-mixed surface water layers, including the epilimnion of stratified lakes, and the output data are average values over the considered water column. Based on intermediate formation yields from the parent compound via the different photochemical pathways, the software can also predict intermediate formation kinetics and overall yield. APEX is based on a photochemical model that has been validated against available field data of pollutant phototransformation, with good agreement between model predictions and field results. The APEX software makes allowance for different levels of knowledge of a photochemical system. For instance, the absorption spectrum of surface water can be used if known, or otherwise it can be modelled from the values of DOC. Also the direct photolysis quantum yield can be entered as a detailed wavelength trend, as a single value (constant or average), or it can be defined as a variable if unknown. APEX is based on the free software Octave. Additional applications are provided within APEX to assess the σ-level uncertainty of the results and the seasonal trend of photochemical processes.

  19. Peritoneal lavage cytology and carcinoembryonic antigen determination in predicting peritoneal metastasis and prognosis of gastric cancer

    Institute of Scientific and Technical Information of China (English)

    Ji-Kun Li; Miao Zheng; Chuan-Wen Miao; Jian-Hai Zhang; Guang-Han Ding; Wen-Shen Wu

    2005-01-01

    AIM: To evaluate the role of peritoneal lavage cytology (PLC) and carcinoembryonic antigen (CEA) determination of peritoneal washes (pCEA) in predicting the peritoneal metastasis and prognosis after curative resection of gastric cancer.METHODS: PLC and radioimmunoassay of CEA were performed in peritoneal washes from 64 patients with gastric cancer and 8 patients with benign diseases.RESULTS: The positive rate of pCEA (40.6%) was significantly higher than that of PLC (23.4%) (P<0.05).The positive rates of PLC and pCEA correlated with the depth of tumor invasion and lymph node metastasis (P<0.05). pCEA was found to have a higher sensitivity and a lower false-positive rate in predicting peritoneal metastasis after curative resection of gastric cancer as compared to PLC. The 1-, 3-, and 5-year survival rates of patients with positive cytologic findings or positive pCEA results were significantly lower than those of patients with negative cytologic findings or negative pCEA results (P<0.05). Multivariate analysis indicated that pCEA was an independent prognostic factor for the survival of patients with gastric cancer.CONCLUSION: Intraoperative pCEA is a more sensitive and reliable predictor of peritoneal metastasis as well as prognosis in patients with gastric cancer as compared to PLC method.

  20. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

    Science.gov (United States)

    Barretina, Jordi; Caponigro, Giordano; Stransky, Nicolas; Venkatesan, Kavitha; Margolin, Adam A; Kim, Sungjoon; Wilson, Christopher J; Lehár, Joseph; Kryukov, Gregory V; Sonkin, Dmitriy; Reddy, Anupama; Liu, Manway; Murray, Lauren; Berger, Michael F; Monahan, John E; Morais, Paula; Meltzer, Jodi; Korejwa, Adam; Jané-Valbuena, Judit; Mapa, Felipa A; Thibault, Joseph; Bric-Furlong, Eva; Raman, Pichai; Shipway, Aaron; Engels, Ingo H; Cheng, Jill; Yu, Guoying K; Yu, Jianjun; Aspesi, Peter; de Silva, Melanie; Jagtap, Kalpana; Jones, Michael D; Wang, Li; Hatton, Charles; Palescandolo, Emanuele; Gupta, Supriya; Mahan, Scott; Sougnez, Carrie; Onofrio, Robert C; Liefeld, Ted; MacConaill, Laura; Winckler, Wendy; Reich, Michael; Li, Nanxin; Mesirov, Jill P; Gabriel, Stacey B; Getz, Gad; Ardlie, Kristin; Chan, Vivien; Myer, Vic E; Weber, Barbara L; Porter, Jeff; Warmuth, Markus; Finan, Peter; Harris, Jennifer L; Meyerson, Matthew; Golub, Todd R; Morrissey, Michael P; Sellers, William R; Schlegel, Robert; Garraway, Levi A

    2012-03-28

    The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

  1. E2F1 and KIAA0191 expression predicts breast cancer patient survival

    Directory of Open Access Journals (Sweden)

    Hassell John A

    2011-03-01

    Full Text Available Abstract Background Gene expression profiling of human breast tumors has uncovered several molecular signatures that can divide breast cancer patients into good and poor outcome groups. However, these signatures typically comprise many genes (~50-100, and the prognostic tests associated with identifying these signatures in patient tumor specimens require complicated methods, which are not routinely available in most hospital pathology laboratories, thus limiting their use. Hence, there is a need for more practical methods to predict patient survival. Methods We modified a feature selection algorithm and used survival analysis to derive a 2-gene signature that accurately predicts breast cancer patient survival. Results We developed a tree based decision method that segregated patients into various risk groups using KIAA0191 expression in the context of E2F1 expression levels. This approach led to highly accurate survival predictions in a large cohort of breast cancer patients using only a 2-gene signature. Conclusions Our observations suggest a possible relationship between E2F1 and KIAA0191 expression that is relevant to the pathogenesis of breast cancer. Furthermore, our findings raise the prospect that the practicality of patient prognosis methods may be improved by reducing the number of genes required for analysis. Indeed, our E2F1/KIAA0191 2-gene signature would be highly amenable for an immunohistochemistry based test, which is commonly used in hospital laboratories.

  2. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

    Science.gov (United States)

    Yu, Kun-Hsing; Zhang, Ce; Berry, Gerald J.; Altman, Russ B.; Ré, Christopher; Rubin, Daniel L.; Snyder, Michael

    2016-01-01

    Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs. PMID:27527408

  3. Computerized prediction of breast cancer risk: comparison between the global and local bilateral mammographic tissue asymmetry

    Science.gov (United States)

    Wang, Xingwei; Lederman, Dror; Tan, Jun; Wang, Xiao Hui; Zheng, Bin

    2011-03-01

    We have developed and preliminarily tested a new breast cancer risk prediction model based on computerized bilateral mammographic tissue asymmetry. In this study, we investigated and compared the performance difference of our risk prediction model when the bilateral mammographic tissue asymmetrical features were extracted in two different methods namely (1) the entire breast area and (2) the mirror-matched local strips between the left and right breast. A testing dataset including bilateral craniocaudal (CC) view images of 100 negative and 100 positive cases for developing breast abnormalities or cancer was selected from a large and diverse full-field digital mammography (FFDM) image database. To detect bilateral mammographic tissue asymmetry, a set of 20 initial "global" features were extracted from the entire breast areas of two bilateral mammograms in CC view and their differences were computed. Meanwhile, a pool of 16 local histogram-based statistic features was computed from eight mirror-matched strips between the left and right breast. Using a genetic algorithm (GA) to select optimal features, two artificial neural networks (ANN) were built to predict the risk of a test case developing cancer. Using the leave-one-case-out training and testing method, two GAoptimized ANNs yielded the areas under receiver operating characteristic (ROC) curves of 0.754+/-0.024 (using feature differences extracted from the entire breast area) and 0.726+/-0.026 (using the feature differences extracted from 8 pairs of local strips), respectively. The risk prediction model using either ANN is able to detect 58.3% (35/60) of cancer cases 6 to 18 months earlier at 80% specificity level. This study compared two methods to compute bilateral mammographic tissue asymmetry and demonstrated that bilateral mammographic tissue asymmetry was a useful breast cancer risk indicator with high discriminatory power.

  4. Prediction of bleeding and prophylactic platelet transfusions in cancer patients with thrombocytopenia

    DEFF Research Database (Denmark)

    Vinholt, Pernille J; Alnor, Anne; Nybo, Mads;

    2016-01-01

    platelet transfusion within 30 days were registered. Of 197 patients enrolled, 56 (28%) experienced bleeding. In multivariate analyses, predictors of bleeding were infection (adjusted odds ratio (OR) = 2.65 and 95% confidence interval (95% CI) 1.04-6.74); treatment with platelet inhibitors, heparin...... platelet transfusion but not bleeding. Bleeding risk factors were previous haematuria or gastrointestinal bleeding, infection, antiplatelet or anticoagulant treatment, high urea nitrogen, low haemoglobin or high creatinine.......Studies on markers for bleeding risk among thrombocytopenic cancer patients are lacking. This prospective observational cohort study investigated whether platelet parameters and a standardised bleeding questionnaire predicted bleeding or prophylactic platelet transfusions in patients with cancer...

  5. Can metabolomics in addition to genomics add to prognostic and predictive information in breast cancer?

    Science.gov (United States)

    Howell, Anthony

    2010-11-16

    Genomic data from breast cancers provide additional prognostic and predictive information that is beginning to be used for patient management. The question arises whether additional information derived from other 'omic' approaches such as metabolomics can provide additional information. In an article published this month in BMC Cancer, Borgan et al. add metabolomic information to genomic measures in breast tumours and demonstrate, for the first time, that it may be possible to further define subgroups of patients which could be of value clinically. See research article: http://www.biomedcentral.com/1471-2407/10/628.

  6. The impact of p53 in predicting clinical outcome of breast cancer patients with visceral metastasis

    OpenAIRE

    Yang, P.; C. W. Du; Kwan, M.; Liang, S. X.; G. J. Zhang

    2013-01-01

    In the study, we analyzed role of p53 in predicting outcome in visceral metastasis breast cancer (VMBC) patients. 97 consecutive VMBC patients were studied. P53 positivity rate was 29.9%. In the p53-negative group, median disease free survival (DFS), and time from primary breast cancer diagnosis to death (OS1), time from metastases to death (OS2) were 25, 42.5, and 13.5 months, respectively. In the p53-positive group, they were 10, 22, and 8 months, respectively. Statistically significant dif...

  7. Ultrasound and Biomarker Tests in Predicting Cancer Aggressiveness in Tissue Samples of Patients With Bladder Cancer

    Science.gov (United States)

    2016-06-09

    Bladder Papillary Urothelial Carcinoma; Stage 0a Bladder Urothelial Carcinoma; Stage 0is Bladder Urothelial Carcinoma; Stage I Bladder Cancer With Carcinoma In Situ; Stage I Bladder Urothelial Carcinoma; Stage II Bladder Urothelial Carcinoma; Stage III Bladder Urothelial Carcinoma; Stage IV Bladder Urothelial Carcinoma

  8. A utility/cost analysis of breast cancer risk prediction algorithms

    Science.gov (United States)

    Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

    2016-03-01

    Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

  9. Preoperative Multiparametric Magnetic Resonance Imaging Predicts Biochemical Recurrence in Prostate Cancer after Radical Prostatectomy.

    Directory of Open Access Journals (Sweden)

    Richard Ho

    Full Text Available To evaluate the utility of preoperative multiparametric magnetic resonance imaging (MP-MRI in predicting biochemical recurrence (BCR following radical prostatectomy (RP.From March 2007 to January 2015, 421 consecutive patients with prostate cancer (PCa underwent preoperative MP-MRI and RP. BCR-free survival rates were estimated using the Kaplan-Meier method. Cox proportional hazards models were used to identify clinical and imaging variables predictive of BCR. Logistic regression was performed to generate a nomogram to predict three-year BCR probability.Of the total cohort, 370 patients met inclusion criteria with 39 (10.5% patients experiencing BCR. On multivariate analysis, preoperative prostate-specific antigen (PSA (p = 0.01, biopsy Gleason score (p = 0.0008, MP-MRI suspicion score (p = 0.03, and extracapsular extension on MP-MRI (p = 0.03 were significantly associated with time to BCR. A nomogram integrating these factors to predict BCR at three years after RP demonstrated a c-index of 0.84, outperforming the predictive value of Gleason score and PSA alone (c-index 0.74, p = 0.02.The addition of MP-MRI to standard clinical factors significantly improves prediction of BCR in a post-prostatectomy PCa cohort. This could serve as a valuable tool to support clinical decision-making in patients with moderate and high-risk cancers.

  10. Preoperative Multiparametric Magnetic Resonance Imaging Predicts Biochemical Recurrence in Prostate Cancer after Radical Prostatectomy

    Science.gov (United States)

    George, Arvin K.; Frye, Thomas; Kilchevsky, Amichai; Fascelli, Michele; Shakir, Nabeel A.; Chelluri, Raju; Abboud, Steven F.; Walton-Diaz, Annerleim; Sankineni, Sandeep; Merino, Maria J.; Turkbey, Baris; Choyke, Peter L.; Wood, Bradford J.; Pinto, Peter A.

    2016-01-01

    Objectives To evaluate the utility of preoperative multiparametric magnetic resonance imaging (MP-MRI) in predicting biochemical recurrence (BCR) following radical prostatectomy (RP). Materials/Methods From March 2007 to January 2015, 421 consecutive patients with prostate cancer (PCa) underwent preoperative MP-MRI and RP. BCR-free survival rates were estimated using the Kaplan-Meier method. Cox proportional hazards models were used to identify clinical and imaging variables predictive of BCR. Logistic regression was performed to generate a nomogram to predict three-year BCR probability. Results Of the total cohort, 370 patients met inclusion criteria with 39 (10.5%) patients experiencing BCR. On multivariate analysis, preoperative prostate-specific antigen (PSA) (p = 0.01), biopsy Gleason score (p = 0.0008), MP-MRI suspicion score (p = 0.03), and extracapsular extension on MP-MRI (p = 0.03) were significantly associated with time to BCR. A nomogram integrating these factors to predict BCR at three years after RP demonstrated a c-index of 0.84, outperforming the predictive value of Gleason score and PSA alone (c-index 0.74, p = 0.02). Conclusion The addition of MP-MRI to standard clinical factors significantly improves prediction of BCR in a post-prostatectomy PCa cohort. This could serve as a valuable tool to support clinical decision-making in patients with moderate and high-risk cancers. PMID:27336392

  11. Cancer stem cells are new vistas for predicting the course of breast cancer

    Directory of Open Access Journals (Sweden)

    E. A. Maslyukova

    2015-01-01

    Full Text Available Despite progress in treating breast cancer (BC using a combined approach in view of morphological findings, distant metastases may develop in 30–90 % of patients with primary BC even at its early stages. The cancer stem cell (CSC theory is one of the versions that could at least partially explain therapeutic inefficiency. This theory suggests that cancer may occur and arise from a small proportion of stem cells that are able to induce tumor growth and to affect resistance to chemoand radiotherapy. CSCs were identified in different malignant tumors, including BC, cancer of the prostate, colon, pancreas, head and neck, melanoma, and multiple myelomas. This investigation analyzed aldehyde dehydrogenase type 1 (ALDH1 expression in patients with BC. Moreover, the investigators examined the relationship between this marker and the clinical and pathological features of BC. The investigation enrolled 83 locally advanced BC (T1–4N0–3M0 patients treated in 2005 to 2009. To detect CSCs, 83 histological specimens obtained at biopsy in BC patients treated at the Russian Research Center for Radiology and Surgical Technologies underwent immunohistochemical examination for ALDH1 according to the developed protocol. Analysis of a relationship between time to metastases and ALDH1 expression showed a statistically significant decrease in time to disease progression in BC patients with high ALDH1 expression versus those with low ALDH1 expression. The similar trend was observed in overall survival. The survival of patients with less than 1 % ALDH1 expression in the cancer cells was statistically higher than that in the patients with high ALDH1 expression (more than 1 %. 

  12. Fuzzy logic-based prognostic score for outcome prediction in esophageal cancer.

    Science.gov (United States)

    Wang, Chang-Yu; Lee, Tsair-Fwu; Fang, Chun-Hsiung; Chou, Jyh-Horng

    2012-11-01

    Given the poor prognosis of esophageal cancer and the invasiveness of combined modality treatment, improved prognostic scoring systems are needed. We developed a fuzzy logic-based system to improve the predictive performance of a risk score based on the serum concentrations of C-reactive protein (CRP) and albumin in a cohort of 271 patients with esophageal cancer before radiotherapy. Univariate and multivariate survival analyses were employed to validate the independent prognostic value of the fuzzy risk score. To further compare the predictive performance of the fuzzy risk score with other prognostic scoring systems, time-dependent receiver operating characteristic curve (ROC) analysis was used. Application of fuzzy logic to the serum values of CRP and albumin increased predictive performance for 1-year overall survival (AUC=0.773) compared with that of a single marker (AUC=0.743 and 0.700 for CRP and albumin, respectively), where the AUC denotes the area under curve. This fuzzy logic-based approach also performed consistently better than the Glasgow Prognostic Score (GPS) (AUC=0.745). Thus, application of fuzzy logic to the analysis of serum markers can more accurately predict the outcome for patients with esophageal cancer.

  13. Integrating Domain Specific Knowledge and Network Analysis to Predict Drug Sensitivity of Cancer Cell Lines.

    Science.gov (United States)

    Kim, Sebo; Sundaresan, Varsha; Zhou, Lei; Kahveci, Tamer

    2016-01-01

    One of fundamental challenges in cancer studies is that varying molecular characteristics of different tumor types may lead to resistance to certain drugs. As a result, the same drug can lead to significantly different results in different types of cancer thus emphasizing the need for individualized medicine. Individual prediction of drug response has great potential to aid in improving the clinical outcome and reduce the financial costs associated with prescribing chemotherapy drugs to which the patient's tumor might be resistant. In this paper we develop a network based classifier (NBC) method for predicting sensitivity of cell lines to anticancer drugs from transcriptome data. In the literature, this strategy has been used for predicting cancer types. Here, we extend it to estimate sensitivity of cells from different tumor types to various anticancer drugs. Furthermore, we incorporate domain specific knowledge such as the use of apoptotic gene list and clinical dose information in our method to impart biological significance to the prediction. Our experimental results suggest that our network based classifier (NBC) method outperforms existing classifiers in estimating sensitivity of cell lines for different drugs. PMID:27607242

  14. Microsatellite Instability Predicts Clinical Outcome in Radiation-Treated Endometrioid Endometrial Cancer

    International Nuclear Information System (INIS)

    Purpose: To elucidate whether microsatellite instability (MSI) predicts clinical outcome in radiation-treated endometrioid endometrial cancer (EEC). Methods and Materials: A consecutive series of 93 patients with EEC treated with extrafascial hysterectomy and postoperative radiotherapy was studied. The median clinical follow-up of patients was 138 months, with a maximum of 232 months. Five quasimonomorphic mononucleotide markers (BAT-25, BAT-26, NR21, NR24, and NR27) were used for MSI classification. Results: Twenty-five patients (22%) were classified as MSI. Both in the whole series and in early stages (I and II), univariate analysis showed a significant association between MSI and poorer 10-year local disease-free survival, disease-free survival, and cancer-specific survival. In multivariate analysis, MSI was excluded from the final regression model in the whole series, but in early stages MSI provided additional significant predictive information independent of traditional prognostic and predictive factors (age, stage, grade, and vascular invasion) for disease-free survival (hazard ratio [HR] 3.25, 95% confidence interval [CI] 1.01-10.49; p = 0.048) and cancer-specific survival (HR 4.20, 95% CI 1.23-14.35; p = 0.022) and was marginally significant for local disease-free survival (HR 3.54, 95% CI 0.93-13.46; p = 0.064). Conclusions: These results suggest that MSI may predict radiotherapy response in early-stage EEC.

  15. Predictive value of Sp1/Sp3/FLIP signature for prostate cancer recurrence.

    Directory of Open Access Journals (Sweden)

    Roble G Bedolla

    Full Text Available Prediction of prostate cancer prognosis is challenging and predictive biomarkers of recurrence remain elusive. Although prostate specific antigen (PSA has high sensitivity (90% at a PSA level of 4.0 ng/mL, its low specificity leads to many false positive results and considerable overtreatment of patients and its performance at lower ranges is poor. Given the histopathological and molecular heterogeneity of prostate cancer, we propose that a panel of markers will be a better tool than a single marker. We tested a panel of markers composed of the anti-apoptotic protein FLIP and its transcriptional regulators Sp1 and Sp3 using prostate tissues from 64 patients with recurrent and non-recurrent cancer who underwent radical prostatectomy as primary treatment for prostate cancer and were followed with PSA measurements for at least 5 years. Immunohistochemical staining for Sp1, Sp3, and FLIP was performed on these tissues and scored based on the proportion and intensity of staining. The predictive value of the FLIP/Sp1/Sp3 signature for clinical outcome (recurrence vs. non-recurrence was explored with logistic regression, and combinations of FLIP/Sp1/Sp3 and Gleason score were analyzed with a stepwise (backward and forward logistic model. The discrimination of the markers was identified by sensitivity-specificity analysis and the diagnostic value of FLIP/Sp1/Sp3 was determined using area under the curve (AUC for receiver operator characteristic curves. The AUCs for FLIP, Sp1, Sp3, and Gleason score for predicting PSA failure and non-failure were 0.71, 0.66, 0.68, and 0.76, respectively. However, this increased to 0.93 when combined. Thus, the "biomarker signature" of FLIP/Sp1/Sp3 combined with Gleason score predicted disease recurrence and stratified patients who are likely to benefit from more aggressive treatment.

  16. The Cancer Exome Generated by Alternative mRNA Splicing Dilutes Predicted HLA Class I Epitope Density

    DEFF Research Database (Denmark)

    Stranzl, Thomas; Larsen, Mette Voldby; Lund, Ole;

    2012-01-01

    is frequently observed in various types of cancer. Down-regulation of genes related to HLA class I antigen processing has been observed in several cancer types, leading to fewer HLA class I antigens on the cell surface. Here, we use a peptidome wide analysis of predicted alternative splice forms, based......Several studies have shown that cancers actively regulate alternative splicing. Altered splicing mechanisms in cancer lead to cancer-specific transcripts different from the pool of transcripts occurring only in healthy tissue. At the same time, altered presentation of HLA class I epitopes...... on a publicly available database, to show that peptides over-represented in cancer splice variants comprise significantly fewer predicted HLA class I epitopes compared to peptides from normal transcripts. Peptides over-represented in cancer transcripts are in the case of the three most common HLA class I...

  17. Prediction consistency and clinical presentations of breast cancer molecular subtypes for Han Chinese population

    Directory of Open Access Journals (Sweden)

    Huang Chi-Cheng

    2012-09-01

    Full Text Available Abstract Background Breast cancer is a heterogeneous disease in terms of transcriptional aberrations; moreover, microarray gene expression profiles had defined 5 molecular subtypes based on certain intrinsic genes. This study aimed to evaluate the prediction consistency of breast cancer molecular subtypes from 3 distinct intrinsic gene sets (Sørlie 500, Hu 306 and PAM50 as well as clinical presentations of each molecualr subtype in Han Chinese population. Methods In all, 169 breast cancer samples (44 from Taiwan and 125 from China of Han Chinese population were gathered, and the gene expression features corresponding to 3 distinct intrinsic gene sets (Sørlie 500, Hu 306 and PAM50 were retrieved for molecular subtype prediction. Results For Sørlie 500 and Hu 306 intrinsic gene set, mean-centring of genes and distance-weighted discrimination (DWD remarkably reduced the number of unclassified cases. Regarding pairwise agreement, the highest predictive consistency was found between Hu 306 and PAM50. In all, 150 and 126 samples were assigned into identical subtypes by both Hu 306 and PAM50 genes, under mean-centring and DWD. Luminal B tended to show a higher nuclear grade and have more HER2 over-expression status than luminal A did. No basal-like breast tumours were ER positive, and most HER2-enriched breast tumours showed HER2 over-expression, whereas, only two-thirds of ER negativity/HER2 over-expression tumros were predicted as HER2-enriched molecular subtype. For 44 Taiwanese breast cancers with survival data, a better prognosis of luminal A than luminal B subtype in ER-postive breast cancers and a better prognosis of basal-like than HER2-enriched subtype in ER-negative breast cancers was observed. Conclusions We suggest that the intrinsic signature Hu 306 or PAM50 be used for breast cancers in the Han Chinese population during molecular subtyping. For the prognostic value and decision making based on intrinsic subtypes, further prospective

  18. A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

    CERN Document Server

    Staiger, C; Kooter, R; Dittrich, M; Mueller, T; Klau, G W; Wessels, L F A

    2011-01-01

    Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple sin...

  19. Predicting Adverse Health Outcomes in Long-Term Survivors of a Childhood Cancer

    Directory of Open Access Journals (Sweden)

    Chaya S. Moskowitz

    2014-07-01

    Full Text Available More than 80% of children and young adults diagnosed with invasive cancer will survive five or more years beyond their cancer diagnosis. This population has an increased risk for serious illness- and treatment-related morbidity and premature mortality. A number of these adverse health outcomes, such as cardiovascular disease and some second primary neoplasms, either have modifiable risk factors or can be successfully treated if detected early. Absolute risk models that project a personalized risk of developing a health outcome can be useful in patient counseling, in designing intervention studies, in forming prevention strategies, and in deciding upon surveillance programs. Here, we review existing absolute risk prediction models that are directly applicable to survivors of a childhood cancer, discuss the concepts and interpretation of absolute risk models, and examine ways in which these models can be used applied in clinical practice and public health.

  20. From origami to software development: a review of studies on judgment-based predictions of performance time.

    Science.gov (United States)

    Halkjelsvik, Torleif; Jørgensen, Magne

    2012-03-01

    This article provides an integrative review of the literature on judgment-based predictions of performance time, often described as task duration predictions in psychology and as expert-based effort estimation in engineering and management science. We summarize results on the characteristics of performance time predictions, processes and strategies, the influence of task characteristics and contextual factors, and the relations between estimates and characteristics of the estimator. Although dependent on the type of study and the level of analysis, underestimation was more frequently reported than overestimation in studies from the engineering and management literature. However, this was not the case in studies from the psychology literature. Our summaries challenge earlier results regarding the effects of factors such as complexity/difficulty and experience. We also question the recurrent finding that small tasks are overestimated and large tasks are underestimated, as this to some extent can be a statistical artifact caused by random error. Several other influences on predictions are identified and discussed. These include various types of anchoring effects, performance and accuracy incentives, task decomposition, request formats, group estimation, revisions of initial ideal or incomplete estimates, level of abstraction, and superficial cues. We summarize similarities and differences between performance time predictions (e.g., number of work hours) and completion time predictions (e.g., delivery dates) because many studies fail to distinguish between these 2 types of predictions. Finally, we discuss methodological issues in time prediction research and implications for research and application. PMID:22061688

  1. Prognostic nomograms for predicting survival and distant metastases in locally advanced rectal cancers.

    Directory of Open Access Journals (Sweden)

    Junjie Peng

    Full Text Available To develop prognostic nomograms for predicting outcomes in patients with locally advanced rectal cancers who do not receive preoperative treatment.A total of 883 patients with stage II-III rectal cancers were retrospectively collected from a single institution. Survival analyses were performed to assess each variable for overall survival (OS, local recurrence (LR and distant metastases (DM. Cox models were performed to develop a predictive model for each endpoint. The performance of model prediction was validated by cross validation and on an independent group of patients.The 5-year LR, DM and OS rates were 22.3%, 32.7% and 63.8%, respectively. Two prognostic nomograms were successfully developed to predict 5-year OS and DM-free survival rates, with c-index of 0.70 (95% CI = [0.66, 0.73] and 0.68 (95% CI = [0.64, 0.72] on the original dataset, and 0.76 (95% CI = [0.67, 0.86] and 0.73 (95% CI = [0.63, 0.83] on the validation dataset, respectively. Factors in our models included age, gender, carcinoembryonic antigen value, tumor location, T stage, N stage, metastatic lymph nodes ratio, adjuvant chemotherapy and chemoradiotherapy. Predicted by our nomogram, substantial variability in terms of 5-year OS and DM-free survival was observed within each TNM stage category.The prognostic nomograms integrated demographic and clinicopathological factors to account for tumor and patient heterogeneity, and thereby provided a more individualized outcome prognostication. Our individualized prediction nomograms could help patients with preoperatively under-staged rectal cancer about their postoperative treatment strategies and follow-up protocols.

  2. Comparison of Elixhauser and Charlson Methods for Predicting Oral Cancer Survival.

    Science.gov (United States)

    Chang, Heng-Jui; Chen, Po-Chun; Yang, Ching-Chieh; Su, Yu-Chieh; Lee, Ching-Chih

    2016-02-01

    Cancer survival correlates not only with the features of primary malignancy but also with the degree of underlying comorbidities. Of the multiple methods used for evaluating the impact of comorbidities on survival, the Charlson and Elixhauser methods are most common. This study compared these 2 comorbidity measures for predicting survival in oral cancer patients. Using the Taiwan National Health Insurance claims data (2008-2011), we acquired data regarding patients' characteristics, comorbidities, and survival from 3583 oral cancer patients. Comorbidity was classified according to both the Charlson comorbidity and Elixhauser comorbidity based on the International Classification of Diseases, 9th Revision. The Elixhauser comorbidity score and Charlson comorbidity score were also calculated. The prediction of survival was determined using measures of discrimination, including the Akaike information criterion and Harrell C (C-statistic). The mean age of the study cohort was 52 ± 10 years, and 94.9% of the patients were male. The median follow-up time was 30.1 months, and the 3-year overall survival was 61.6%. Elixhauser comorbidity method added higher discrimination, compared with the Charlson comorbidity method (Harrell C, 0.677 vs 0.651). Furthermore, the Elixhauser comorbidity score outperformed the Charlson comorbidity score in continuous variable (Harrell C, 0.654 vs 0.646) and category (Harrell C, 0.658 vs 0.645). The Elixhauser method is a superior comorbidity risk-adjustment model for oral cancer survival prediction. Utilization of the Elixhauser comorbidity method may be encouraged for risk adjustment in oral cancer study.

  3. Lung perfusion SPECT in predicting postoperative pulmonary function in lung cancer

    International Nuclear Information System (INIS)

    The aim of this prospective study is to evaluate the availability of preoperative perfusion SPECT in predicting postoperative pulmonary function following resection. Twenty-three patients with lung cancer who were candidates for lobectomy were investigated preoperatively with spirometry, x-ray computed tomography and 99mTc-macroaggregated albumin SPECT. Their postoperative pulmonary functions were predicted with these examinations. The forced vital capacity and the forced expiratory volume in one second were selected as parameters for overall pulmonary function. The postoperative pulmonary function was predicted by the following formula: Predicted postoperative value=observed preoperative value x precent perfusion of the lung not to be resected. The patients were reinvestigated with spirometry at 3 months and 6 months after lobectomy, and the values obtained were statistically compared with the predicted values. Close relationships were found between predicted and observed forced vital capacity (r=0.87, p<0.001), and predicted and observed forced expiratory volume in one second (r=0.90, p<0.001). The accurate prediction of pulmonary function after lobectomy could be achieved by means of lung perfusion SPECT. (author)

  4. The Prognostic and Predictive Value of Soluble Type IV Collagen in Colorectal Cancer

    DEFF Research Database (Denmark)

    Rolff, Hans Christian; Christensen, Ib Jarle; Vainer, Ben;

    2016-01-01

    validation set, respectively. The prognostic impact was present both in patients with metastatic and nonmetastatic disease. The predictive value of the marker was investigated in stage II and III patients. In the training set, type IV collagen was prognostic both in the subsets of patients receiving and not...... receiving adjuvant antineoplastic therapy. However, in the validation set, the prognostic effect of the marker vanished when looking at patients who received adjuvant antineoplatic therapy (HR 0.90; 95% CI, 0.42-1.93) but was still present in the group not receiving adjuvant chemotherapy (HR 2.88; 95% CI, 1.......98-4.21). CONCLUSIONS: The results indicate clinical validity of type IV collagen as a prognostic biomarker in colorectal cancer, although the suggested predictive role of the marker should be validated. Clin Cancer Res; 22(10); 2427-34. ©2015 AACR....

  5. Assessment of two mammographic density related features in predicting near-term breast cancer risk

    Science.gov (United States)

    Zheng, Bin; Sumkin, Jules H.; Zuley, Margarita L.; Wang, Xingwei; Klym, Amy H.; Gur, David

    2012-02-01

    In order to establish a personalized breast cancer screening program, it is important to develop risk models that have high discriminatory power in predicting the likelihood of a woman developing an imaging detectable breast cancer in near-term (e.g., breast cancer risk models, mammographic density is considered the second highest breast cancer risk factor (second to woman's age). In this study we explored a new feature, namely bilateral mammographic density asymmetry, and investigated the feasibility of predicting near-term screening outcome. The database consisted of 343 negative examinations, of which 187 depicted cancers that were detected during the subsequent screening examination and 155 that remained negative. We computed the average pixel value of the segmented breast areas depicted on each cranio-caudal view of the initial negative examinations. We then computed the mean and difference mammographic density for paired bilateral images. Using woman's age, subjectively rated density (BIRADS), and computed mammographic density related features we compared classification performance in estimating the likelihood of detecting cancer during the subsequent examination using areas under the ROC curves (AUC). The AUCs were 0.63+/-0.03, 0.54+/-0.04, 0.57+/-0.03, 0.68+/-0.03 when using woman's age, BIRADS rating, computed mean density and difference in computed bilateral mammographic density, respectively. Performance increased to 0.62+/-0.03 and 0.72+/-0.03 when we fused mean and difference in density with woman's age. The results suggest that, in this study, bilateral mammographic tissue density is a significantly stronger (p<0.01) risk indicator than both woman's age and mean breast density.

  6. Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

    Directory of Open Access Journals (Sweden)

    Marcio Luis Acencio

    Full Text Available Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI. This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved

  7. Development and external validation of a prostate health index-based nomogram for predicting prostate cancer

    OpenAIRE

    Yao Zhu; Cheng-Tao Han; Gui-Ming Zhang; Fang Liu; Qiang Ding; Jian-Feng Xu; Vidal, Adriana C.; Freedland, Stephen J.; Chi-Fai Ng; Ding-Wei Ye

    2015-01-01

    To develop and externally validate a prostate health index (PHI)-based nomogram for predicting the presence of prostate cancer (PCa) at biopsy in Chinese men with prostate-specific antigen 4–10 ng/mL and normal digital rectal examination (DRE). 347 men were recruited from two hospitals between 2012 and 2014 to develop a PHI-based nomogram to predict PCa. To validate these results, we used a separate cohort of 230 men recruited at another center between 2008 and 2013. Receiver operator curves ...

  8. Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

    International Nuclear Information System (INIS)

    Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin level before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.

  9. Predicting Ovarian Cancer Patients' Clinical Response to Platinum-Based Chemotherapy by Their Tumor Proteomic Signatures.

    Science.gov (United States)

    Yu, Kun-Hsing; Levine, Douglas A; Zhang, Hui; Chan, Daniel W; Zhang, Zhen; Snyder, Michael

    2016-08-01

    Ovarian cancer is the deadliest gynecologic malignancy in the United States with most patients diagnosed in the advanced stage of the disease. Platinum-based antineoplastic therapeutics is indispensable to treating advanced ovarian serous carcinoma. However, patients have heterogeneous responses to platinum drugs, and it is difficult to predict these interindividual differences before administering medication. In this study, we investigated the tumor proteomic profiles and clinical characteristics of 130 ovarian serous carcinoma patients analyzed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), predicted the platinum drug response using supervised machine learning methods, and evaluated our prediction models through leave-one-out cross-validation. Our data-driven feature selection approach indicated that tumor proteomics profiles contain information for predicting binarized platinum response (P drug responses as well as provided insights into the biological processes influencing the efficacy of platinum-based therapeutics. Our analytical approach is also extensible to predicting response to other antineoplastic agents or treatment modalities for both ovarian and other cancers. PMID:27312948

  10. Accuracy of CT cerebral perfusion in predicting infarct in the emergency department: lesion characterization on CT perfusion based on commercially available software.

    Science.gov (United States)

    Ho, Chang Y; Hussain, Sajjad; Alam, Tariq; Ahmad, Iftikhar; Wu, Isaac C; O'Neill, Darren P

    2013-06-01

    This study aims to assess the diagnostic accuracy of a single vendor commercially available CT perfusion (CTP) software in predicting stroke. A retrospective analysis on patients presenting with stroke-like symptoms within 6 h with CTP and diffusion-weighted imaging (DWI) was performed. Lesion maps, which overlays areas of computer-detected abnormally elevated mean transit time (MTT) and decreased cerebral blood volume (CBV), were assessed from a commercially available software package and compared to qualitative interpretation of color maps. Using DWI as the gold standard, parameters of diagnostic accuracy were calculated. Point biserial correlation was performed to assess for relationship of lesion size to a true positive result. Sixty-five patients (41 females and 24 males, age range 22-92 years, mean 57) were included in the study. Twenty-two (34 %) had infarcts on DWI. Sensitivity (83 vs. 70 %), specificity (21 vs. 69 %), negative predictive value (77 vs. 84 %), and positive predictive value (29 vs. 50 %) for lesion maps were contrasted to qualitative interpretation of perfusion color maps, respectively. By using the lesion maps to exclude lesions detected qualitatively on color maps, specificity improved (80 %). Point biserial correlation for computer-generated lesions (R pb = 0.46, p perfusion color map assessment, the lesion maps can help improve specificity.

  11. Computer Software.

    Science.gov (United States)

    Kay, Alan

    1984-01-01

    Discusses the nature and development of computer software. Programing, programing languages, types of software (including dynamic spreadsheets), and software of the future are among the topics considered. (JN)

  12. Evaluation of the effect of PSA inter-assay variability on nomograms for prostate cancer prediction

    OpenAIRE

    Siemßen, Kerstin

    2012-01-01

    Purpose: To evaluate the suitability of published nomograms for prostate cancer (PCa) risk prediction, in particular considering the prostate specific antigen (PSA) inter-assay variability. Patients and Methods: Total (tPSA) and free PSA were determined with five different assays in 780 biopsy-referred men. Together with age, prostate volume and digital rectal examination (DRE) status these data were applied to five published nomograms for PCa detection. The criteria discrimina...

  13. Can high frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

    Energy Technology Data Exchange (ETDEWEB)

    Clough, Gillian R. [University of Bradford, Unity Building, 25 Trinity Road, Bradford BD5 0BB (United Kingdom)]. E-mail: g.r.clough@bradford.ac.uk; Truscott, John [University of Leeds (United Kingdom); Haigh, Isobel [Leeds Teaching Hospitals NHS Trust (United Kingdom)

    2006-05-15

    Purpose: Use high frequency ultrasound to predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to improve the surgical management and possible survival rate of groups of patients who would not normally have conventional axillary dissections. Materials and methods: The ipsilateral and contralateral axillas of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, measurements were also taken to determine the L/S (long axis/short axis) ratio of the node and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results: In this study of patients with invasive breast cancer, where ipsilateral lymph nodes had a cortical bulge of {>=}3 mm and/or at least two lymph nodes had absent echogenic centres, all had disease spread to the axillary lymph nodes (10 patients). Sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P-value was 0.001 and the Kappa score was 0.55. Conclusion: This would indicate that high frequency ultrasound could be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management.

  14. Predicting Response to Hormonal Therapy and Survival in Men with Hormone Sensitive Metastatic Prostate Cancer

    OpenAIRE

    Grivas, Petros D; Robins, Diane M.; Hussain, Maha

    2012-01-01

    Androgen deprivation is the cornerstone of the management of metastatic prostate cancer. Despite several decades of clinical experience with this therapy there are no standard predictive biomarkers for response. Although several candidate genetic, hormonal, inflammatory, biochemical, metabolic biomarkers have been suggested as potential predictors of response and outcome, none has been prospectively validated nor has proven clinical utility to date. There is significant heterogeneity in the d...

  15. Multiscale approach predictions for biological outcomes in ion-beam cancer therapy

    OpenAIRE

    Alexey Verkhovtsev; Eugene Surdutovich; Solov’yov, Andrey V.

    2016-01-01

    Ion-beam therapy provides advances in cancer treatment, offering the possibility of excellent dose localization and thus maximising cell-killing within the tumour. The full potential of such therapy can only be realised if the fundamental mechanisms leading to lethal cell damage under ion irradiation are well understood. The key question is whether it is possible to quantitatively predict macroscopic biological effects caused by ion radiation on the basis of physical and chemical effects rela...

  16. Which biomarker predicts benefit from EGFR-TKI treatment for patients with lung cancer?

    OpenAIRE

    Uramoto, H; Mitsudomi, T.

    2007-01-01

    Subsets of patients with non-small cell lung cancer respond remarkably well to small molecule tyrosine kinase inhibitors (TKI) specific for epidermal growth factor receptor (EGFR) such as gefitinib or erlotinib. In 2004, it was found that EGFR mutations occurring in the kinase domain are strongly associated with EGFR-TKI sensitivity. However, subsequent studies revealed that this relationship was not perfect and various predictive markers have been reported. These include EGFR gene copy numbe...

  17. Mitochondrial genetic polymorphisms do not predict survival in patients with pancreatic cancer

    OpenAIRE

    Halfdanarson, Thorvardur R.; Wang, Liang; William R Bamlet; de ANDRADE, MARIZA; Robert R McWilliams; Cunningham, Julie M.; Petersen, Gloria M.

    2008-01-01

    Pancreatic cancer (PC) is a highly lethal malignancy, and the majority of patients succumb to the disease within two years. We evaluated the role of variants of mitochondrial DNA (mtDNA) and mitochondrial haplogroups in predicting prognosis of patients with PC. A total of 24 mitochondrial single nucleotide polymorphisms (mtSNPs) were genotyped in 990 patients with PC. After adjusting for covariates and multiple comparisons, no association between any of the mtSNPs or haplogroups and survival ...

  18. Prediction of Treatment Outcome with Bioimpedance Measurements in Breast Cancer Related Lymphedema Patients

    OpenAIRE

    Kim, Leesuk; Jeon, Jae Yong; Sung, In Young; Jeong, Soon Yong; Do, Jung Hwa; Kim, Hwa Jung

    2011-01-01

    Objective To investigate the usefulness of bioimpedance measurement for predicting the treatment outcome in breast cancer related lymphedema (BCRL) patients. Method Unilateral BCRL patients who received complex decongestive therapy (CDT) for 2 weeks (5 days per week) were enrolled in this study. We measured the ratio of extracellular fluid (ECF) volume by using bioelectrical impedance spectroscopy (BIS), and single frequency bioimpedance analysis (SFBIA) at a 5 kHz frequency before treatment....

  19. Statistical models for predicting number of involved nodes in breast cancer patients

    OpenAIRE

    Dwivedi, Alok Kumar; Dwivedi, Sada Nand; Deo, Suryanarayana; Shukla, Rakesh; Kopras, Elizabeth

    2010-01-01

    Clinicians need to predict the number of involved nodes in breast cancer patients in order to ascertain severity, prognosis, and design subsequent treatment. The distribution of involved nodes often displays over-dispersion—a larger variability than expected. Until now, the negative binomial model has been used to describe this distribution assuming that over-dispersion is only due to unobserved heterogeneity. The distribution of involved nodes contains a large proportion of excess zeros (neg...

  20. Prediction of lung cancer spread and functional resectability by 133Xe radiospirometry

    International Nuclear Information System (INIS)

    Sixty-six patients with primary lung cancer who underwent thoracotomy were studied to determine the correlations among 133Xe radiospirometry, surgical procedures and histological extension of the lung cancer. Disturbance in the regional perfusion (Q radical per cent) was more prominent than disturbance of the regional ventilation (V radical per cent), as the pathological stage and t factor proceeded, while V radical per cent and Q radical per cent were disturbed almost equally in relation to the pathological n factor. Lobectomy was impossible in patients with a Q radical per cent of less than 33 per cent of the total, but low perfusion did not necessarily contraindicate surgery. The predicted postoperative FEVsub(1.0) was calculated according to the equation of (1 - b/a x (V radical per cent or Q radical per cent)) x (preoperative FEVsub(1.0)), where a and b were the number of subsegments in the lung lobes on the involved side and the resected lobe. The predicted and actually measured postoperative FEVsub(1.0) showed significant correlations (<0.001) in both equations. We conclude that Q radical per cent reflects a complex pattern of lung cancer spread more sensitivity than dose V radical per cent, and the significance of V radical per cent and Q radical per cent in terms of prediction of postoperative EFVsub(1.0) seems to be equivocal. (author)

  1. Bone scintigraphy predicts the risk of spinal cord compression in hormone-refractory prostate cancer

    International Nuclear Information System (INIS)

    In prostate cancer, confirmation of metastatic involvement of the skeleton has traditionally been achieved by bone scintigraphy, although the widespread availability of prostate-specific antigen (PSA) measurements has tended to eliminate the need for this investigation. The potential of bone scintigraphy to predict skeletal-related events, particularly spinal cord compression, after the onset of hormone refractoriness has never been investigated. The aim of this study was to establish whether a new method of evaluating bone scintigraphy would offer a better predictive value for this complication of the metastatic process than is achieved with currently available grading methods. We studied 84 patients with hormone-refractory prostate cancer who had undergone bone scintigraphy at the time of hormone escape. Tumour grading and parameters of tumour load (PSA and alkaline phosphatase activity) were available in all patients. The incidence of spinal cord compression was documented and all patients were followed up until death. Bone scintigraphy was evaluated by the conventional Soloway grading and by an additional analysis determining total or partial involvement of individual vertebrae. In contrast to the Soloway method, the new method was able to predict spinal cord compression at various spinal levels. Our data suggest that there is still a place for bone scintigraphy in the management of hormone-refractory prostate cancer. (orig.)

  2. Validation of the memorial Sloan-Kettering Cancer Center nomogram to predict disease-specific survival after R0 resection in a Chinese gastric cancer population.

    Directory of Open Access Journals (Sweden)

    Donglai Chen

    Full Text Available BACKGROUND: Prediction of disease-specific survival (DSS for individual patient with gastric cancer after R0 resection remains a clinical concern. Since the clinicopathologic characteristics of gastric cancer vary widely between China and western countries, this study is to evaluate a nomogram from Memorial Sloan-Kettering Cancer Center (MSKCC for predicting the probability of DSS in patients with gastric cancer from a Chinese cohort. METHODS: From 1998 to 2007, clinical data of 979 patients with gastric cancer who underwent R0 resection were retrospectively collected from Peking University Cancer Hospital & Institute and used for external validation. The performance of the MSKCC nomogram in our population was assessed using concordance index (C-index and calibration plot. RESULTS: The C-index for the MSKCC predictive nomogram was 0.74 in the Chinese cohort, compared with 0.69 for American Joint Committee on Cancer (AJCC staging system (P<0.0001. This suggests that the discriminating value of MSKCC nomogram is superior to AJCC staging system for prognostic prediction in the Chinese population. Calibration plots showed that the actual survival of Chinese patients corresponded closely to the MSKCC nonogram-predicted survival probabilities. Moreover, MSKCC nomogram predictions demonstrated the heterogeneity of survival in stage IIA/IIB/IIIA/IIIB disease of the Chinese patients. CONCLUSION: In this study, we externally validated MSKCC nomogram for predicting the probability of 5- and 9-year DSS after R0 resection for gastric cancer in a Chinese population. The MSKCC nomogram performed well with good discrimination and calibration. The MSKCC nomogram improved individualized predictions of survival, and may assist Chinese clinicians and patients in individual follow-up scheduling, and decision making with regard to various treatment options.

  3. Toxicity ratios: Their use and abuse in predicting the risk from induced cancer

    International Nuclear Information System (INIS)

    The toxicity ratio concept assumes the validity of certain relationships. In some examples for bone sarcoma induction, the approximate toxicity of 239Pu in man can be calculated algebraically from the observed toxicity in the radium-dial painters and the ratio of 239Pu/226Ra toxicities in suitable laboratory mammals. In a species highly susceptible to bone sarcoma induction, the risk coefficients for both 239Pu and 226Ra are elevated, but the toxicity ratio of 239Pu to 226Ra tends to be similar to the ratio in resistant species. Among the tested species the toxicity ratio of 239Pu to 226Ra ranged from 6 to 22 (a fourfold range), whereas their relative sensitivities to 239Pu varied by a factor of 150. The toxicity ratio approach can also be used to estimate the actinide risk to man from liver cancer, by comparing to the Thorotrast patients; from lung cancer, by comparing to the uranium miners and the atomic-bomb survivors; and from neutron-induced cancers, by comparing to cancers induced by gamma rays. The toxicity ratio can be used to predict the risk to man from a specific type of cancer that has been reliably induced by a reference radiation in humans and that can be induced by both the reference and the investigated radiation in suitable laboratory animals. 26 refs., 3 figs., 1 tab

  4. Altered expression patterns of syndecan-1 and -2 predict biochemical recurrence in prostate cancer

    Institute of Scientific and Technical Information of China (English)

    Rodrigo Ledezma; Federico Cifuentes; Iván Gallegos; Juan Fullá; Enrique Ossandon; Enrique A Castellon; Héctor R Contreras

    2011-01-01

    The clinical features of prostate cancer do not provide an accurate determination of patients undergoing biochemical relapse and are therefore not suitable as indicators of prognosis for recurrence. New molecular markers are needed for proper pre-treatment risk stratification of patients. Our aim was to assess the value of altered expression of syndecan-1 and -2 as a marker for predicting biochemical relapse in patients with clinically localized prostate cancer treated by radical prostatectomy. The expression of syndecan-1 and -2 was examined by immunohistochemical staining in a series of 60 paraffin-embedded tissue samples from patients with localized prostate cancer. Ten specimens from patients with benign prostatic hyperplasia were used as non-malignant controls. Semiquantitative analysis was performed to evaluate the staining patterns. To investigate the prognostic value, Kaplan-Meier survival curves were performed and compared by a log-rank test. In benign samples, syndecan-1 was expressed in basal and secretory epithelial cells with basolateral membrane localisation, whereas syndecan-2 was expressed preferentially in basal cells. In prostate cancer samples, the expression patterns of both syndecans shifted to granular-cytoplasmic localisation. Survival analysis showed a significant difference (P<0.05) between normal and altered expression of syndecan-1 and -2 in free prostate-specific antigen recurrence survival curves. These data suggest that the expression of syndecan-1 and -2 can be used as a prognostic marker for patients with clinically localized prostate cancer, improving the prostate-specific antigen recurrence risk stratification.

  5. Risk factors predictive of occult cancer detection in patients with unprovoked venous thromboembolism

    Science.gov (United States)

    Ihaddadene, Ryma; Corsi, Daniel J.; Lazo-Langner, Alejandro; Shivakumar, Sudeep; Zarychanski, Ryan; Tagalakis, Vicky; Solymoss, Susan; Routhier, Nathalie; Douketis, James; Le Gal, Gregoire

    2016-01-01

    Risk factors predictive of occult cancer detection in patients with a first unprovoked symptomatic venous thromboembolism (VTE) are unknown. Cox proportional hazard models and multivariate analyses were performed to assess the effect of specific risk factors on occult cancer detection within 1 year of a diagnosis of unprovoked VTE in patients randomized in the Screening for Occult Malignancy in Patients with Idiopathic Venous Thromboembolism (SOME) trial. A total of 33 (3.9%; 95% CI, 2.8%-5.4%) out of the 854 included patients received a new diagnosis of cancer at 1-year follow-up. Age ≥ 60 years (hazard ratio [HR], 3.11; 95% CI, 1.41-6.89; P = .005), previous provoked VTE (HR, 3.20; 95% CI, 1.19-8.62; P = .022), and current smoker status (HR, 2.80; 95% CI, 1.24-6.33; P = .014) were associated with occult cancer detection. Age, prior provoked VTE, and smoking status may be important predictors of occult cancer detection in patients with first unprovoked VTE. This trial was registered at www.clinicaltrials.gov as #NCT00773448. PMID:26817957

  6. A mouse stromal response to tumor invasion predicts prostate and breast cancer patient survival.

    Directory of Open Access Journals (Sweden)

    Marina Bacac

    Full Text Available Primary and metastatic tumor growth induces host tissue responses that are believed to support tumor progression. Understanding the molecular changes within the tumor microenvironment during tumor progression may therefore be relevant not only for discovering potential therapeutic targets, but also for identifying putative molecular signatures that may improve tumor classification and predict clinical outcome. To selectively address stromal gene expression changes during cancer progression, we performed cDNA microarray analysis of laser-microdissected stromal cells derived from prostate intraepithelial neoplasia (PIN and invasive cancer in a multistage model of prostate carcinogenesis. Human orthologs of genes identified in the stromal reaction to tumor progression in this mouse model were observed to be expressed in several human cancers, and to cluster prostate and breast cancer patients into groups with statistically different clinical outcomes. Univariate Cox analysis showed that overexpression of these genes is associated with shorter survival and recurrence-free periods. Taken together, our observations provide evidence that the expression signature of the stromal response to tumor invasion in a mouse tumor model can be used to probe human cancer, and to provide a powerful prognostic indicator for some of the most frequent human malignancies.

  7. A new molecular signature method for prediction of driver cancer pathways from transcriptional data.

    Science.gov (United States)

    Rykunov, Dmitry; Beckmann, Noam D; Li, Hui; Uzilov, Andrew; Schadt, Eric E; Reva, Boris

    2016-06-20

    Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways. PMID:27098033

  8. Identification of tumor-associated antigens as diagnostic and predictive biomarkers in cancer.

    Science.gov (United States)

    Zhang, Jian-Ying; Looi, Kok Sun; Tan, Eng M

    2009-01-01

    Many studies demonstrated that cancer sera contain antibodies which react with autologous cellular antigens generally known as tumor-associated antigens (TAAs). In our laboratories, the approach used in the identification of TAAs has involved initially examining the sera of cancer patients using extracts of tissue culture cells as source of antigens in Western blotting and by indirect immunofluorescence on whole cells. With these two techniques, we identify sera which have high-titer fluorescent staining or strong signals to cell extracts on Western blotting and subsequently use these sera as probes in immunoscreening cDNA expression libraries, and also in proteomic approaches to isolate and identify targeted antigens which might potentially be involved in malignant transformation. In this manner, several novel TAAs including HCC1, p62, p90, and others have been identified. In extension of these studies, we evaluate the sensitivity and specificity of different antigen-antibody systems as markers in cancer in order to develop "tumor-associated antigen array" systems for cancer diagnosis, cancer prediction, and for following the response of patients to treatment. PMID:19381943

  9. Predicting Municipal Solid Waste Generation through Time Series Method (ARMA Technique) and System Dynamics Modeling (Vensim Software)

    OpenAIRE

    A EBRAHIMI; M.H Ehrampoush; H Hashemi; M Dehvari

    2016-01-01

    Background and Objective: Predicting municipal solid waste generation has an important role in solid waste management. The aim of this study was to predict municipal solid waste generation in Isfahan through time series method and system dynamics modeling. Materials and Methods: Verified data of solid waste generation was collected from Waste Management Organization and population information was collected from the National Statistics Center, Iran for the period 1996-2011. Next, the effec...

  10. Predicting Lymph Node Metastasis in Endometrial Cancer Using Serum CA125 Combined with Immunohistochemical Markers PR and Ki67, and a Comparison with Other Prediction Models.

    Directory of Open Access Journals (Sweden)

    Bingyi Yang

    Full Text Available We aimed to evaluate the value of immunohistochemical markers and serum CA125 in predicting the risk of lymph node metastasis (LNM in women with endometrial cancer and to identify a low-risk group of LNM. The medical records of 370 patients with endometrial endometrioid adenocarcinoma who underwent surgical staging in the Obstetrics & Gynecology Hospital of Fudan University were collected and retrospectively reviewed. Immunohistochemical markers were screened. A model using serum cancer antigen 125 (CA125 level, the immunohistochemical markers progesterone receptor (PR and Ki67 was created for prediction of LNM. A predicted probability of 4% among these patients was defined as low risk. The developed model was externally validated in 200 patients from Shanghai Cancer Center. The efficiency of the model was compared with three other reported prediction models. Patients with serum CA125 50% and Ki67 < 40% in cancer lesion were defined as low risk for LNM. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.82. The model classified 61.9% (229/370 of patients as being at low risk for LNM. Among these 229 patients, 6 patients (2.6% had LNM and the negative predictive value was 97.4% (223/229. The sensitivity and specificity of the model were 84.6% and 67.4% respectively. In the validation cohort, the model classified 59.5% (119/200 of patients as low-risk, 3 out of these 119 patients (2.5% has LNM. Our model showed a predictive power similar to those of two previously reported prediction models. The prediction model using serum CA125 and the immunohistochemical markers PR and Ki67 is useful to predict patients with a low risk of LNM and has the potential to provide valuable guidance to clinicians in the treatment of patients with endometrioid endometrial cancer.

  11. High Levels of Nucleolar Spindle-Associated Protein and Reduced Levels of BRCA1 Expression Predict Poor Prognosis in Triple-Negative Breast Cancer.

    Directory of Open Access Journals (Sweden)

    Li Chen

    Full Text Available Nucleolar spindle-associated protein (NuSAP1 is an important mitosis-related protein, and aberrant NuSAP1 expression is associated with abnormal spindles and mitosis. This study investigated the prognostic value of NuSAP1 in breast cancer.Two sets of tissue microarrays (TMAs that included samples from 450 breast cancer patients were constructed, of which 250 patients were training set and the other 200 patients were validation set. Immunohistochemical staining was performed to determine the NuSAP1 levels. A Kaplan-Meier analysis was used to estimate the prognostic value of NuSAP1 in breast cancer. A stepwise Cox analysis was performed to construct a risk-prediction model for triple-negative breast cancer (TNBC. All statistical analysis was performed with SPSS software.There were 108 (43.5% and 88 (44.0% patients expressed NuSAP1 in the training set and validation set respectively. High levels of NuSAP1 expression were related to poor disease-free survival (DFS in both training (P = 0.028 and validation (P = 0.006 cohorts, particularly in TNBC. With combination of two cohorts, both NuSAP1 (HR = 4.136, 95% CI: 1.956-8.747, P < 0.001 and BRCA1 (HR = 0.383, 95% CI: 0.160-0.915, P = 0.031 were independent prognostic indicators of DFS in TNBC. A receiver operating characteristic (ROC analysis revealed that the combination of NuSAP1 and BRCA1 significantly improved the prognostic power compared with the traditional model (0.778 versus 0.612, P < 0.001.Our study confirms the prognostic value of NuSAP1 in breast cancer. The combination of NuSAP1 and BRCA1 could improve the DFS prediction accuracy in TNBC.

  12. Risk of colon cancer in hereditary non-polyposis colorectal cancer patients as predicted by fuzzy modeling: Influence of smoking

    Institute of Scientific and Technical Information of China (English)

    Rhonda M Brand; David D Jones; Henry T Lynch; Randall E Brand; Patrice Watson; Ramesh Ashwathnayaran; Hemant K Roy

    2006-01-01

    AIM: To investigate whether a fuzzy logic model could predict colorectal cancer (CRC) risk engendered by smoking in hereditary non-polyposis colorectal cancer(HNPCC) patients.METHODS: Three hundred and forty HNPCC mismatch repair (MMR) mutation carriers from the Creighton University Hereditary Cancer Institute Registry were selected for modeling. Age-dependent curves were generated to elucidate the joint effects between gene mutation (hMLH1 or hMSH2), gender, and smoking status on the probability of developing CRC.RESULTS: Smoking significantly increased CRC risk in male hMSH2 mutation carriers (P<0.05). hMLH1 mutations augmented CRC risk relative to hMSH2 mutation carriers for males (P < 0.05). Males had a significantly higher risk of CRC than females for hMLH1 non smokers (P<0.05), hMLH1 smokers (P < 0.1) and hMSH2 smokers (P < 0.1). Smoking promoted CRC in a dose-dependent manner in hMSH2 in males (P<0.05).Females with hMSH2 mutations and both sexes with the hMLH1 groups only demonstrated a smoking effect after an extensive smoking history (P<0.05).CONCLUSION: CRC promotion by smoking in HNPCC patients is dependent on gene mutation, gender and age. These data demonstrate that fuzzy modeling may enable formulation of clinical risk scores, thereby allowing individualization of CRC prevention strategies.

  13. A germline mutation in the BRCA1 3’UTR predicts Stage IV breast cancer

    International Nuclear Information System (INIS)

    A germline, variant in the BRCA1 3’UTR (rs8176318) was previously shown to predict breast and ovarian cancer risk in women from high-risk families, as well as increased risk of triple negative breast cancer. Here, we tested the hypothesis that this variant predicts tumor biology, like other 3’UTR mutations in cancer. The impact of the BRCA1-3’UTR-variant on BRCA1 gene expression, and altered response to external stimuli was tested in vitro using a luciferase reporter assay. Gene expression was further tested in vivo by immunoflourescence staining on breast tumor tissue, comparing triple negative patient samples with the variant (TG or TT) or non-variant (GG) BRCA1 3’UTR. To determine the significance of the variant on clinically relevant endpoints, a comprehensive collection of West-Irish breast cancer patients were tested for the variant. Finally, an association of the variant with breast screening clinical phenotypes was evaluated using a cohort of women from the High Risk Breast Program at the University of Vermont. Luciferase reporters with the BRCA1-3’UTR-variant (T allele) displayed significantly lower gene expression, as well as altered response to external hormonal stimuli, compared to the non-variant 3’UTR (G allele) in breast cancer cell lines. This was confirmed clinically by the finding of reduced BRCA1 gene expression in triple negative samples from patients carrying the homozygous TT variant, compared to non-variant patients. The BRCA1-3’UTR-variant (TG or TT) also associated with a modest increased risk for developing breast cancer in the West-Irish cohort (OR = 1.4, 95% CI 1.1-1.8, p = 0.033). More importantly, patients with the BRCA1-3’UTR-variant had a 4-fold increased risk of presenting with Stage IV disease (p = 0.018, OR = 3.37, 95% CI 1.3-11.0). Supporting that this finding is due to tumor biology, and not difficulty screening, obese women with the BRCA1-3’UTR-variant had significantly less dense breasts (p = 0.0398) in the

  14. A Nomogram to Predict Prognostic Value of Red Cell Distribution Width in Patients with Esophageal Cancer

    Directory of Open Access Journals (Sweden)

    Gui-Ping Chen

    2015-01-01

    Full Text Available Objectives. The prognostic value of inflammatory index in esophageal cancer (EC was not established. In the present study, we initially used a nomogram to predict prognostic value of red cell distribution width (RDW in patients with esophageal squamous cell carcinoma (ESCC. Methods. A total of 277 ESCC patients were included in this retrospective study. Kaplan-Meier method was used to calculate the cancer-specific survival (CSS. A nomogram was established to predict the prognosis for CSS. Results. The mean value of RDW was 14.5 ± 2.3%. The patients were then divided into two groups: RDW ≥ 14.5% and RDW < 14.5%. Patients with RDW < 14.5% had a significantly better 5-year CSS than patients with RDW ≥ 14.5% (43.9% versus 23.3%, P < 0.001. RDW was an independent prognostic factor in patients with ESCC (P = 0.036. A nomogram could be more accurate for CSS. Harrell’s c-index for CSS prediction was 0.68. Conclusion. RDW was a potential prognostic biomarker in patients with ESCC. The nomogram based on CSS could be used as an accurately prognostic prediction for patients with ESCC.

  15. Evaluation of prognostic and predictive factors in breast cancer in Cuba. Its role in personalized therapy

    International Nuclear Information System (INIS)

    The identification of prognostic and predictive factors in breast cancer has allowed applying personalized therapeutic programs without achieving, still, the individualization for all patients. The objective of the present study was to evaluate the frequency of estrogen receptors, progesterone and HER2 along with the expression of the EGFR1 and ganglioside NglicolilGM3. 1509 patients found the frequency of expression of the aforementioned receivers, which were correlated with the morphological and General variables. It was compared the AcM recognition ior egf/r3 with a game of diagnosis - shopping, and the AcM 14F7 vitro tissue fresh and included in paraffin and in vivo labelled with 99mTc. It was obtained the frequency in Cuba of these prognostic and prediction markers of response, noting her hormone dependence of tumor associated with less aggressive features. The AcM 14F7 showed a broad recognition that was not correlated with prognostic factors, but was able to detect live in primary breast tumors. The ior egf/r3 exhibited 100% specificity and positive predictive value, as well as a sensitivity and negative predictive value of 68 and 73% respectively. The recognition of the AcM 14F7 and ior egf/r3 opens a new possibility of therapeutic directed against these targets for breast cancer (author)

  16. Gene Expression Profiling to Predict Outcome After Chemoradiation in Head and Neck Cancer

    International Nuclear Information System (INIS)

    Purpose: The goal of the present study was to improve prediction of outcome after chemoradiation in advanced head and neck cancer using gene expression analysis. Materials and Methods: We collected 92 biopsies from untreated head and neck cancer patients subsequently given cisplatin-based chemoradiation (RADPLAT) for advanced squamous cell carcinomas (HNSCC). After RNA extraction and labeling, we performed dye swap experiments using 35k oligo-microarrays. Supervised analyses were performed to create classifiers to predict locoregional control and disease recurrence. Published gene sets with prognostic value in other studies were also tested. Results: Using supervised classification on the whole series, gene sets separating good and poor outcome could be found for all end points. However, when splitting tumors into training and validation groups, no robust classifiers could be found. Using Gene Set Enrichment analysis, several gene sets were found to be enriched in locoregional recurrences, although with high false-discovery rates. Previously published signatures for radiosensitivity, hypoxia, proliferation, 'wound,' stem cells, and chromosomal instability were not significantly correlated with outcome. However, a recently published signature for HNSCC defining a 'high-risk' group was shown to be predictive for locoregional control in our dataset. Conclusion: Gene sets can be found with predictive potential for locoregional control after combined radiation and chemotherapy in HNSCC. How treatment-specific these gene sets are needs further study

  17. Predicting prognosis of rectal cancer patients with total mesorectal excision using molecular markers

    Institute of Scientific and Technical Information of China (English)

    Jun-Jie Peng; San-Jun Cai; Hong-Feng Lu; Guo-Xiang Cai; Peng Lian; Zu-Qing Guan; Ming-He Wang; Ye Xu

    2007-01-01

    AIM: To explore the prognostic variables in rectal cancer patients undergoing curative total mesorectal excision and the effect of postoperative chemotherapy in advanced rectal cancer.METHODS: A total of 259 consecutive rectal cancer patients treated with curative total mesorectal excision between 1999 and 2004 were collected, p53, p21, PCNA,and CD44v6 were examined using immunohistochemistry (IHC). The correlation between clinicopathological or molecular variables and clinical outcomes, including local recurrence, metastasis, disease-free survival and overall survival, was analyzed.RESULTS: The median follow-up was 44 mo. Fiveyear survival rates and 5-year disease free survival rates were 75.43% and 70.32%, respectively. Multi-analysis revealed TNM staging, preoperative CEA, and CD44v6 level were independent risk factors predicting overall survival or disease free survival. The hazard ratio of peroperative CEA was 2.65 (95% CI 1.4-5) and 3.10 (95% CI 1.37-6.54) for disease free survival and overall survival, respectively. The hazard ratio of CD44v6 was 1.93 (95% CI 1.04-3.61) and 2.21 (95% CI 1.01-4.88)for disease free survival and overall survival, respectively.TNM staging was the only risk factor predicting local recurrence. Postoperative chemotherapy without radiotherapy did not improve patients' outcome.CONCLUSION: TNM staging, preoperative CEA and CD44v6 were independent prognostic factors for rectal cancer patients with total mesorectal excision.Postoperative chemotherapy may be only used together with radiotherapy for rectal cancer patients.

  18. Accuracy of a nomogram for prediction of lymph-node metastasis detected with conventional histopathology and ultrastaging in endometrial cancer

    OpenAIRE

    Koskas, M.; Chereau, E; Ballester, M.; Dubernard, G; Lécuru, F; Heitz, D; Mathevet, P.; Marret, H; Querleu, D; Golfier, F.; Leblanc, E.; D. Luton; Rouzier, R; Daraï, E

    2013-01-01

    Background: We developed a nomogram based on five clinical and pathological characteristics to predict lymph-node (LN) metastasis with a high concordance probability in endometrial cancer. Sentinel LN (SLN) biopsy has been suggested as a compromise between systematic lymphadenectomy and no dissection in patients with low-risk endometrial cancer. Methods: Patients with stage I–II endometrial cancer had pelvic SLN and systematic pelvic-node dissection. All LNs were histopathologically examined,...

  19. Diagnostic value of CYFRA 21-1 and CEA for predicting lymph node metastasis in operable lung cancer

    OpenAIRE

    Chen, Feng; Yan, Cui-E; Li, Jia; Han, Xiao-Hong; Wang, Hai; Qi, Jun

    2015-01-01

    Tumour markers are used extensively for the management of lung cancer, including diagnosis, evaluating effectiveness of treatments, monitoring recurrence after therapy and for predicting prognosis. However, there exists a knowledge gap regarding potential quantitative correlations between tumour marker levels and the extents of lymph node involvement in primary lung cancer. The current study is comprised of 139 lung cancer patients scheduled to undergo surgical operation. Of the 139 patients,...

  20. Combination of Circulating Tumor Cells with Serum Carcinoembryonic Antigen Enhances Clinical Prediction of Non-Small Cell Lung Cancer

    OpenAIRE

    Xi Chen; Xu Wang; Hua He; Ziling Liu; Ji-Fan Hu; Wei Li

    2015-01-01

    Circulating tumor cells (CTCs) have emerged as a potential biomarker in the diagnosis, prognosis, treatment, and surveillance of lung cancer. However, CTC detection is not only costly, but its sensitivity is also low, thus limiting its usage and the collection of robust data regarding the significance of CTCs in lung cancer. We aimed to seek clinical variables that enhance the prediction of CTCs in patients with non-small cell lung cancer (NSCLC). Clinical samples and pathological data were c...

  1. Diffusion-weighted magnetic resonance imaging for the prediction of pathologic response to neoadjuvant chemoradiotherapy in esophageal cancer.

    NARCIS (Netherlands)

    Rossum, P.S. van; Lier, A.L. van; Vulpen, M. van; Reerink, O.; Lagendijk, J.J.; Lin, S.H.; Hillegersberg, R. van; Ruurda, J.P.; Meijer, G.J.; Lips, I.M.

    2015-01-01

    PURPOSE: To explore the value of diffusion-weighted magnetic resonance imaging (DW-MRI) for the prediction of pathologic response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer. MATERIAL AND METHODS: In 20 patients receiving nCRT for esophageal cancer DW-MRI scanning was performed befo

  2. CHRNA5 risk variant predicts delayed smoking cessation and earlier lung cancer diagnosis--a meta-analysis

    NARCIS (Netherlands)

    Chen, L.S.; Hung, R.J.; Baker, T.; Horton, A.; Culverhouse, R.; Saccone, N.; Cheng, I.; Deng, B.; Han, Y.; Hansen, H.M.; Horsman, J.; Kim, C.; Lutz, S.; Rosenberger, A.; Aben, K.K.H.; Andrew, A.S.; Breslau, N.; Chang, S.C.; Dieffenbach, A.K.; Dienemann, H.; Frederiksen, B.; Han, J.; Hatsukami, D.K.; Johnson, E.O.; Pande, M.; Wrensch, M.R.; McLaughlin, J.; Skaug, V.; Heijden, H.F. van der; Wampfler, J.; Wenzlaff, A.; Woll, P.; Zienolddiny, S.; Bickeboller, H.; Brenner, H.; Duell, E.J.; Haugen, A.; Heinrich, J.; Hokanson, J.E.; Hunter, D.J.; Kiemeney, B.; Lazarus, P.; Marchand, L. Le; Liu, G.; Mayordomo, J.; Risch, A.; Schwartz, A.G.; Teare, D.; Wu, X.; Wiencke, J.K.; Yang, P.; Zhang, Z.F.; Spitz, M.R.; Kraft, P.; Amos, C.I.; Bierut, L.J.

    2015-01-01

    BACKGROUND: Recent meta-analyses show strong evidence of associations among genetic variants in CHRNA5 on chromosome 15q25, smoking quantity, and lung cancer. This meta-analysis tests whether the CHRNA5 variant rs16969968 predicts age of smoking cessation and age of lung cancer diagnosis. METHODS: M

  3. Preoperative Nomograms for Predicting Extracapsular Extension in Korean Men with Localized Prostate Cancer: A Multi-institutional Clinicopathologic Study

    OpenAIRE

    Chung, Jae Seung; Choi, Han Yong; Song, Hae-Ryoung; Byun, Seok-Soo; Seo, Seong Il; Song, Cheryn; Cho, Jin Seon; Lee, Sang Eun; Ahn, Hanjong; Lee, Eun Sik; Kim, Won-Jae; Chung, Moon Kee; Jung, Tae Young; Yu, Ho Song; Choi, Young Deuk

    2010-01-01

    We developed a nomogram to predict the probability of extracapsular extension (ECE) in localized prostate cancer and to determine when the neurovascular bundle (NVB) may be spared. Total 1,471 Korean men who underwent radical prostatectomy for prostate cancer between 1995 and 2008 were included. We drew nonrandom samples of 1,031 for nomogram development, leaving 440 samples for nomogram validation. With multivariate logistic regression analyses, we made a nomogram to predicts the ECE probabi...

  4. 可配置网络式软件系统的可用性预计研究%Research on Reconfiguration Networked Software System Availability Prediction

    Institute of Scientific and Technical Information of China (English)

    杨格兰; 孟令中

    2012-01-01

    Based on modeling and simulation for complex systems, a new approach to predict networked software system availability was proposed by multi-agent system modeling and simulation. Firstly, the method of multi-agent system modeling and simulation was introduced. Secondly, the characteristics of reconfiguration networked software system were analyzed. And then after researching on the approach to multi-agent based modeling and simulation for networked software system and behavioral models of reconfiguration, the new strategy which can be used for availability prediction was addressed. Finally,in order to verify the effectiveness,a case study was taken based on the approach,which was realized on the Netlogo simulation platform.%在复杂系统的建模与仿真研究的基础上,提出了一种基于多Agent的可配置网络式软件系统的可用性预计方法.首先介绍了多Agent系统建模与仿真方法;其次分析了可配置网络式软件系统的特点;然后在研究基于多Agent的网络式软件系统建模与仿真的基础上,研究可配置的行为模型,并建立了基于多Agent的可配置网络式软件系统可用性仿真方法;最后利用Netlogo仿真平台,结合实例对可配置的作用进行了可用性预计,并验证了本方法的有效性.

  5. [{sup 18}F]FDG-PET predicts complete pathological response of breast cancer to neoadjuvant chemotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Berriolo-Riedinger, Alina; Touzery, Claude; Riedinger, Jean-Marc; Toubeau, Michel; Boichot, Christophe; Cochet, Alexandre [Centre Georges Francois Leclerc, Department of Nuclear Medicine, Dijon (France); Coudert, Bruno; Fumoleau, Pierre [Centre Georges Francois Leclerc, Department of Medical Oncology, Dijon (France); Arnould, Laurent [Centre Georges Francois Leclerc, Department of Pathology, Dijon (France); Brunotte, Francois [Centre Georges Francois Leclerc, Department of Nuclear Medicine, Dijon (France); CNRS UMR 5158, Dijon (France)

    2007-12-15

    To evaluate, in breast cancer patients treated by neoadjuvant chemotherapy, the predictive value of reduction in FDG uptake with regard to complete pathological response (pCR). Forty-seven women with non-metastatic, non-inflammatory, large or locally advanced breast cancer were included. Tumour uptake of FDG was evaluated before and after the first course of neoadjuvant chemotherapy. Four indices were used: maximal and average SUV without or with correction by body surface area and glycaemia (SUV{sub max}, SUV{sub avg}, SUV{sub max-BSA-G} and SUV{sub avg-BSA-G}, respectively). The predictive value of reduction in FDG uptake with respect to pCR was studied by logistic regression analysis. Relationships between baseline [{sup 18}F]FDG uptake and prognostic parameters were assessed. The relative decrease in FDG uptake ({delta}SUV) after the first course of neoadjuvant chemotherapy was significantly greater in the pCR group than in the non-pCR group (p < 0.000066). The four FDG uptake indices were all strongly correlated with each other. A decrease in SUV{sub max-BSA-G} of 85.4% {+-} 21.9% was found in pCR patients, versus 22.6% {+-} 36.6% in non-pCR patients. {delta}SUV{sub max-BSA-G} <-60% predicted the pCR with an accuracy of 87% and {delta}SUVs were found to be only factors predictive of the pCR at multivariate analysis. An elevated baseline SUV was associated with high mitotic activity (p < 0.0016), tumour grading (p < 0.004), high nuclear pleomorphism score (p < 0.03) and negative hormonal receptor status (p < 0.005). In breast cancer patients, after only one course of neoadjuvant chemotherapy the reduction in FDG uptake is an early and powerful predictor of pCR. (orig.)

  6. Prognostic and Predictive Biomarkers of Endocrine Responsiveness for Estrogen Receptor Positive Breast Cancer.

    Science.gov (United States)

    Ma, Cynthia X; Bose, Ron; Ellis, Matthew J

    2016-01-01

    The estrogen-dependent nature of breast cancer is the fundamental basis for endocrine therapy. The presence of estrogen receptor (ER), the therapeutic target of endocrine therapy, is a prerequisite for this therapeutic approach. However, estrogen-independent growth often exists de novo at diagnosis or develops during the course of endocrine therapy. Therefore ER alone is insufficient in predicting endocrine therapy efficacy. Several RNA-based multigene assays are now available in clinical practice to assess distant recurrence risk, with majority of these assays evaluated in patients treated with 5 years of adjuvant endocrine therapy. While MammaPrint and Oncotype Dx are most predictive of recurrence risk within the first 5 years of diagnosis, Prosigna, Breast Cancer Index (BCI), and EndoPredict Clin have also demonstrated utility in predicting late recurrence. In addition, PAM50, or Prosigna, provides further biological insights by classifying breast cancers into intrinsic molecular subtypes. Additional strategies are under investigation in prospective clinical trials to differentiate endocrine sensitive and resistant tumors and include on-treatment Ki-67 and Preoperative Endocrine Prognostic Index (PEPI) score in the setting of neoadjuvant endocrine therapy. These biomarkers have become important tools in clinical practice for the identification of low risk patients for whom chemotherapy could be avoided. However, there is much work ahead toward the development of a molecular classification that informs the biology and novel therapeutic targets in high-risk disease as chemotherapy has only modest benefit in this population. The recognition of somatic mutations and their relationship to endocrine therapy responsiveness opens important opportunities toward this goal. PMID:26987533

  7. Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.

    Directory of Open Access Journals (Sweden)

    Jing Tang

    Full Text Available A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced

  8. Irregular menses predicts ovarian cancer: Prospective evidence from the Child Health and Development Studies.

    Science.gov (United States)

    Cirillo, Piera M; Wang, Erica T; Cedars, Marcelle I; Chen, Lee-May; Cohn, Barbara A

    2016-09-01

    We tested the hypothesis that irregular menstruation predicts lower risk for ovarian cancer, possibly due to less frequent ovulation. We conducted a 50-year prospective study of 15,528 mothers in the Child Health and Development Studies cohort recruited from the Kaiser Foundation Health Plan from 1959 to 1966. Irregular menstruation was classified via medical record and self-report at age 26. We identified 116 cases and 84 deaths due to ovarian cancer through 2011 via linkage to the California Cancer Registry and Vital Statistics. Contrary to expectation, women with irregular menstrual cycles had a higher risk of ovarian cancer incidence and mortality over the 50-year follow-up. Associations increased with age (p age 70 (95% confidence interval [CI] = 1.1, 3.4) rising to a 3-fold increase by age 77 (95% CI = 1.5, 6.7 for incidence; 95% CI = 1.4, 5.9 for mortality). We also found a 3-fold higher risk of mortality for high-grade serous tumors (95% CI = 1.3, 7.6) that did not vary by age. This is the first prospective study to show an association between irregular menstruation and ovarian cancer-we unexpectedly found higher risk for women with irregular cycles. These women are easy to identify and many may have polycystic ovarian syndrome. Classifying high-risk phenotypes such as irregular menstruation creates opportunities to find novel early biomarkers, refine clinical screening protocols and potentially develop new risk reduction strategies. These efforts can lead to earlier detection and better survival for ovarian cancer. PMID:27082375

  9. Prediction and experimental validation of novel STAT3 target genes in human cancer cells.

    Directory of Open Access Journals (Sweden)

    Young Min Oh

    Full Text Available The comprehensive identification of functional transcription factor binding sites (TFBSs is an important step in understanding complex transcriptional regulatory networks. This study presents a motif-based comparative approach, STAT-Finder, for identifying functional DNA binding sites of STAT3 transcription factor. STAT-Finder combines STAT-Scanner, which was designed to predict functional STAT TFBSs with improved sensitivity, and a motif-based alignment to minimize false positive prediction rates. Using two reference sets containing promoter sequences of known STAT3 target genes, STAT-Finder identified functional STAT3 TFBSs with enhanced prediction efficiency and sensitivity relative to other conventional TFBS prediction tools. In addition, STAT-Finder identified novel STAT3 target genes among a group of genes that are over-expressed in human cancer cells. The binding of STAT3 to the predicted TFBSs was also experimentally confirmed through chromatin immunoprecipitation. Our proposed method provides a systematic approach to the prediction of functional TFBSs that can be applied to other TFs.

  10. Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer

    Directory of Open Access Journals (Sweden)

    Gabere MN

    2016-06-01

    Full Text Available Musa Nur Gabere,1 Mohamed Aly Hussein,1 Mohammad Azhar Aziz2 1Department of Bioinformatics, King Abdullah International Medical Research Center/King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 2Colorectal Cancer Research Program, Department of Medical Genomics, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia Purpose: There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC. The selection of important features is a crucial step before training a classifier.Methods: In this study, we built a model that uses support vector machine (SVM to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid.Results: The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF, Bayes net (BN, multilayer perceptron (MLP, naïve Bayes (NB, reduced error pruning tree (REPT, and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP. Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1

  11. Predicting the continuous values of breast cancer relapse time by type-2 fuzzy logic system.

    Science.gov (United States)

    Mahmoodian, Hamid

    2012-06-01

    Microarray analysis and gene expression profile have been widely used in tumor classification, survival analysis and ER statues of breast cancer. Sample discrimination as well as identification of significant genes have been the focus of most previous studies. The aim of this research is to propose a fuzzy model to predict the relapse time of breast cancer by using breast cancer dataset published by van't Veer. Fuzzy rule mining based on support vector machine has been used in a hybrid method with rule pruning and shown its ability to divide the samples in many subgroups. To handle the existence of uncertainties in linguistic variables and fuzzy sets, the TSK model of Interval type-2 fuzzy logic system has been used and a new simple method is also developed to consider the uncertainties of the rules which have been optimized by genetic algorithm. B632 validation method is applied to estimate the error of the model. The results with 95 % confidence interval show a reasonable accuracy in prediction.

  12. Gene Co-Expression Analysis Predicts Genetic Variants Associated with Drug Responsiveness in Lung Cancer.

    Science.gov (United States)

    Shroff, Sanaya; Zhang, Jie; Huang, Kun

    2016-01-01

    Responsiveness to drugs is an important concern in designing personalized treatment for cancer patients. Currently genetic markers are often used to guide targeted therapy. However, deeper understanding of the molecular basis for drug responses and discovery of new predictive biomarkers for drug sensitivity are much needed. In this paper, we present a workflow for identifying condition-specific gene co-expression networks associated with responses to the tyrosine kinase inhibitor, Erlotinib, in lung adenocarcinoma cell lines using data from the Cancer Cell Line Encyclopedia by combining network mining and statistical analysis. Particularly, we have identified multiple gene modules specifically co-expressed in the drug responsive cell lines but not in the unresponsive group. Interestingly, most of these modules are enriched on specific cytobands, suggesting potential copy number variation events on these loci. Our results therefore imply that there are multiple genetic loci with copy number variations associated with the Erlotinib responses. The existence of CNVs in these loci is also confirmed in lung cancer tissue samples using the TCGA data. Since these structural variations are inferred from functional genomics data, these CNVs are functional variations. These results suggest the condition specific gene co- expression network mining approach is an effective approach in predicting candidate biomarkers for drug responses. PMID:27570645

  13. Circadian gene expression predicts patient response to neoadjuvant chemoradiation therapy for rectal cancer.

    Science.gov (United States)

    Lu, Haijie; Chu, Qiqi; Xie, Guojiang; Han, Hao; Chen, Zheng; Xu, Benhua; Yue, Zhicao

    2015-01-01

    Preoperative neoadjuvant chemoradiation therapy may be useful in patients with operable rectal cancer, but treatment responses are variable. We examined whether expression levels of circadian clock genes could be used as biomarkers to predict treatment response. We retrospectively analyzed clinical data from 250 patients with rectal cancer, treated with neoadjuvant chemoradiation therapy in a single institute between 2011 and 2013. Gene expression analysis (RT-PCR) was performed in tissue samples from 20 patients showing pathological complete regression (pCR) and 20 showing non-pCR. The genes analyzed included six core clock genes (Clock, Per1, Per2, Cry1, Cry2 and Bmal1) and three downstream target genes (Wee1, Chk2 and c-Myc). Patient responses were analyzed through contrast-enhanced pelvic MRI and endorectal ultrasound, and verified by histological assessment. pCR was defined histologically as an absence of tumor cells. Among the 250 included patients, 70.8% showed regression of tumor size, and 18% showed pCR. Clock, Cry2 and Per2 expressions were significantly higher in the pCR group than in the non-pCR group (PWee1 and Chk2 expression did not differ significantly between groups. Circadian genes are potential biomarkers for predicting whether a patient with rectal cancer would benefit from neoadjuvant chemoradiation therapy. PMID:26617816

  14. A Transcriptional Fingerprint of Estrogen in Human Breast Cancer Predicts Patient Survival

    Directory of Open Access Journals (Sweden)

    Jianjun Yu

    2008-01-01

    Full Text Available Estrogen signaling plays an essential role in breast cancer progression, and estrogen receptor (ER status has long been a marker of hormone responsiveness. However, ER status alone has been an incomplete predictor of endocrine therapy, as some ER+ tumors, nevertheless, have poor prognosis. Here we sought to use expression profiling of ER+ breast cancer cells to screen for a robust estrogen-regulated gene signature that may serve as a better indicator of cancer outcome. We identified 532 estrogen-induced genes and further developed a 73-gene signature that best separated a training set of 286 primary breast carcinomas into prognostic subtypes by stepwise cross-validation. Notably, this signature predicts clinical outcome in over 10 patient cohorts as well as their respective ER+ subcohorts. Further, this signature separates patients who have received endocrine therapy into two prognostic subgroups, suggesting its specificity as a measure of estrogen signaling, and thus hormone sensitivity. The 73-gene signature also provides additional predictive value for patient survival, independent of other clinical parameters, and outperforms other previously reported molecular outcome signatures. Taken together, these data demonstrate the power of using cell culture systems to screen for robust gene signatures of clinical relevance.

  15. Quantitative DCE-MRI for prediction of pathological complete response following neoadjuvant treatment for locally advanced breast cancer: the impact of breast cancer subtypes on the diagnostic accuracy

    International Nuclear Information System (INIS)

    To assess whether DCE-MRI pharmacokinetic (PK) parameters obtained before and during chemotherapy can predict pathological complete response (pCR) differently for different breast cancer groups. Eighty-four patients who received neoadjuvant chemotherapy for locally advanced breast cancer were retrospectively included. All patients underwent two DCE-MRI examinations, one before (EX1) and one during treatment (EX2). Tumours were classified into different breast cancer groups, namely triple negative (TNBC), HER2+ and ER+/HER2-, and compared with the whole population (WP). PK parameters Ktrans and Ve were extracted using a two-compartment Tofts model. At EX1, Ktrans predicted pCR for WP and TNBC. At EX2, maximum diameter (Dmax) predicted pCR for WP and ER+/HER2-. Both PK parameters predicted pCR in WP and TNBC and only Ktrans for the HER2+. pCR was predicted from relative difference (EX1 - EX2)/EX1 of Dmax and both PK parameters in the WP group and only for Ve in the TNBC group. No PK parameter could predict response for ER+/HER-. ROC comparison between WP and breast cancer groups showed higher but not statistically significant values for TNBC for the prediction of pCR Quantitative DCE-MRI can better predict pCR after neoadjuvant treatment for TNBC but not for the ER+/HER2- group. (orig.)

  16. Quantitative DCE-MRI for prediction of pathological complete response following neoadjuvant treatment for locally advanced breast cancer: the impact of breast cancer subtypes on the diagnostic accuracy

    Energy Technology Data Exchange (ETDEWEB)

    Drisis, Stylianos; Stathopoulos, Konstantinos; Chao, Shih-Li; Lemort, Marc [Institute Jules Bordet, Radiology Department, Brussels (Belgium); Metens, Thierry [Erasme University Hospital, Radiology Department, Brussels (Belgium); Ignatiadis, Michael [Institute Jules Bordet, Oncology Department, Brussels (Belgium)

    2016-05-15

    To assess whether DCE-MRI pharmacokinetic (PK) parameters obtained before and during chemotherapy can predict pathological complete response (pCR) differently for different breast cancer groups. Eighty-four patients who received neoadjuvant chemotherapy for locally advanced breast cancer were retrospectively included. All patients underwent two DCE-MRI examinations, one before (EX1) and one during treatment (EX2). Tumours were classified into different breast cancer groups, namely triple negative (TNBC), HER2+ and ER+/HER2-, and compared with the whole population (WP). PK parameters Ktrans and Ve were extracted using a two-compartment Tofts model. At EX1, Ktrans predicted pCR for WP and TNBC. At EX2, maximum diameter (Dmax) predicted pCR for WP and ER+/HER2-. Both PK parameters predicted pCR in WP and TNBC and only Ktrans for the HER2+. pCR was predicted from relative difference (EX1 - EX2)/EX1 of Dmax and both PK parameters in the WP group and only for Ve in the TNBC group. No PK parameter could predict response for ER+/HER-. ROC comparison between WP and breast cancer groups showed higher but not statistically significant values for TNBC for the prediction of pCR Quantitative DCE-MRI can better predict pCR after neoadjuvant treatment for TNBC but not for the ER+/HER2- group. (orig.)

  17. Response Assessment and Prediction in Esophageal Cancer Patients via F-18 FDG PET/CT Scans

    Science.gov (United States)

    Higgins, Kyle J.

    Purpose: The purpose of this study is to utilize F-18 FDG PET/CT scans to determine an indicator for the response of esophageal cancer patients during radiation therapy. There is a need for such an indicator since local failures are quite common in esophageal cancer patients despite modern treatment techniques. If an indicator is found, a patient's treatment strategy may be altered to possibly improve the outcome. This is investigated with various standard uptake volume (SUV) metrics along with image texture features. The metrics and features showing the most promise and indicating response are used in logistic regression analysis to find an equation for the prediction of response. Materials and Methods: 28 patients underwent F-18 FDG PET/CT scans prior to the start of radiation therapy (RT). A second PET/CT scan was administered following the delivery of ~32 Gray (Gy) of dose. A physician contoured gross tumor volume (GTV) was used to delineate a PET based GTV (GTV-pre-PET) based on a threshold of >40% and >20% of the maximum SUV value in the GTV. Deformable registration was used in VelocityAI software to register the pre-treatment and intra-treatment CT scans so that the GTV-pre-PET contours could be transferred from the pre to intra scans (GTV-intra-PET). The fractional decrease in the maximum, mean, volume to the highest intensity 10%-90%, and combination SUV metrics of the significant previous SUV metrics were compared to post-treatment pathologic response for an indication of response. Next for the >40% threshold, texture features based on a neighborhood gray-tone dimension matrix (NGTDM) were analyzed. The fractional decrease in coarseness, contrast, busyness, complexity, and texture strength were compared to the pathologic response of the patients. From these previous two types of analysis, SUV and texture features, the two most significant results were used in logistic regression analysis to find an equation to predict the probability of a non

  18. Prediction of gastric cancer metastasis through urinary metabolomic investigation using GC/MS

    Institute of Scientific and Technical Information of China (English)

    Jun-Duo Hu; Hui-Qing Tang; Qiang Zhang; Jing Fan; Jing Hong; Jian-Zhong Gu; Jin-Lian Chen

    2011-01-01

    AIM: To gain new insights into tumor metabolism and to identify possible biomarkers with potential diagnostic values to predict tumor metastasis.METHODS: Human gastric cancer SGC-7901 cells were implanted into 24 severe combined immune deficiency (SCID) mice, which were randomly divided into metastasis group (n = 8), non-metastasis group (n = 8), and normal group (n = 8). Urinary metabolomic information was obtained by gas chromatography/mass spectrometry (GC/MS).RESULTS: There were significant metabolic differences among the three groups (t test, P < 0.05). Ten selected metabolites were different between normal and cancer groups (non-metastasis and metastasis groups), and seven metabolites were also different between non-metastasis and metastasis groups. Two diagnostic models for gastric cancer and metastasis were constructed respectively by the principal component analysis (PCA). These PCA models were confirmed by corresponding receiver operating characteristic analysis (area under the curve = 1.00).CONCLUSION: The urinary metabolomic profile is different,and the selected metabolites might be instructive to clinical diagnosis or screening metastasis for gastric cancer.

  19. Elevated MED28 expression predicts poor outcome in women with breast cancer

    International Nuclear Information System (INIS)

    MED28 (also known as EG-1 and magicin) has been implicated in transcriptional control, signal regulation, and cell proliferation. MED28 has also been associated with tumor progression in in vitro and in vivo models. Here we examined the association of MED28 expression with human breast cancer progression. Expression of MED28 protein was determined on a population basis using a high-density tissue microarray consisting of 210 breast cancer patients. The association and validation of MED28 expression with histopathological subtypes, clinicopathological variables, and disease outcome was assessed. MED28 protein expression levels were increased in ductal carcinoma in situ and invasive ductal carcinoma of the breast compared to non-malignant glandular and ductal epithelium. Moreover, MED28 was a predictor of disease outcome in both univariate and multivariate analyses with higher expression predicting a greater risk of disease-related death. We have demonstrated that MED28 expression is increased in breast cancer. In addition, although the patient size was limited (88 individuals with survival information) MED28 is a novel and strong independent prognostic indicator of survival for breast cancer

  20. Can exhaled NO fraction predict radiotherapy-induced lung toxicity in lung cancer patients?

    International Nuclear Information System (INIS)

    A large increase in nitric oxide fraction (FeNO) after radiotherapy (RT) for lung cancer may predict RT-induced lung toxicity. In this study, we assessed the relationships between FeNO variations and respiratory symptoms, CT scan changes or dose volume histogram (DVH) parameters after RT. We measured FeNO before RT, 4, 5, 6, 10 weeks, 4 and 7.5 months after RT in 65 lung cancer patients. Eleven lung cancer patients (17%) complained of significant respiratory symptoms and 21 (31%) had radiation pneumonitis images in >1/3 of the irradiated lung after RT. Thirteen patients (20%) showed increases in FeNO >10 ppb. The sensitivity and specificity of a >10 ppb FeNO increase for the diagnosis of RT-associated respiratory symptoms were 18% and 83%, respectively. There was no correlation between DVH parameters or CT scan changes after RT and FeNO variations. Three patients (5%) showed intriguingly strong (2 or 3-fold, up to 55 ppb) and sustained increases in FeNO at 4 and 5 weeks, followed by significant respiratory symptoms and/or radiation-pneumonitis images. Serial FeNO measurements during RT had a low ability to identify lung cancer patients who developed symptoms or images of radiation pneumonitis. However, three patients presented with a particular pattern which deserves to be investigated

  1. KOHBRA BRCA risk calculator (KOHCal): a model for predicting BRCA1 and BRCA2 mutations in Korean breast cancer patients.

    Science.gov (United States)

    Kang, Eunyoung; Park, Sue K; Lee, Jong Won; Kim, Zisun; Noh, Woo-Chul; Jung, Yongsik; Yang, Jung-Hyun; Jung, Sung Hoo; Kim, Sung-Won

    2016-05-01

    The widely used Western BRCA mutation prediction models underestimated the risk of having a BRCA mutation in Korean breast cancer patients. This study aimed to identify predictive factors for BRCA1/2 mutations and to develop a Korean BRCA risk calculator. The model was constructed by logistic regression model, and it was based on the Korean Hereditary Breast Cancer study, in which 1669 female patients were enrolled between May 2007 and December 2010. A separate data set of 402 patients, who were enrolled from Jan 2011 to August 2012, was used to test the performance of our model. In total, 264 (15.8%) and 67 (16.7%) BRCA mutation carriers were identified in the model and validation set, respectively. Multivariate analysis showed that age at breast cancer diagnosis, bilateral breast cancer, triple-negative breast cancer (TNBC) and the number of relatives with breast or ovarian cancer within third-degree relatives were independent predictors of the BRCA mutation among familial breast cancer patients. An age cancer, both breast and ovarian cancer and TNBC remained significant predictors in non-familial breast cancer cases. Our model was developed based on logistic regression models. The validation results showed no differences between the observed and expected carrier probabilities. This model will be a useful tool for providing genetic risk assessments in Korean populations. PMID:26763880

  2. Logistic曲线在软件开发质量预测中的应用研究%ON APPLYING LOGISTIC CURVE IN SOFTWARE DEVELOPMENT QUALITY PREDICTION

    Institute of Scientific and Technical Information of China (English)

    晏明

    2014-01-01

    影响软件质量的因素除了开发方式多种多样外,还受其他因素影响。对于多阶段、不断开发、不断测试的软件开发项目,跟踪项目整体的测试质量对项目的质量控制有重要意义。研究发现软件开发项目中测试出的缺陷累计值的时间曲线基本符合Lo-gistic与Gompertz函数曲线。采用VBA编程,遍历所有实测数据的三点可求解出实测数据分别与两条函数曲线拟合度最好(最小2乘法)的三个曲线参数( L,b,a)。其中Logistic曲线的L值(即饱和值)可用于预测软件开发项目系统稳定时的缺陷累计值。通过分析软件项目开发中及系统发布运行后的累计缺陷的实测值与函数曲线(三个参数决定的曲线)的预测值,发现该函数曲线可用于预测及监控软件开发过程中及系统发布后的软件质量。%The factors affecting the quality of software are also influenced by other complications apart from the diversity of development modes.For software development projects with multi-phases, constant developing and continual tests, to follow up the overall test quality of the project is of significance to the project quality control.Our study found that the shapes of the time curves of cumulative defect values tested in software development projects are basically in accord with the curves of Logistic and Gompertz function.By adopting VBA programming to traverse three points of all the measured data, three curve parameters ( L, b, a) of the measured data which have the best fitting degrees with two function curves ( the least squares) respectively can be computed.Among them the L value ( saturation value) of Logistic curve can be used to predict the cumulative defect values of the software development project system when it is stable.By analysing the measured cumulative defect values and the prediction values of the functional curves ( determined by three parameters) of the software project

  3. Coping Strategies of Southern Italian Women Predict Distress Following Breast Cancer Surgery

    Directory of Open Access Journals (Sweden)

    Rossana De Feudis

    2015-05-01

    Full Text Available The present study was aimed at investigating the role of coping strategies in predicting emotional distress following breast cancer, over and above the illness severity, operationalized in terms of the type of surgery performed. In order to achieve this goal, two groups of newly diagnosed breast cancer women were selected and compared on the basis of the type of surgical treatment received. A subsample of 30 women with quadrantectomy and sentinel lymph-node biopsy (SLNB and a subsample of 31 patients with mastectomy and axillary dissection (MAD filled in the Brief Cope scale and Hospital Anxiety and Depression Scale. Summarizing, results showed that emotional support, venting, and humor explained a statistically significant increment of variance in psychological distress indices. Implication for clinical practice and future research were discussed.

  4. Identification and targeting of a TACE-dependent autocrine loopwhich predicts poor prognosis in breast cancer

    Energy Technology Data Exchange (ETDEWEB)

    Kenny, Paraic A.; Bissell, Mina J.

    2005-06-15

    The ability to proliferate independently of signals from other cell types is a fundamental characteristic of tumor cells. Using a 3D culture model of human breast cancer progression, we have delineated a protease-dependent autocrine loop which provides an oncogenic stimulus in the absence of proto-oncogene mutation. Inhibition of this protease, TACE/ADAM17, reverts the malignant phenotype by preventing mobilization of two crucial growth factors, Amphiregulin and TGF{alpha}. We show further that the efficacy of EGFR inhibitors is overcome by physiological levels of growth factors and that successful EGFR inhibition is dependent on reducing ligand bioavailability. Using existing patient outcome data, we demonstrate a strong correlation between TACE and TGF{alpha} expression in human breast cancers that is predictive of poor prognosis.

  5. Role of perfusion SPECT in prediction and measurement of pulmonary complications after radiotherapy for lung cancer

    DEFF Research Database (Denmark)

    Farr, Katherina P; Kramer, Stine; Khalil, Azza A;

    2015-01-01

    radiotherapy (RT) for non-small-cell lung cancer (NSCLC). METHODS: Patients with NSCLC undergoing curative RT were included prospectively. Perfusion SPECT/CT and global pulmonary function tests (PFT) were performed before RT and four times during follow-up. Functional activity on SPECT was measured using......PURPOSE: The purpose of the study was to evaluate the ability of baseline perfusion defect score (DS) on SPECT to predict the development of severe symptomatic radiation pneumonitis (RP) and to evaluate changes in perfusion on SPECT as a method of lung perfusion function assessment after curative...... a semiquantitative perfusion DS. Pulmonary morbidity was graded by the National Cancer Institute's Common Terminology Criteria for Adverse Events version 4 for pneumonitis. Patients were divided into two groups according to the severity of RP. RESULTS: A total of 71 consecutive patients were included in the study...

  6. Prediction of response to chemotherapy by ERCC1 immunohistochemistry and ERCC1 polymorphism in ovarian cancer

    DEFF Research Database (Denmark)

    Dahl Steffensen, Karina; Waldstrøm, M.; Jeppesen, Ulla;

    2007-01-01

    The response of tumor cells to platinum-based chemotherapy involves DNA repair mechanisms. Excision repair cross-complementation group 1 (ercc1) is one of the leading genes involved in DNA repair, and several studies have linked ercc1 to platinum resistance in cell lines and in human cancers....... A common single nucleotide polymorphism (SNP) of ercc1 at codon 118 has been proposed to impair ercc1 translation and reduce ERCC1 protein expression and consequently influence the response to platinum-based chemotherapy. The primary aim of the present study was to evaluate ERCC1 expression and ercc1 codon...... 118 polymorphism in epithelial ovarian cancer (EOC) and their possible predictive value in patients treated with platinum-based chemotherapy. Formalin-fixed, paraffin-embedded tissue sections from 159 patients with advanced EOC were used for immunohistochemistry. Ercc1 codon 118 SNP genotyping...

  7. Clinical Nomogram for Predicting Survival of Esophageal Cancer Patients after Esophagectomy

    Science.gov (United States)

    Cao, Jinlin; Yuan, Ping; Wang, Luming; Wang, Yiqing; Ma, Honghai; Yuan, Xiaoshuai; Lv, Wang; Hu, Jian

    2016-01-01

    The aim of this study was to construct an effective clinical nomogram for predicting the survival of esophageal cancer patients after esophagectomy. We identified esophageal cancer patients (n = 4,281) who underwent esophagectomy between 1988 and 2007 from the Surveillance, Epidemiology, and End Results (SEER) 18 registries database. Clinically significant parameters for survival were used to construct a nomogram based on Cox regression analyses. The model was validated using bootstrap resampling and a Chinese cohort (n = 145). A total of 4,109 patients from the SEER database were included for analysis. The multivariate analyses showed that the factors of age, race, histology, tumor site, tumor size, grade and depth of invasion, and the numbers of metastases and retrieved nodes were independent prognostic factors. All of these factors were selected into the nomogram. The nomogram showed a clear prognostic superiority over the seventh AJCC-TNM classification (C-index: SEER cohort, 0.716 vs 0.693, respectively; P nomogram predicted the probabilities of 3- and 5-year survival, which corresponded closely with the actual survival rates. This novel prognostic model may improve clinicians’ abilities to predict individualized survival and to make treatment recommendations. PMID:27215834

  8. Thyroid Hormones, Autoantibodies, Ultrasonography, and Clinical Parameters for Predicting Thyroid Cancer

    Science.gov (United States)

    He, Lin-zheng; Zeng, Tian-shu; Pu, Lin; Pan, Shi-xiu; Xia, Wen-fang; Chen, Lu-lu

    2016-01-01

    Our objective was to evaluate thyroid nodule malignancy prediction using thyroid function tests, autoantibodies, ultrasonographic imaging, and clinical data. We conducted a retrospective cohort study in 1400 patients with nodular thyroid disease (NTD). The thyroid stimulating hormone (TSH) concentration was significantly higher in patients with differentiated thyroid cancer (DTC) versus benign thyroid nodular disease (BTND) (p = 0.004). The receiver operating characteristic curve of TSH showed an AUC of 0.58 (95% CI 0.53–0.62, p = 0.001), sensitivity of 74%, and specificity of 57% at a cut-off of 1.59 mIU/L. There was an incremental increase in TSH concentration along with the increasing tumor size (p < 0.001). Thyroglobulin antibody (TgAb) concentration was associated with an increased risk of malignancy (p = 0.029), but this association was lost when the effect of TSH was taken into account (p = 0.11). Thyroid ultrasonographic characteristics, including fewer than three nodules, hypoechoic appearance, solid component, poorly defined margin, intranodular or peripheral-intranodular flow, and punctate calcification, can be used to predict the risk of thyroid cancer. In conclusion, our study suggests that preoperative serum TSH concentration, age, and ultrasonographic features can be used to predict the risk of malignancy in patients with NTD. PMID:27313612

  9. Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer

    International Nuclear Information System (INIS)

    Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures. Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients. The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways. We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different

  10. Pathological Gleason prediction through gland ring morphometry in immunofluorescent prostate cancer images

    Science.gov (United States)

    Scott, Richard; Khan, Faisal M.; Zeineh, Jack; Donovan, Michael; Fernandez, Gerardo

    2016-03-01

    The Gleason score is the most common architectural and morphological assessment of prostate cancer severity and prognosis. There have been numerous quantitative techniques developed to approximate and duplicate the Gleason scoring system. Most of these approaches have been developed in standard H and E brightfield microscopy. Immunofluorescence (IF) image analysis of tissue pathology has recently been proven to be extremely valuable and robust in developing prognostic assessments of disease, particularly in prostate cancer. There have been significant advances in the literature in quantitative biomarker expression as well as characterization of glandular architectures in discrete gland rings. In this work we leverage a new method of segmenting gland rings in IF images for predicting the pathological Gleason; both the clinical and the image specific grade, which may not necessarily be the same. We combine these measures with nuclear specific characteristics as assessed by the MST algorithm. Our individual features correlate well univariately with the Gleason grades, and in a multivariate setting have an accuracy of 85% in predicting the Gleason grade. Additionally, these features correlate strongly with clinical progression outcomes (CI of 0.89), significantly outperforming the clinical Gleason grades (CI of 0.78). This work presents the first assessment of morphological gland unit features from IF images for predicting the Gleason grade.

  11. Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer

    International Nuclear Information System (INIS)

    To validate, in the context of adaptive radiotherapy, three commercial software solutions for atlas-based segmentation. Fifteen patients, five for each group, with cancer of the Head&Neck, pleura, and prostate were enrolled in the study. In addition to the treatment planning CT (pCT) images, one replanning CT (rCT) image set was acquired for each patient during the RT course. Three experienced physicians outlined on the pCT and rCT all the volumes of interest (VOIs). We used three software solutions (VelocityAI 2.6.2 (V), MIM 5.1.1 (M) by MIMVista and ABAS 2.0 (A) by CMS-Elekta) to generate the automatic contouring on the repeated CT. All the VOIs obtained with automatic contouring (AC) were successively corrected manually. We recorded the time needed for: 1) ex novo ROIs definition on rCT; 2) generation of AC by the three software solutions; 3) manual correction of AC. To compare the quality of the volumes obtained automatically by the software and manually corrected with those drawn from scratch on rCT, we used the following indexes: overlap coefficient (DICE), sensitivity, inclusiveness index, difference in volume, and displacement differences on three axes (x, y, z) from the isocenter. The time saved by the three software solutions for all the sites, compared to the manual contouring from scratch, is statistically significant and similar for all the three software solutions. The time saved for each site are as follows: about an hour for Head&Neck, about 40 minutes for prostate, and about 20 minutes for mesothelioma. The best DICE similarity coefficient index was obtained with the manual correction for: A (contours for prostate), A and M (contours for H&N), and M (contours for mesothelioma). From a clinical point of view, the automated contouring workflow was shown to be significantly shorter than the manual contouring process, even though manual correction of the VOIs is always needed

  12. Socioeconomic patient characteristics predict delay in cancer diagnosis: a Danish cohort study

    Directory of Open Access Journals (Sweden)

    Sokolowski Ineta

    2008-02-01

    . Conclusion We found socioeconomic predictors of delay that allow us to hypothesize social inequalities in the distribution of delay, but, in general, only a few socioeconomic variables predicted delay in cancer diagnosis. Future research should examine a broader array of patients' personal characteristics.

  13. CREATION OF THE NOMOGRAM THAT PREDICTS PATHOLOGICAL LOCAL EXTENT OF THE BLADDER CANCER BASED ON CLINICAL VARIABLES

    OpenAIRE

    L. V. Mirylenka; O. G. Sukonko; A. V. Pravorov; A. I. Rolevich; A. S. Mavrichev

    2014-01-01

    Objective: to develop nomogram based on clinical variables, that predicts pathological local extent of the bladder cancer рТ3-рТ4 (рТ3+).Material and methods: We used data of 511 patients with bladder cancer, that have undergone radical cystectomy between 1999 and 2008 at N.N. Alexandrov National Cancer Centre. For prediction of pT3+ on preoperative data were used mono- and multivariate logistic regression analysis. Coefficients from logistic regression equalization were used to construct nom...

  14. Prediction of Female Breast Cancer Incidence among the Aging Society in Kanagawa, Japan

    Science.gov (United States)

    Katayama, Kayoko

    2016-01-01

    Owing to the increasing number of elderly “baby boomers” in Japan, the number of cancer patients is also expected to increase. Approximately 2 million baby boomers from nearby local areas are residing in metropolitan areas; hence, the geographical distribution of cancer patients will probably markedly change. We assessed the expected number of breast cancer (BC) patients in different regions (urban, outer city, town, rural) using estimates of the nation’s population and Kanagawa Cancer Registry data. To estimate future BC incidence for each region, we multiplied the 2010 rate by the predicted female population for each region according to age group. The incidence cases of BC in those aged ≥65 years is expected to increase in all areas; in particular, compared to rates in 2010, the BC incidence in urban areas was predicted to increase by 82.6% in 2035 and 102.2% in 2040. Although the incidence in all BC cases in urban areas showed an increasing trend, until peaking in 2040 (increasing 31.2% from 2010), the number of BC patients would continue to decrease in other areas. The number of BC patients per capita BC specialist was 64.3 patients in 2010; this value would increase from 59.3 in 2010 to 77.7 in 2040 in urban areas, but would decrease in other areas. Our findings suggest that the number of elderly BC patients is expected to increase rapidly in urban areas and that the demand for BC treatment would increase in the elderly population in urban areas. PMID:27532126

  15. Partial wave spectroscopic microscopy can predict prostate cancer progression and mitigate over-treatment (Conference Presentation)

    Science.gov (United States)

    Zhang, Di; Graff, Taylor; Crawford, Susan; Subramanian, Hariharan; Thompson, Sebastian; Derbas, Justin R.; Lyengar, Radha; Roy, Hemant K.; Brendler, Charles B.; Backman, Vadim

    2016-02-01

    Prostate Cancer (PC) is the second leading cause of cancer deaths in American men. While prostate specific antigen (PSA) test has been widely used for screening PC, >60% of the PSA detected cancers are indolent, leading to unnecessary clinical interventions. An alternative approach, active surveillance (AS), also suffer from high expense, discomfort and complications associated with repeat biopsies (every 1-3 years), limiting its acceptance. Hence, a technique that can differentiate indolent from aggressive PC would attenuate the harms from over-treatment. Combining microscopy with spectroscopy, our group has developed partial wave spectroscopic (PWS) microscopy, which can quantify intracellular nanoscale organizations (e.g. chromatin structures) that are not accessible by conventional microscopy. PWS microscopy has previously been shown to predict the risk of cancer in seven different organs (N ~ 800 patients). Herein we use PWS measurement of label-free histologically-normal prostatic epithelium to distinguish indolent from aggressive PC and predict PC risk. Our results from 38 men with low-grade PC indicated that there is a significant increase in progressors compared to non-progressors (p=0.002, effect size=110%, AUC=0.80, sensitivity=88% and specificity=72%), while the baseline clinical characteristics were not significantly different. We further improved the diagnostic power by performing nuclei-specific measurements using an automated system that separates in real-time the cell nuclei from the remaining prostate epithelium. In the long term, we envision that the PWS based prognostication can be coupled with AS without any change to the current procedure to mitigate the harms caused by over-treatment.

  16. Prognostic and predictive value of circulating tumor cell analysis in colorectal cancer patients

    Directory of Open Access Journals (Sweden)

    de Albuquerque Andreia

    2012-11-01

    Full Text Available Abstract Objective The aim of this study was to assess the prognostic and predictive values of circulating tumor cell (CTC analysis in colorectal cancer patients. Patients and methods Presence of CTCs was evaluated in 60 colorectal cancer patients before systemic therapy - from which 33 patients were also evaluable for CTC analysis during the first 3 months of treatment - through immunomagnetic enrichment, using the antibodies BM7 and VU1D9 (targeting mucin 1 and EpCAM, respectively, followed by real-time RT-PCR analysis of the tumor-associated genes KRT19, MUC1, EPCAM, CEACAM5 and BIRC5. Results Patients were stratified into groups according to CTC detection (CTC negative, when all marker genes were negative; and CTC positive when at least one of the marker genes was positive. Patients with CTC positivity at baseline had a significant shorter median progression-free survival (median PFS 181.0 days; 95% CI 146.9-215.1 compared with patients with no CTCs (median PFS 329.0 days; 95% CI 299.6-358.4; Log-rank P Conclusion The present study provides evidence of a strong correlation between CTC detection and radiographic disease progression in patients receiving chemotherapy for colorectal cancer. Our results suggest that in addition to the current prognostic factors, CTC analysis represent a potential complementary tool for prediction of colorectal cancer patients’ outcome. Moreover, the present test allows for molecular characterization of CTCs, which may be of relevance to the creation of personalized therapies.

  17. A tissue biomarker panel predicting systemic progression after PSA recurrence post-definitive prostate cancer therapy.

    Directory of Open Access Journals (Sweden)

    Tohru Nakagawa

    Full Text Available BACKGROUND: Many men develop a rising PSA after initial therapy for prostate cancer. While some of these men will develop a local or metastatic recurrence that warrants further therapy, others will have no evidence of disease progression. We hypothesized that an expression biomarker panel can predict which men with a rising PSA would benefit from further therapy. METHODOLOGY/PRINCIPAL FINDINGS: A case-control design was used to test the association of gene expression with outcome. Systemic (SYS progression cases were men post-prostatectomy who developed systemic progression within 5 years after PSA recurrence. PSA progression controls were matched men post-prostatectomy with PSA recurrence but no evidence of clinical progression within 5 years. Using expression arrays optimized for paraffin-embedded tissue RNA, 1021 cancer-related genes were evaluated-including 570 genes implicated in prostate cancer progression. Genes from 8 previously reported marker panels were included. A systemic progression model containing 17 genes was developed. This model generated an AUC of 0.88 (95% CI: 0.84-0.92. Similar AUCs were generated using 3 previously reported panels. In secondary analyses, the model predicted the endpoints of prostate cancer death (in SYS cases and systemic progression beyond 5 years (in PSA controls with hazard ratios 2.5 and 4.7, respectively (log-rank p-values of 0.0007 and 0.0005. Genes mapped to 8q24 were significantly enriched in the model. CONCLUSIONS/SIGNIFICANCE: Specific gene expression patterns are significantly associated with systemic progression after PSA recurrence. The measurement of gene expression pattern may be useful for determining which men may benefit from additional therapy after PSA recurrence.

  18. Nomogram including pretherapeutic parameters for prediction of survival after SIRT of hepatic metastases from colorectal cancer

    International Nuclear Information System (INIS)

    Pre-therapeutic prediction of outcome is important for clinicians and patients in determining whether selective internal radiation therapy (SIRT) is indicated for hepatic metastases of colorectal cancer (CRC). Pre-therapeutic characteristics of 100 patients with colorectal liver metastases (CRLM) treated by radioembolization were analyzed to develop a nomogram for predicting survival. Prognostic factors were selected by univariate Cox regression analysis and subsequent tested by multivariate analysis for predicting patient survival. The nomogram was validated with reference to an external patient cohort (n = 25) from the Bonn University Department of Nuclear Medicine. Of the 13 parameters tested, four were independently associated with reduced patient survival in multivariate analysis. These parameters included no liver surgery before SIRT (HR:1.81, p = 0.014), CEA serum level ≥ 150 ng/ml (HR:2.08, p = 0.001), transaminase toxicity level ≥2.5 x upper limit of normal (HR:2.82, p = 0.001), and summed computed tomography (CT) size of the largest two liver lesions ≥10 cm (HR:2.31, p < 0.001). The area under the receiver-operating characteristic curve for our prediction model was 0.83 for the external patient cohort, indicating superior performance of our multivariate model compared to a model ignoring covariates. The nomogram developed in our study entailing four pre-therapeutic parameters gives good prediction of patient survival post SIRT. (orig.)

  19. Nomogram for predicting pathologically complete response after neoadjuvant chemoradiotherapy for oesophageal cancer

    International Nuclear Information System (INIS)

    Background: A pathologically complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) is seen in 30% of the patients with oesophageal cancer. The aim is to identify patient and tumour characteristics associated with a pCR and to develop a nomogram for the prediction of pCR. Patients and methods: Patients who underwent nCRT followed by surgery were identified and response to nCRT was assessed according to a modified Mandard classification in the resection specimen. A model was developed with age, gender, histology and location of the tumour, differentiation grade, alcohol use, smoking, percentage weight loss, Charlson Comorbidity Index (CCI), cT-stage and cN-stage as potential predictors for pCR. Probability of pCR was studied via logistic regression. Performance of the prediction nomogram was quantified using the concordance statistic (c-statistic) and corrected for optimism. Results: A total of 381 patients were included. After surgery, 27.6% of the tumours showed a pCR. Female sex, squamous cell histology, poor differentiation grade, and low cT-stage were predictive for a pCR with a c-statistic of 0.64 (corrected for optimism). Conclusion: A nomogram for the prediction of pathologically complete response after neoadjuvant chemoradiotherapy was developed, with a reasonable predictive power. This nomogram needs external validation before it can be used for individualised clinical decision-making

  20. Nomogram including pretherapeutic parameters for prediction of survival after SIRT of hepatic metastases from colorectal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Fendler, Wolfgang Peter [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Klinik und Poliklinik fuer Nuklearmedizin, Munich (Germany); Ilhan, Harun [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Paprottka, Philipp M. [Ludwig-Maximilians-University of Munich, Department of Clinical Radiology, Munich (Germany); Jakobs, Tobias F. [Hospital Barmherzige Brueder, Department of Diagnostic and Interventional Radiology, Munich (Germany); Heinemann, Volker [Ludwig-Maximilians-University of Munich, Department of Internal Medicine III, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Bartenstein, Peter; Haug, Alexander R. [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Khalaf, Feras [University Hospital Bonn, Department of Nuclear Medicine, Bonn (Germany); Ezziddin, Samer [Saarland University Medical Center, Department of Nuclear Medicine, Homburg (Germany); Hacker, Marcus [Vienna General Hospital, Department of Nuclear Medicine, Vienna (Austria)

    2015-09-15

    Pre-therapeutic prediction of outcome is important for clinicians and patients in determining whether selective internal radiation therapy (SIRT) is indicated for hepatic metastases of colorectal cancer (CRC). Pre-therapeutic characteristics of 100 patients with colorectal liver metastases (CRLM) treated by radioembolization were analyzed to develop a nomogram for predicting survival. Prognostic factors were selected by univariate Cox regression analysis and subsequent tested by multivariate analysis for predicting patient survival. The nomogram was validated with reference to an external patient cohort (n = 25) from the Bonn University Department of Nuclear Medicine. Of the 13 parameters tested, four were independently associated with reduced patient survival in multivariate analysis. These parameters included no liver surgery before SIRT (HR:1.81, p = 0.014), CEA serum level ≥ 150 ng/ml (HR:2.08, p = 0.001), transaminase toxicity level ≥2.5 x upper limit of normal (HR:2.82, p = 0.001), and summed computed tomography (CT) size of the largest two liver lesions ≥10 cm (HR:2.31, p < 0.001). The area under the receiver-operating characteristic curve for our prediction model was 0.83 for the external patient cohort, indicating superior performance of our multivariate model compared to a model ignoring covariates. The nomogram developed in our study entailing four pre-therapeutic parameters gives good prediction of patient survival post SIRT. (orig.)

  1. Can Mathematical Models Predict the Outcomes of Prostate Cancer Patients Undergoing Intermittent Androgen Deprivation Therapy?

    Science.gov (United States)

    Everett, R. A.; Packer, A. M.; Kuang, Y.

    Androgen deprivation therapy is a common treatment for advanced or metastatic prostate cancer. Like the normal prostate, most tumors depend on androgens for proliferation and survival but often develop treatment resistance. Hormonal treatment causes many undesirable side effects which significantly decrease the quality of life for patients. Intermittently applying androgen deprivation in cycles reduces the total duration with these negative effects and may reduce selective pressure for resistance. We extend an existing model which used measurements of patient testosterone levels to accurately fit measured serum prostate specific antigen (PSA) levels. We test the model's predictive accuracy, using only a subset of the data to find parameter values. The results are compared with those of an existing piecewise linear model which does not use testosterone as an input. Since actual treatment protocol is to re-apply therapy when PSA levels recover beyond some threshold value, we develop a second method for predicting the PSA levels. Based on a small set of data from seven patients, our results showed that the piecewise linear model produced slightly more accurate results while the two predictive methods are comparable. This suggests that a simpler model may be more beneficial for a predictive use compared to a more biologically insightful model, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting. Nevertheless, both models are an important step in this direction.

  2. Chromosomal aberrations in lymphocytes predict human cancer: a report from the European Study Group on Cytogenetic Biomarkers and Health (ESCH)

    DEFF Research Database (Denmark)

    Hagmar, L; Bonassi, S; Strömberg, U;

    1998-01-01

    . No association was seen between the SCEs or the MN frequencies and subsequent cancer incidence/mortality. The present study further supports our previous observation on the cancer predictivity of the CA biomarker, which seems to be independent of age at test, gender, and time since test. The risk patterns were...... similar within each national cohort. This result suggests that the frequency of CAs in peripheral blood lymphocytes is a relevant biomarker for cancer risk in humans, reflecting either early biological effects of genotoxic carcinogens or individual cancer susceptibility....

  3. Use of Germline Polymorphisms in Predicting Concurrent Chemoradiotherapy Response in Esophageal Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Pei-Chun [Department of Statistics and Informatics Science, Providence University, Taiwan (China); Chen, Yen-Ching [Institute of Epidemiology Preventive Medicine, College of Public Health, National Taiwan University, Taiwan (China); Research Center for Gene, Environment, and Human Health, College of Public Health, National Taiwan University, Taiwan (China); Department of Public Health, Institute of Epidemiology, National Taiwan University, Taiwan (China); Lai, Liang-Chuan [Graduate Institute of Physiology, National Taiwan University, Taiwan (China); Tsai, Mong-Hsun [Institute of Biotechnology, National Taiwan University, Taiwan (China); Chen, Shin-Kuang [National Clinical Trial and Research Center, National Taiwan University Hospital, Taiwan (China); Yang, Pei-Wen; Lee, Yung-Chie [Department of Surgery, National Taiwan University Hospital, Taiwan (China); Hsiao, Chuhsing K. [Research Center for Gene, Environment, and Human Health, College of Public Health, National Taiwan University, Taiwan (China); Department of Public Health, Institute of Epidemiology, National Taiwan University, Taiwan (China); Bioinformatics and Biostatistics Core, Research Center for Medical Excellence, National Taiwan University, Taiwan (China); Lee, Jang-Ming, E-mail: jangming@ntuh.gov.tw [Department of Surgery, National Taiwan University Hospital, Taiwan (China); Chuang, Eric Y., E-mail: chuangey@ntu.edu.tw [National Clinical Trial and Research Center, National Taiwan University Hospital, Taiwan (China); Bioinformatics and Biostatistics Core, Research Center for Medical Excellence, National Taiwan University, Taiwan (China); Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taiwan (China)

    2012-04-01

    Purpose: To identify germline polymorphisms to predict concurrent chemoradiation therapy (CCRT) response in esophageal cancer patients. Materials and Methods: A total of 139 esophageal cancer patients treated with CCRT (cisplatin-based chemotherapy combined with 40 Gy of irradiation) and subsequent esophagectomy were recruited at the National Taiwan University Hospital between 1997 and 2008. After excluding confounding factors (i.e., females and patients aged {>=}70 years), 116 patients were enrolled to identify single nucleotide polymorphisms (SNPs) associated with specific CCRT responses. Genotyping arrays and mass spectrometry were used sequentially to determine germline polymorphisms from blood samples. These polymorphisms remain stable throughout disease progression, unlike somatic mutations from tumor tissues. Two-stage design and additive genetic models were adopted in this study. Results: From the 26 SNPs identified in the first stage, 2 SNPs were found to be significantly associated with CCRT response in the second stage. Single nucleotide polymorphism rs16863886, located between SGPP2 and FARSB on chromosome 2q36.1, was significantly associated with a 3.93-fold increase in pathologic complete response to CCRT (95% confidence interval 1.62-10.30) under additive models. Single nucleotide polymorphism rs4954256, located in ZRANB3 on chromosome 2q21.3, was associated with a 3.93-fold increase in pathologic complete response to CCRT (95% confidence interval 1.57-10.87). The predictive accuracy for CCRT response was 71.59% with these two SNPs combined. Conclusions: This is the first study to identify germline polymorphisms with a high accuracy for predicting CCRT response in the treatment of esophageal cancer.

  4. Predicting Pelvic Lymph Node Involvement in Current-Era Prostate Cancer

    International Nuclear Information System (INIS)

    Purpose: The Roach formula [2/3 × prostate-specific antigen + (Gleason score – 6) × 10], derived in 1993 during the early prostate specific antigen (PSA) screening era, has been used to predict the risk of pelvic lymph node involvement in patients with prostate cancer. In the current era of widespread PSA screening with a shift to earlier disease stages, there is evidence to suggest that the Roach score overestimates risk of nodal metastasis. This study retrospectively reviews the validity of this formula as a prediction tool. Methods and Materials: We conducted a retrospective institutional review including men with clinical T1c–T3 prostate cancer, with baseline PSA levels and biopsy-obtained Gleason scores who underwent radical prostatectomy with pelvic node dissection from 2001 through 2009 (N = 1,022). The predicted risk of nodal involvement was calculated for each patient using the Roach formula and then compared with actual rates following surgery. Results: The study included 1,022 patients; 99.6% had clinical T1c/T2 disease, with a mean of 10.3 lymph nodes surgically evaluated. Overall, 42 patients (4.1%) had nodal metastasis. For every range of scores, the Roach formula overestimates the risk of nodal involvement. Observed nodal positivity was 1%, 6.3%, 10%, 15.2%, and 16.7% for Roach scores ≤10%, >10%–20%, >20%–30%, >30%–40%, and >40%, respectively. The Roach score overestimates the risk by approximately 4.5-fold in patients with scores ≤10%, by 2.5-fold for all scores between 10% and 40%, and by 4-fold for scores >40%. Conclusion: The Roach formula overpredicts the risk of pelvic nodal involvement in current-era prostate cancer patients undergoing regular PSA screening and with mainly T1c/T2 disease. Contemporary patients are much less likely to have nodal involvement for a given PSA and Gleason score.

  5. Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer

    Science.gov (United States)

    Bychkov, Dmitrii; Turkki, Riku; Haglund, Caj; Linder, Nina; Lundin, Johan

    2016-03-01

    Recent advances in computer vision enable increasingly accurate automated pattern classification. In the current study we evaluate whether a convolutional neural network (CNN) can be trained to predict disease outcome in patients with colorectal cancer based on images of tumor tissue microarray samples. We compare the prognostic accuracy of CNN features extracted from the whole, unsegmented tissue microarray spot image, with that of CNN features extracted from the epithelial and non-epithelial compartments, respectively. The prognostic accuracy of visually assessed histologic grade is used as a reference. The image data set consists of digitized hematoxylin-eosin (H and E) stained tissue microarray samples obtained from 180 patients with colorectal cancer. The patient samples represent a variety of histological grades, have data available on a series of clinicopathological variables including long-term outcome and ground truth annotations performed by experts. The CNN features extracted from images of the epithelial tissue compartment significantly predicted outcome (hazard ratio (HR) 2.08; CI95% 1.04-4.16; area under the curve (AUC) 0.66) in a test set of 60 patients, as compared to the CNN features extracted from unsegmented images (HR 1.67; CI95% 0.84-3.31, AUC 0.57) and visually assessed histologic grade (HR 1.96; CI95% 0.99-3.88, AUC 0.61). As a conclusion, a deep-learning classifier can be trained to predict outcome of colorectal cancer based on images of H and E stained tissue microarray samples and the CNN features extracted from the epithelial compartment only resulted in a prognostic discrimination comparable to that of visually determined histologic grade.

  6. Use of Germline Polymorphisms in Predicting Concurrent Chemoradiotherapy Response in Esophageal Cancer

    International Nuclear Information System (INIS)

    Purpose: To identify germline polymorphisms to predict concurrent chemoradiation therapy (CCRT) response in esophageal cancer patients. Materials and Methods: A total of 139 esophageal cancer patients treated with CCRT (cisplatin-based chemotherapy combined with 40 Gy of irradiation) and subsequent esophagectomy were recruited at the National Taiwan University Hospital between 1997 and 2008. After excluding confounding factors (i.e., females and patients aged ≥70 years), 116 patients were enrolled to identify single nucleotide polymorphisms (SNPs) associated with specific CCRT responses. Genotyping arrays and mass spectrometry were used sequentially to determine germline polymorphisms from blood samples. These polymorphisms remain stable throughout disease progression, unlike somatic mutations from tumor tissues. Two-stage design and additive genetic models were adopted in this study. Results: From the 26 SNPs identified in the first stage, 2 SNPs were found to be significantly associated with CCRT response in the second stage. Single nucleotide polymorphism rs16863886, located between SGPP2 and FARSB on chromosome 2q36.1, was significantly associated with a 3.93-fold increase in pathologic complete response to CCRT (95% confidence interval 1.62–10.30) under additive models. Single nucleotide polymorphism rs4954256, located in ZRANB3 on chromosome 2q21.3, was associated with a 3.93-fold increase in pathologic complete response to CCRT (95% confidence interval 1.57–10.87). The predictive accuracy for CCRT response was 71.59% with these two SNPs combined. Conclusions: This is the first study to identify germline polymorphisms with a high accuracy for predicting CCRT response in the treatment of esophageal cancer.

  7. Screen-detected breast cancer: Does presence of minimal signs on prior mammograms predict staging or grading of cancer?

    Energy Technology Data Exchange (ETDEWEB)

    Bansal, G.J., E-mail: gjbansal@gmail.com [Department of Radiology, University Hospital of Wales, Heath Park, Cardiff, CF14 4XW (United Kingdom); Thomas, K.G. [Department of Radiology, Breast Test Wales, Cathedral Road, Cardiff (United Kingdom)

    2011-07-15

    Aim: To investigate whether the presence of minimal signs on prior mammograms predict staging or grading of cancer. Materials and methods: The previous mammograms of 148 consecutive patients with screen-detected breast cancer were examined. Women with an abnormality visible (minimal signs) on both current and prior mammograms formed the study group; the remaining patients formed the control group. Age, average size of tumour, tumour characteristic, histopathology, grade, and lymph node status were compared between the two groups, using Fisher's exact test. Cases in which earlier diagnosis would have made a significant prognostic difference were also evaluated. Results: Eighteen percent of patients showed an abnormality at the site of the tumour on previous mammograms. There was no statistically significant difference between the two groups with respect to age, average size of tumour, histopathology, grade or lymph node status with p-values being 0.609, 0.781, 0.938, and 0.444, respectively. The only statistically significant difference between the two groups was tumour characteristics with more microcalcifications associated with either mass or asymmetrical density seen in the study group (p = 0.003). Five patients in the study group showed lymph node positivity and were grade 3, and therefore, may have had possible gain from earlier diagnosis. Conclusion: The present study did not demonstrate a statistical difference in grading or staging between the group that showed 'minimal signs' on prior mammograms versus normal prior mammograms. Microcalcification seems to be the most common characteristic seen in the missed cancer and a more aggressive management approach is suggested for breast microcalcifications.

  8. Screen-detected breast cancer: Does presence of minimal signs on prior mammograms predict staging or grading of cancer?

    International Nuclear Information System (INIS)

    Aim: To investigate whether the presence of minimal signs on prior mammograms predict staging or grading of cancer. Materials and methods: The previous mammograms of 148 consecutive patients with screen-detected breast cancer were examined. Women with an abnormality visible (minimal signs) on both current and prior mammograms formed the study group; the remaining patients formed the control group. Age, average size of tumour, tumour characteristic, histopathology, grade, and lymph node status were compared between the two groups, using Fisher's exact test. Cases in which earlier diagnosis would have made a significant prognostic difference were also evaluated. Results: Eighteen percent of patients showed an abnormality at the site of the tumour on previous mammograms. There was no statistically significant difference between the two groups with respect to age, average size of tumour, histopathology, grade or lymph node status with p-values being 0.609, 0.781, 0.938, and 0.444, respectively. The only statistically significant difference between the two groups was tumour characteristics with more microcalcifications associated with either mass or asymmetrical density seen in the study group (p = 0.003). Five patients in the study group showed lymph node positivity and were grade 3, and therefore, may have had possible gain from earlier diagnosis. Conclusion: The present study did not demonstrate a statistical difference in grading or staging between the group that showed 'minimal signs' on prior mammograms versus normal prior mammograms. Microcalcification seems to be the most common characteristic seen in the missed cancer and a more aggressive management approach is suggested for breast microcalcifications.

  9. Dosemetric Parameters Predictive of Rib Fractures after Proton Beam Therapy for Early-Stage Lung Cancer.

    Science.gov (United States)

    Ishikawa, Yojiro; Nakamura, Tatsuya; Kato, Takahiro; Kadoya, Noriyuki; Suzuki, Motohisa; Azami, Yusuke; Hareyama, Masato; Kikuchi, Yasuhiro; Jingu, Keiichi

    2016-01-01

    Proton beam therapy (PBT) is the preferred modality for early-stage lung cancer. Compared with X-ray therapy, PBT offers good dose concentration as revealed by the characteristics of the Bragg peak. Rib fractures (RFs) after PBT lead to decreased quality of life for patients. However, the incidence of and the risk factors for RFs after PBT have not yet been clarified. We therefore explored the relationship between irradiated rib volume and RFs after PBT for early-stage lung cancer. The purpose of this study was to investigate the incidence and the risk factors for RFs following PBT for early-stage lung cancer. We investigated 52 early-stage lung cancer patients and analyzed a total of 215 irradiated ribs after PBT. Grade 2 RFs occurred in 12 patients (20 ribs); these RFs were symptomatic without displacement. No patient experienced more severe RFs. The median time to grade 2 RFs development was 17 months (range: 9-29 months). The three-year incidence of grade 2 RFs was 30.2%. According to the analysis comparing radiation dose and rib volume using receiver operating characteristic curves, we demonstrated that the volume of ribs receiving more than 120 Gy3 (relative biological effectiveness (RBE)) was more than 3.7 cm(3) at an area under the curve of 0.81, which increased the incidence of RFs after PBT (P < 0.001). In this study, RFs were frequently observed following PBT for early-stage lung cancer. We demonstrated that the volume of ribs receiving more than 120 Gy3 (RBE) was the most significant parameter for predicting RFs. PMID:27087118

  10. TFF3 is a valuable predictive biomarker of endocrine response in metastatic breast cancer.

    Science.gov (United States)

    May, Felicity E B; Westley, Bruce R

    2015-06-01

    The stratification of breast cancer patients for endocrine therapies by oestrogen or progesterone receptor expression is effective but imperfect. The present study aims were to validate microarray studies that demonstrate TFF3 regulation by oestrogen and its association with oestrogen receptors in breast cancer, to evaluate TFF3 as a biomarker of endocrine response, and to investigate TFF3 function. Microarray data were validated by quantitative RT-PCR and northern and western transfer analyses. TFF3 was induced by oestrogen, and its induction was inhibited by antioestrogens, tamoxifen, 4-hydroxytamoxifen and fulvestrant in oestrogen-responsive breast cancer cells. The expression of TFF3 mRNA was associated with oestrogen receptor mRNA in breast tumours (Pearson's coefficient=0.762, P=0.000). Monoclonal antibodies raised against the TFF3 protein detected TFF3 by immunohistochemistry in oesophageal submucosal glands, intestinal goblet and neuroendocrine cells, Barrett's metaplasia and intestinal metaplasia. TFF3 protein expression was associated with oestrogen receptor, progesterone receptor and TFF1 expression in malignant breast cells. TFF3 is a specific and sensitive predictive biomarker of response to endocrine therapy, degree of response and duration of response in unstratified metastatic breast cancer patients (P=0.000, P=0.002 and P=0.002 respectively). Multivariate binary logistic regression analysis demonstrated that TFF3 is an independent biomarker of endocrine response and degree of response, and this was confirmed in a validation cohort. TFF3 stimulated migration and invasion of breast cancer cells. In conclusion, TFF3 expression is associated with response to endocrine therapy, and outperforms oestrogen receptor, progesterone receptor and TFF1 as an independent biomarker, possibly because it mediates the malign effects of oestrogen on invasion and metastasis.

  11. Prediction of key genes in ovarian cancer treated with decitabine based on network strategy.

    Science.gov (United States)

    Wang, Yu-Zhen; Qiu, Sheng-Chun

    2016-06-01

    The objective of the present study was to predict key genes in ovarian cancer before and after treatment with decitabine utilizing a network approach and to reveal the molecular mechanism. Pathogenic networks of ovarian cancer before and after treatment were identified based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) detected by Significance Analysis of Microarrays (SAM) method. A weight was assigned to each gene in the pathogenic network and then candidate genes were evaluated. Topological properties (degree, betweenness, closeness and stress) of candidate genes were analyzed to investigate more confident pathogenic genes. Pathway enrichment analysis for candidate and seed genes were conducted. Validation of candidate gene expression in ovarian cancer was performed by reverse transcriptase-polymerase chain reaction (RT-PCR) assays. There were 73 nodes and 147 interactions in the pathogenic network before treatment, while 47 nodes and 66 interactions after treatment. A total of 32 candidate genes were identified in the before treatment group of ovarian cancer, of which 16 were rightly candidate genes after treatment and the others were silenced. We obtained 5 key genes (PIK3R2, CCNB1, IL2, IL1B and CDC6) for decitabine treatment that were validated by RT-PCR. In conclusion, we successfully identified 5 key genes (PIK3R2, CCNB1, IL2, IL1B and CDC6) and validated them, which provides insight into the molecular mechanisms of decitabine treatment and may be potential pathogenic biomarkers for the therapy of ovarian cancer.

  12. Displacements Prediction in Double-Arch Dam Rock Abutment Using SPSS Software Based on Extensometer Readings Case study: Karun 4 Concrete Dam, Iran

    Directory of Open Access Journals (Sweden)

    Hadi kamali Bandpey

    2012-11-01

    Full Text Available In this study we present a method for Displacements Prediction in Double-Arch Dam Rock Abutment Using SPSS Software Based on Extensometer Readings. Displacement in dams is the most tangible and important parameter which could be crucial in their safety. Different elevation displacements are yielded by various loadings and the thrust force imposed on foundation and abutment. Most concrete dams are constructed on stone foundations. Displacements in foundation and abutment are measured by extensometers. Karun 4 Concrete dam is designed with 11 galleries, from elevation 1016 to 802 m, in the order from top elevation (dam crest elevation 1032 to the bottom elevation (dam foundation elevation 806 within the dam body. As a whole, 19 extensometers in the left bank, 17 in the right, and one more in the middle are implemented in the dam. Karun 4 dam has already been impounded with water up to the elevation 1003. Displacements in Karun 4 are recorded by extensometers whence water was leveled in 7 elevations 943.68, 953.36, 973.55, 983.28, 993.17, 1003.13. In this study, using SPSS we have tried to predict the displacements for a situation in which water will be elevated to the elevations 1013, 1023, 1032 in the future for elevations which are equipped with anchor. The most predicted displacement pertaining to the left bank when water was leveled to the elevation 1013, was 3.65 mms by R2 = 0.9997 for the implemented anchor. Proceeding further, as water is leveled to the elevations 1023 and 1033, the most predicted displacement respectively would be 4.31 and 5.66 by R2 = 0.9941; and is related to the anchor implemented in the elevation 936.05. The most predicted displacement for the right bank is 5.9397, 7.2347 and 8.6877 mms by R2 = 0.9995 for the elevation 888.128 m.

  13. Statistics-Based Prediction Analysis for Head and Neck Cancer Tumor Deformation

    Directory of Open Access Journals (Sweden)

    Maryam Azimi

    2012-01-01

    Full Text Available Most of the current radiation therapy planning systems, which are based on pre-treatment Computer Tomography (CT images, assume that the tumor geometry does not change during the course of treatment. However, tumor geometry is shown to be changing over time. We propose a methodology to monitor and predict daily size changes of head and neck cancer tumors during the entire radiation therapy period. Using collected patients' CT scan data, MATLAB routines are developed to quantify the progressive geometric changes occurring in patients during radiation therapy. Regression analysis is implemented to develop predictive models for tumor size changes through entire period. The generated models are validated using leave-one-out cross validation. The proposed method will increase the accuracy of therapy and improve patient's safety and quality of life by reducing the number of harmful unnecessary CT scans.

  14. Prognostic and predictive value of liver volume on colorectal cancer patients with unresectable liver metastases

    Energy Technology Data Exchange (ETDEWEB)

    Park, Jun Su; Park, Hee Chul; Choi, Doo Ho; Park, Won; Yu, Jeong Il; Park, Young Suk; Kang, Won Ki; Park, Joon Oh [Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

    2014-06-15

    To determine the prognostic and predictive value of liver volume in colorectal cancer patients with unresectable liver metastases. Sixteen patients received whole liver radiotherapy (WLRT) between January 1997 and June 2013. A total dose of 21 Gy was delivered in 7 fractions. The median survival time after WLRT was 9 weeks. In univariate analysis, performance status, serum albumin and total bilirubin level, liver volume and extrahepatic metastases were associated with survival. The mean liver volume was significantly different between subgroups with and without pain relief (3,097 and 4,739 mL, respectively; p = 0.002). A larger liver volume is a poor prognostic factor for survival and also a negative predictive factor for response to WLRT. If patients who are referred for WLRT have large liver volume, they should be informed of the poor prognosis and should be closely observed during and after WLRT.

  15. RaCon: a software tool serving to predict radiological consequences of various types of accident in support of emergency management and radiation monitoring management

    International Nuclear Information System (INIS)

    The RaCon software system, developed by the Nuclear Research Institute Rez, is described and its application when addressing various tasks in the domain of radiation accidents and nuclear safety (accidents at nuclear facilities, transport of radioactive material, terrorist attacks) are outlined. RaCon is intended for the prediction and evaluation of radiological consequences to population and rescue teams and for optimization of monitoring actions. The system provides support to emergency management when evaluating and devising actions to mitigate the consequences of radiation accidents. The deployment of RaCon within the system of radiation monitoring by mobile emergency teams or remote controlled UAV is an important application. Based on a prediction of the radiological situation, RaCon facilitates decision-making and control of the radiation monitoring system, and in turn, refines the prediction based on observed values. Furthermore, the system can perform simulations of evacuation patterns at the Dukovany NPP and at schools in the vicinity of the power plant and can provide support to emergency management should any such situation arise. (orig.)

  16. Multi-output Model with Box-Jenkins Operators of Quadratic Indices for Prediction of Malaria and Cancer Inhibitors Targeting Ubiquitin- Proteasome Pathway (UPP) Proteins.

    Science.gov (United States)

    Casañola-Martin, Gerardo M; Le-Thi-Thu, Huong; Pérez-Giménez, Facundo; Marrero-Ponce, Yovani; Merino-Sanjuán, Matilde; Abad, Concepción; González-Díaz, Humberto

    2016-01-01

    The ubiquitin-proteasome pathway (UPP) is the primary degradation system of short-lived regulatory proteins. Cellular processes such as the cell cycle, signal transduction, gene expression, DNA repair and apoptosis are regulated by this UPP and dysfunctions in this system have important implications in the development of cancer, neurodegenerative, cardiac and other human pathologies. UPP seems also to be very important in the function of eukaryote cells of the human parasites like Plasmodium falciparum, the causal agent of the neglected disease Malaria. Hence, the UPP could be considered as an attractive target for the development of compounds with Anti-Malarial or Anti-cancer properties. Recent online databases like ChEMBL contains a larger quantity of information in terms of pharmacological assay protocols and compounds tested as UPP inhibitors under many different conditions. This large amount of data give new openings for the computer-aided identification of UPP inhibitors, but the intrinsic data diversity is an obstacle for the development of successful classifiers. To solve this problem here we used the Bob-Jenkins moving average operators and the atom-based quadratic molecular indices calculated with the software TOMOCOMD-CARDD (TC) to develop a quantitative model for the prediction of the multiple outputs in this complex dataset. Our multi-target model can predict results for drugs against 22 molecular or cellular targets of different organisms with accuracies above 70% in both training and validation sets.

  17. Multi-output Model with Box-Jenkins Operators of Quadratic Indices for Prediction of Malaria and Cancer Inhibitors Targeting Ubiquitin- Proteasome Pathway (UPP) Proteins.

    Science.gov (United States)

    Casañola-Martin, Gerardo M; Le-Thi-Thu, Huong; Pérez-Giménez, Facundo; Marrero-Ponce, Yovani; Merino-Sanjuán, Matilde; Abad, Concepción; González-Díaz, Humberto

    2016-01-01

    The ubiquitin-proteasome pathway (UPP) is the primary degradation system of short-lived regulatory proteins. Cellular processes such as the cell cycle, signal transduction, gene expression, DNA repair and apoptosis are regulated by this UPP and dysfunctions in this system have important implications in the development of cancer, neurodegenerative, cardiac and other human pathologies. UPP seems also to be very important in the function of eukaryote cells of the human parasites like Plasmodium falciparum, the causal agent of the neglected disease Malaria. Hence, the UPP could be considered as an attractive target for the development of compounds with Anti-Malarial or Anti-cancer properties. Recent online databases like ChEMBL contains a larger quantity of information in terms of pharmacological assay protocols and compounds tested as UPP inhibitors under many different conditions. This large amount of data give new openings for the computer-aided identification of UPP inhibitors, but the intrinsic data diversity is an obstacle for the development of successful classifiers. To solve this problem here we used the Bob-Jenkins moving average operators and the atom-based quadratic molecular indices calculated with the software TOMOCOMD-CARDD (TC) to develop a quantitative model for the prediction of the multiple outputs in this complex dataset. Our multi-target model can predict results for drugs against 22 molecular or cellular targets of different organisms with accuracies above 70% in both training and validation sets. PMID:26427384

  18. Random Forests to Predict Rectal Toxicity Following Prostate Cancer Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Ospina, Juan D. [LTSI, Université de Rennes 1, Rennes (France); INSERM, U1099, Rennes (France); Escuela de Estadística, Universidad Nacional de Colombia Sede Medellín, Medellín (Colombia); Zhu, Jian [LTSI, Université de Rennes 1, Rennes (France); Laboratory of Image Science and Technology, Southeast University, Nanjing (China); Department of Radiation Physics, Shandong Cancer Hospital and Institute, Jinan (China); Centre de Recherche en Information Biomédical Sino-Français, Rennes (France); Chira, Ciprian [Département de Radiothérapie, Centre Eugène Marquis, Rennes (France); Bossi, Alberto [Département de Radiothérapie, Institut Gustave-Roussy, Villejuif (France); Delobel, Jean B. [Département de Radiothérapie, Centre Eugène Marquis, Rennes (France); Beckendorf, Véronique [Département de Radiothérapie, Centre Alexis Vautrin, Nancy (France); Dubray, Bernard [Département de Radiothérapie, CRLCC Henri Becquerel, Rouen (France); Lagrange, Jean-Léon [Département de Radiothérapie, Hôpital Henri Mondor, Créteil (France); Correa, Juan C. [Escuela de Estadística, Universidad Nacional de Colombia Sede Medellín, Medellín (Colombia); and others

    2014-08-01

    Purpose: To propose a random forest normal tissue complication probability (RF-NTCP) model to predict late rectal toxicity following prostate cancer radiation therapy, and to compare its performance to that of classic NTCP models. Methods and Materials: Clinical data and dose-volume histograms (DVH) were collected from 261 patients who received 3-dimensional conformal radiation therapy for prostate cancer with at least 5 years of follow-up. The series was split 1000 times into training and validation cohorts. A RF was trained to predict the risk of 5-year overall rectal toxicity and bleeding. Parameters of the Lyman-Kutcher-Burman (LKB) model were identified and a logistic regression model was fit. The performance of all the models was assessed by computing the area under the receiving operating characteristic curve (AUC). Results: The 5-year grade ≥2 overall rectal toxicity and grade ≥1 and grade ≥2 rectal bleeding rates were 16%, 25%, and 10%, respectively. Predictive capabilities were obtained using the RF-NTCP model for all 3 toxicity endpoints, including both the training and validation cohorts. The age and use of anticoagulants were found to be predictors of rectal bleeding. The AUC for RF-NTCP ranged from 0.66 to 0.76, depending on the toxicity endpoint. The AUC values for the LKB-NTCP were statistically significantly inferior, ranging from 0.62 to 0.69. Conclusions: The RF-NTCP model may be a useful new tool in predicting late rectal toxicity, including variables other than DVH, and thus appears as a strong competitor to classic NTCP models.

  19. Introduction of Global Software for Prediction of Acoustic Signals on Ships%国外舰船声特征信号预测软件简介

    Institute of Scientific and Technical Information of China (English)

    伏捷

    2013-01-01

      舰船声特征信号的预测和评价是当前国内外的前沿课题,也是迫切需要发展的研究领域.迄今推广运用了国外的三种软件:辐射噪声估算软件——NDES、声目标强度估算软件——ARTES、综合辐射噪声与自噪声建模软件——FNVNOISE.以上方法将声特征信号预测建立在理论计算基础上,对于复杂问题均建立了各种专门用来帮助设计人员进行重要计算的数据库.研究人员可通过不断增加和核对新的数据,使数据库和计算方法得到不断改进和完善,从而为将来在解决设计中遇到的问题提供更有效的支持.%Prediction and evaluation of acoustic signature on ships is a frontier subject and a field to be developed. Three kinds of popular software, radiation noise evaluation software—NDES, target strength evaluation software—ARTES, and integrated radiation noise and self-noise modeling software—FNVNOISE, were introduced in the aspects of principles, utility and functions. By means of above software, prediction of the acoustic signals was completed based on theoretical calculation. The computation output was used to establish a database which can help designers for essential calculations of complicated issues. The database and algorithm can be constantly improved and updated through addition and verification of new data in order to provide solutions for new problems in future design.

  20. T cell subpopulations in lymph nodes may not be predictive of patient outcome in colorectal cancer

    Directory of Open Access Journals (Sweden)

    Yoon Han-Seung

    2011-08-01

    Full Text Available Abstract Background The immune response has been proposed to be an important factor in determining patient outcome in colorectal cancer (CRC. Previous studies have concentrated on characterizing T cell populations in the primary tumour where T cells with regulatory effect (Foxp3+ Tregs have been identified as both enhancing and diminishing anti-tumour immune responses. No previous studies have characterized the T cell response in the regional lymph nodes in CRC. Methods Immunohistochemistry was used to analyse CD4, CD8 or Foxp3+ T cell populations in the regional lymph nodes of patients with stage II CRC (n = 31, with (n = 13 or without (n = 18 cancer recurrence after 5 years of follow up, to determine if the priming environment for anti-tumour immunity was associated with clinical outcome. Results The proportions of CD4, CD8 or Foxp3+ cells in the lymph nodes varied widely between and within patients, and there was no association between T cell populations and cancer recurrence or other clinicopathological characteristics. Conclusions These data indicate that frequency of these T cell subsets in lymph nodes may not be a useful tool for predicting patient outcome.

  1. Clinicopathological Characteristics as Predictive Factrs for Lymph Node Metastasis in Submucosal Gastric Cancer

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    OBJECTIVE To identify clinicopathological characteristics as predictive factors for lymph node metastasis in submucosal gastric cancer, and in addition to establish objective criteria as indications for endoscopic submucosal dissection (ESD).METHODS Data from 130 patients with submucosal gastric cancer were collected, and the relationship between their clinicopathological characteristics and the presence of lymph node metastasis was retrospectively analyzed by multivariate analysis.RESULTS In the multivariate logistic regression model, a tumor size of 2 cm or more and an undifferentiated histologic type were found to be independent risk clinicopathological characteristics for lymph node metastasis.Among 130 patients with submucosal carcinoma, no lymph node metastases were observed in 17 patients who showed neither of the two risk clinicopathological characteristics. Lymph node metastasis occurred in 61.1% (22/36) of the patients who had both risk clinicopathological characteristics.CONCLUSION A tumor size of 2 cm or more and an undifferentiated histologic type were significantly and independently related to lymph node metastasis in submucosal gastric cancer. It is rational for the paitients with neither of these two independent risk clinicopathological characteristics to undergo an ESD.

  2. Circulating Tumor Cells in Metastatic Breast Cancer: A Prognostic and Predictive Marker

    Directory of Open Access Journals (Sweden)

    Sayyed Farshid Moussavi-Harami

    2014-05-01

    Full Text Available The role of circulating tumor cells (CTCs as a marker for disease progression in metastatic cancer is controversial. The current review will serve to summarize the evidence on CTCs as a marker of disease progression in patients with metastatic breast cancer. The immunohistochemistry (IHC-based CellSearch® is the only FDA-approved isolation technique for quantifying CTCs in patients with metastatic breast cancer. We searched PubMed and Web of Knowledge for clinical studies that assessed the prognostic and predictive value of CTCs using IHC-based isolation. The patient outcomes reported include median and Cox-proportional hazard ratios for overall survival (OS and progression-free survival (PFS. All studies reported shorter OS for CTC-positive patients versus CTC-negative. A subset of the selected trials reported significant lower median PFS for CTC-positive patients. The reported trials support the utility of CTC enumeration for patient prognosis. But further studies are required to determine the utility of CTC enumeration for guiding patient therapy. There are three clinical trials ongoing to test this hypothesis. These studies, and others, will further establish the role of CTCs in clinical practice.

  3. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds.

    Directory of Open Access Journals (Sweden)

    Howard Y Chang

    2004-02-01

    Full Text Available Cancer invasion and metastasis have been likened to wound healing gone awry. Despite parallels in cellular behavior between cancer progression and wound healing, the molecular relationships between these two processes and their prognostic implications are unclear. In this study, based on gene expression profiles of fibroblasts from ten anatomic sites, we identify a stereotyped gene expression program in response to serum exposure that appears to reflect the multifaceted role of fibroblasts in wound healing. The genes comprising this fibroblast common serum response are coordinately regulated in many human tumors, allowing us to identify tumors with gene expression signatures suggestive of active wounds. Genes induced in the fibroblast serum-response program are expressed in tumors by the tumor cells themselves, by tumor-associated fibroblasts, or both. The molecular features that define this wound-like phenotype are evident at an early clinical stage, persist during treatment, and predict increased risk of metastasis and death in breast, lung, and gastric carcinomas. Thus, the transcriptional signature of the response of fibroblasts to serum provides a possible link between cancer progression and wound healing, as well as a powerful predictor of the clinical course in several common carcinomas.

  4. The Prognostic and Predictive Role of Epidermal Growth Factor Receptor in Surgical Resected Pancreatic Cancer.

    Science.gov (United States)

    Guo, Meng; Luo, Guopei; Liu, Chen; Cheng, He; Lu, Yu; Jin, Kaizhou; Liu, Zuqiang; Long, Jiang; Liu, Liang; Xu, Jin; Huang, Dan; Ni, Quanxing; Yu, Xianjun

    2016-01-01

    The data regarding the prognostic significance of EGFR (epidermal growth factor receptor) expression and adjuvant therapy in patients with resected pancreatic cancer are insufficient. We retrospectively investigated EGFR status in 357 resected PDAC (pancreatic duct adenocarcinoma) patients using tissue immunohistochemistry and validated the possible role of EGFR expression in predicting prognosis. The analysis was based on excluding the multiple confounding parameters. A negative association was found between overall EGFR status and postoperative survival (p = 0.986). Remarkably, adjuvant chemotherapy and radiotherapy were significantly associated with favorable postoperative survival, which prolonged median overall survival (OS) for 5.8 and 10.2 months (p = 0.009 and p = 0.006, respectively). Kaplan-Meier analysis showed that adjuvant chemotherapy correlated with an obvious survival benefit in the EGFR-positive subgroup rather than in the EGFR-negative subgroup. In the subgroup analyses, chemotherapy was highly associated with increased postoperative survival in the EGFR-negative subgroup (p = 0.002), and radiotherapy had a significant survival benefit in the EGFR-positive subgroup (p = 0.029). This study demonstrated that EGFR expression is not correlated with outcome in resected pancreatic cancer patients. Adjuvant chemotherapy and radiotherapy were significantly associated with improved survival in contrary EGFR expressing subgroup. Further studies of EGFR as a potential target for pancreatic cancer treatment are warranted. PMID:27399694

  5. Predicting the response of localised oesophageal cancer to neo-adjuvant chemoradiation

    Directory of Open Access Journals (Sweden)

    Reynolds John

    2007-08-01

    Full Text Available Abstract Background A complete pathological response to neo-adjuvant chemo-radiation for oesophageal cancer is associated with favourable survival. However, such a benefit is seen in the minority. If one could identify, at diagnosis, those patients who were unlikely to respond unnecessary toxicity could be avoided and alternative treatment can be considered. The aim of this review was to highlight predictive markers currently assessed and evaluate their clinical utility. Methods A systematic search of Pubmed and Google Scholar was performed using the following keywords; "neo-adjuvant", "oesophageal", "trimodality", "chemotherapy", "radiotherapy", "chemoradiation" and "predict". The original manuscripts were sourced for further articles of relevance. Results Conventional indices including tumour stage and grade seem unable to predict histological response. Immuno-histochemical markers have been extensively studied, but none has made its way into routine clinical practice. Global gene expression from fresh pre-treatment tissue using cDNA microarray has only recently been assessed, but shows considerable promise. Molecular imaging using FDG-PET seems to be able to predict response after neo-adjuvant chemoradiation has finished, but there is a paucity of data when such imaging is performed earlier. Conclusion Currently there are no clinically useful predictors of response based on standard pathological assessment and immunohistochemistry. Genomics, proteomics and molecular imaging may hold promise.

  6. Increased tumour ADC value during chemotherapy predicts improved survival in unresectable pancreatic cancer

    International Nuclear Information System (INIS)

    To investigate whether changes to the apparent diffusion coefficient (ADC) of primary tumour in the early period after starting chemotherapy can predict progression-free survival (PFS) or overall survival (OS) in patients with unresectable pancreatic adenocarcinoma. Subjects comprised 43 patients with histologically confirmed unresectable pancreatic cancer treated with first-line chemotherapy. Minimum ADC values in primary tumour were measured using the selected area ADC (sADC), which excluded cystic and necrotic areas and vessels, and the whole tumour ADC (wADC), which included whole tumour components. Relative changes in ADC were calculated from baseline to 4 weeks after initiation of chemotherapy. Relationships between ADC and both PFS and OS were modelled by Cox proportional hazards regression. Median PFS and OS were 6.1 and 11.0 months, respectively. In multivariate analysis, sADC change was the strongest predictor of PFS (hazard ratio (HR), 4.5; 95 % confidence interval (CI), 1.7-11.9; p = 0.002). Multivariate Cox regression analysis for OS revealed sADC change and CRP as independent predictive markers, with sADC change as the strongest predictive biomarker (HR, 6.7; 95 % CI, 2.7-16.6; p = 0.001). Relative changes in sADC could provide a useful imaging biomarker to predict PFS and OS with chemotherapy for unresectable pancreatic adenocarcinoma. (orig.)

  7. Increased tumour ADC value during chemotherapy predicts improved survival in unresectable pancreatic cancer

    Energy Technology Data Exchange (ETDEWEB)

    Nishiofuku, Hideyuki; Tanaka, Toshihiro; Kichikawa, Kimihiko [Nara Medical University, Department of Radiology and IVR Center, Kashihara-city, Nara (Japan); Marugami, Nagaaki [Nara Medical University, Department of Endoscopy and Ultrasound, Kashihara-city, Nara (Japan); Sho, Masayuki; Akahori, Takahiro; Nakajima, Yoshiyuki [Nara Medical University, Department of Surgery, Kashihara-city, Nara (Japan)

    2016-06-15

    To investigate whether changes to the apparent diffusion coefficient (ADC) of primary tumour in the early period after starting chemotherapy can predict progression-free survival (PFS) or overall survival (OS) in patients with unresectable pancreatic adenocarcinoma. Subjects comprised 43 patients with histologically confirmed unresectable pancreatic cancer treated with first-line chemotherapy. Minimum ADC values in primary tumour were measured using the selected area ADC (sADC), which excluded cystic and necrotic areas and vessels, and the whole tumour ADC (wADC), which included whole tumour components. Relative changes in ADC were calculated from baseline to 4 weeks after initiation of chemotherapy. Relationships between ADC and both PFS and OS were modelled by Cox proportional hazards regression. Median PFS and OS were 6.1 and 11.0 months, respectively. In multivariate analysis, sADC change was the strongest predictor of PFS (hazard ratio (HR), 4.5; 95 % confidence interval (CI), 1.7-11.9; p = 0.002). Multivariate Cox regression analysis for OS revealed sADC change and CRP as independent predictive markers, with sADC change as the strongest predictive biomarker (HR, 6.7; 95 % CI, 2.7-16.6; p = 0.001). Relative changes in sADC could provide a useful imaging biomarker to predict PFS and OS with chemotherapy for unresectable pancreatic adenocarcinoma. (orig.)

  8. Computed Tomography (CT) Perfusion as an Early Predictive Marker for Treatment Response to Neoadjuvant Chemotherapy in Gastroesophageal Junction Cancer and Gastric Cancer - A Prospective Study

    DEFF Research Database (Denmark)

    Lundsgaard Hansen, Martin; Fallentin, Eva; Lauridsen, Carsten;

    2014-01-01

    OBJECTIVES: To evaluate whether early reductions in CT perfusion parameters predict response to pre-operative chemotherapy prior to surgery for gastroesophageal junction (GEJ) and gastric cancer. MATERIALS AND METHODS: Twenty-eight patients with adenocarcinoma of the gastro-esophageal junction (GEJ......-operative chemotherapy in GEJ and gastric cancer. As a single diagnostic test, CT Perfusion only has moderate sensitivity and specificity in response assessment of pre-operative chemotherapy making it insufficient for clinical decision purposes....

  9. Can Western Based Online Prostate Cancer Risk Calculators Be Used to Predict Prostate Cancer after Prostate Biopsy for the Korean Population?

    OpenAIRE

    Lee, Dong Hoon; Jung, Ha Bum; Park, Jae Won; Kim, Kyu Hyun; Kim, Jongchan; Lee, Seung Hwan; Chung, Byung Ha

    2013-01-01

    Purpose To access the predictive value of the European Randomized Screening of Prostate Cancer Risk Calculator (ERSPC-RC) and the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) in the Korean population. Materials and Methods We retrospectively analyzed the data of 517 men who underwent transrectal ultrasound guided prostate biopsy between January 2008 and November 2010. Simple and multiple logistic regression analysis were performed to compare the result of prostate biopsy. Area u...

  10. Structure Identification and Anti-Cancer Pharmacological Prediction of Triterpenes from Ganoderma lucidum.

    Science.gov (United States)

    Shao, Yanyan; Qiao, Liansheng; Wu, Lingfang; Sun, Xuefei; Zhu, Dan; Yang, Guanghui; Zhang, Xiaoxue; Mao, Xin; Chen, Wenjing; Liang, Wenyi; Zhang, Yanling; Zhang, Lanzhen

    2016-05-21

    Ganoderma triterpenes (GTs) are the major secondary metabolites of Ganoderma lucidum, which is a popularly used traditional Chinese medicine for complementary cancer therapy. In the present study, systematic isolation, and in silico pharmacological prediction are implemented to discover potential anti-cancer active GTs from G. lucidum. Nineteen GTs, three steroids, one cerebroside, and one thymidine were isolated from G. lucidum. Six GTs were first isolated from the fruiting bodies of G. lucidum, including 3β,7β,15β-trihydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid methyl ester (1), 3β,7β,15β-trihydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid (2), 3β,7β,15α,28-tetrahydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid (3), ganotropic acid (4), 26-nor-11,23-dioxo-5α-lanost-8-en-3β,7β,15α,25-tetrol (5) and (3β,7α)-dihydroxy-lanosta-8,24-dien- 11-one (6). (4E,8E)-N-d-2'-hydroxypalmitoyl-l-O-β-d-glucopyranosyl-9-methyl-4,8-spingodienine (7), and stigmasta-7,22-dien-3β,5α,6α-triol (8) were first reported from the genus Ganodema. By using reverse pharmacophoric profiling of the six GTs, thirty potential anti-cancer therapeutic targets were identified and utilized to construct their ingredient-target interaction network. Then nineteen high frequency targets of GTs were selected from thirty potential targets to construct a protein interaction network (PIN). In order to cluster the pharmacological activity of GTs, twelve function modules were identified by molecular complex detection (MCODE) and gene ontology (GO) enrichment analysis. The results indicated that anti-cancer effect of GTs might be related to histone acetylation and interphase of mitotic cell cycle by regulating general control non-derepressible 5 (GCN5) and cyclin-dependent kinase-2 (CDK2), respectively. This research mode of extraction, isolation, pharmacological prediction, and PIN analysis might be beneficial to rapidly predict and discover pharmacological activities of novel compounds.

  11. Structure Identification and Anti-Cancer Pharmacological Prediction of Triterpenes from Ganoderma lucidum.

    Science.gov (United States)

    Shao, Yanyan; Qiao, Liansheng; Wu, Lingfang; Sun, Xuefei; Zhu, Dan; Yang, Guanghui; Zhang, Xiaoxue; Mao, Xin; Chen, Wenjing; Liang, Wenyi; Zhang, Yanling; Zhang, Lanzhen

    2016-01-01

    Ganoderma triterpenes (GTs) are the major secondary metabolites of Ganoderma lucidum, which is a popularly used traditional Chinese medicine for complementary cancer therapy. In the present study, systematic isolation, and in silico pharmacological prediction are implemented to discover potential anti-cancer active GTs from G. lucidum. Nineteen GTs, three steroids, one cerebroside, and one thymidine were isolated from G. lucidum. Six GTs were first isolated from the fruiting bodies of G. lucidum, including 3β,7β,15β-trihydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid methyl ester (1), 3β,7β,15β-trihydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid (2), 3β,7β,15α,28-tetrahydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid (3), ganotropic acid (4), 26-nor-11,23-dioxo-5α-lanost-8-en-3β,7β,15α,25-tetrol (5) and (3β,7α)-dihydroxy-lanosta-8,24-dien- 11-one (6). (4E,8E)-N-d-2'-hydroxypalmitoyl-l-O-β-d-glucopyranosyl-9-methyl-4,8-spingodienine (7), and stigmasta-7,22-dien-3β,5α,6α-triol (8) were first reported from the genus Ganodema. By using reverse pharmacophoric profiling of the six GTs, thirty potential anti-cancer therapeutic targets were identified and utilized to construct their ingredient-target interaction network. Then nineteen high frequency targets of GTs were selected from thirty potential targets to construct a protein interaction network (PIN). In order to cluster the pharmacological activity of GTs, twelve function modules were identified by molecular complex detection (MCODE) and gene ontology (GO) enrichment analysis. The results indicated that anti-cancer effect of GTs might be related to histone acetylation and interphase of mitotic cell cycle by regulating general control non-derepressible 5 (GCN5) and cyclin-dependent kinase-2 (CDK2), respectively. This research mode of extraction, isolation, pharmacological prediction, and PIN analysis might be beneficial to rapidly predict and discover pharmacological activities of novel compounds

  12. Structure Identification and Anti-Cancer Pharmacological Prediction of Triterpenes from Ganoderma lucidum

    Directory of Open Access Journals (Sweden)

    Yanyan Shao

    2016-05-01

    Full Text Available Ganoderma triterpenes (GTs are the major secondary metabolites of Ganoderma lucidum, which is a popularly used traditional Chinese medicine for complementary cancer therapy. In the present study, systematic isolation, and in silico pharmacological prediction are implemented to discover potential anti-cancer active GTs from G. lucidum. Nineteen GTs, three steroids, one cerebroside, and one thymidine were isolated from G. lucidum. Six GTs were first isolated from the fruiting bodies of G. lucidum, including 3β,7β,15β-trihydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid methyl ester (1, 3β,7β,15β-trihydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid (2, 3β,7β,15α,28-tetrahydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid (3, ganotropic acid (4, 26-nor-11,23-dioxo-5α-lanost-8-en-3β,7β,15α,25-tetrol (5 and (3β,7α-dihydroxy-lanosta-8,24-dien- 11-one (6. (4E,8E-N-d-2′-hydroxypalmitoyl-l-O-β-d-glucopyranosyl-9-methyl-4,8-spingodienine (7, and stigmasta-7,22-dien-3β,5α,6α-triol (8 were first reported from the genus Ganodema. By using reverse pharmacophoric profiling of the six GTs, thirty potential anti-cancer therapeutic targets were identified and utilized to construct their ingredient-target interaction network. Then nineteen high frequency targets of GTs were selected from thirty potential targets to construct a protein interaction network (PIN. In order to cluster the pharmacological activity of GTs, twelve function modules were identified by molecular complex detection (MCODE and gene ontology (GO enrichment analysis. The results indicated that anti-cancer effect of GTs might be related to histone acetylation and interphase of mitotic cell cycle by regulating general control non-derepressible 5 (GCN5 and cyclin-dependent kinase-2 (CDK2, respectively. This research mode of extraction, isolation, pharmacological prediction, and PIN analysis might be beneficial to rapidly predict and discover pharmacological activities of novel

  13. Multiscale approach predictions for biological outcomes in ion-beam cancer therapy

    Science.gov (United States)

    Verkhovtsev, Alexey; Surdutovich, Eugene; Solov'Yov, Andrey V.

    2016-06-01

    Ion-beam therapy provides advances in cancer treatment, offering the possibility of excellent dose localization and thus maximising cell-killing within the tumour. The full potential of such therapy can only be realised if the fundamental mechanisms leading to lethal cell damage under ion irradiation are well understood. The key question is whether it is possible to quantitatively predict macroscopic biological effects caused by ion radiation on the basis of physical and chemical effects related to the ion-medium interactions on a nanometre scale. We demonstrate that the phenomenon-based MultiScale Approach to the assessment of radiation damage with ions gives a positive answer to this question. We apply this approach to numerous experiments where survival curves were obtained for different cell lines and conditions. Contrary to other, in essence empirical methods for evaluation of macroscopic effects of ionising radiation, the MultiScale Approach predicts the biodamage based on the physical effects related to ionisation of the medium, transport of secondary particles, chemical interactions, thermo-mechanical pathways of biodamage, and heuristic biological criteria for cell survival. We anticipate this method to give great impetus to the practical improvement of ion-beam cancer therapy and the development of more efficient treatment protocols.

  14. A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer.

    Science.gov (United States)

    Kureshi, Nelofar; Abidi, Syed Sibte Raza; Blouin, Christian

    2016-01-01

    Non-small cell lung cancer (NSCLC) constitutes the most common type of lung cancer and is frequently diagnosed at advanced stages. Clinical studies have shown that molecular targeted therapies increase survival and improve quality of life in patients. Nevertheless, the realization of personalized therapies for NSCLC faces a number of challenges including the integration of clinical and genetic data and a lack of clinical decision support tools to assist physicians with patient selection. To address this problem, we used frequent pattern mining to establish the relationships of patient characteristics and tumor response in advanced NSCLC. Univariate analysis determined that smoking status, histology, epidermal growth factor receptor (EGFR) mutation, and targeted drug were significantly associated with response to targeted therapy. We applied four classifiers to predict treatment outcome from EGFR tyrosine kinase inhibitors. Overall, the highest classification accuracy was 76.56% and the area under the curve was 0.76. The decision tree used a combination of EGFR mutations, histology, and smoking status to predict tumor response and the output was both easily understandable and in keeping with current knowledge. Our findings suggest that support vector machines and decision trees are a promising approach for clinical decision support in the patient selection for targeted therapy in advanced NSCLC. PMID:25494516

  15. Prognostic and predictive value of DAMPs and DAMP-associated processes in cancer

    Directory of Open Access Journals (Sweden)

    Jitka eFucikova

    2015-08-01

    Full Text Available It is now clear that human neoplasms form, progress and respond to therapy in the context of an intimate crosstalk with the host immune system. In particular, accumulating evidence demonstrates that the efficacy of most, if not all, chemo- and radiotherapeutic agents commonly employed in the clinic critically depends on the (reactivation of tumor-targeting immune response. One of the mechanisms whereby conventional chemotherapeutics, targeted anticancer agents and radiotherapy can provoke a therapeutically relevant, adaptive immune response against malignant cells is commonly known as „immunogenic cell death (ICD. Importantly, dying cancer cells are perceived as immunogenic only when they emit a set of immunostimulatory signals upon the activation of intracellular stress response pathways. The emission of these signals, which are generally referred to as „damage-associated molecular patterns (DAMPs, may therefore predict whether patients will respond to chemotherapy or not, at least in some settings. Here, we review clinical data indicating that DAMPs and DAMP-associated stress responses might have prognostic or predictive value for cancer patients.

  16. Colorectal cancer in patients with inflammatory bowel disease: Can we predict risk?

    Institute of Scientific and Technical Information of China (English)

    Vibeke Andersen; Jonas Halfvarson; Ulla Vogel

    2012-01-01

    The inflammatory bowel diseases (IBD),Crohn's disease (CD) and ulcerative colitis (UC),may be complicated by colorectal cancer (CRC).In a recent populationbased cohort study of 47 347 Danish patients with IBD by Tine Jess and colleagues 268 patients with UC and 70 patients with CD developed CRC during 30 years of observation.The overall risk of CRC among patients with UC and CD was comparable with that of the general population.However,patients diagnosed with UC during childhood or as adolescents,patients with long duration of disease and those with concomitant primary sclerosing cholangitis were at increased risk.In this commentary,we discuss the mechanisms underlying carcinogenesis in IBD and current investigations of genetic susceptibility in IBD patients.Further advances will depend on the cooperative work by epidemiologist and molecular geneticists in order to identify genetic polymorphisms involved in IBD-associated CRC.The ultimate goal is to incorporate genotypes and clinical parameters into a predictive model that will refine the prediction of risk for CRC in colonic IBD.The challenge will be to translate these new findings into clinical practice and to determine appropriate preventive strategies in order to avoid CRC in IBD patients.The achieved knowledge may also be relevant for other inflammation-associated cancers.

  17. Prediction of response to radiotherapy in the treatment of esophageal cancer using stem cell markers

    International Nuclear Information System (INIS)

    Background and purpose: In this study, we investigated whether cancer stem cell marker expressing cells can be identified that predict for the response of esophageal cancer (EC) to CRT. Materials and methods: EC cell-lines OE-33 and OE-21 were used to assess in vitro, stem cell activity, proliferative capacity and radiation response. Xenograft tumors were generated using NOD/SCID mice to assess in vivo proliferative capacity and tumor hypoxia. Archival and fresh EC biopsy tissue was used to confirm our in vitro and in vivo results. Results: We showed that the CD44+/CD24− subpopulation of EC cells exerts a higher proliferation rate and sphere forming potential and is more radioresistant in vitro, when compared to unselected or CD44+/CD24+ cells. Moreover, CD44+/CD24− cells formed xenograft tumors faster and were often located in hypoxic tumor areas. In a study of archival pre-neoadjuvant CRT biopsy material from EC adenocarcinoma patients (N = 27), this population could only be identified in 50% (9/18) of reduced-responders to neoadjuvant CRT, but never (0/9) in the complete responders (P = 0.009). Conclusion: These results warrant further investigation into the possible clinical benefit of CD44+/CD24− as a predictive marker in EC patients for the response to chemoradiation

  18. Expression changes in the stroma of prostate cancer predict subsequent relapse.

    Directory of Open Access Journals (Sweden)

    Zhenyu Jia

    Full Text Available Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year, and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001. We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.

  19. Subtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications

    Directory of Open Access Journals (Sweden)

    Chen C

    2014-02-01

    Full Text Available Chuang Chen,1 Jing-Ping Yuan,2,3 Wen Wei,1 Yi Tu,1 Feng Yao,1 Xue-Qin Yang,4 Jin-Zhong Sun,1 Sheng-Rong Sun,1 Yan Li2 1Department of Breast and Thyroid Surgery, Wuhan University, Renmin Hospital, Wuhan, 2Department of Oncology, Zhongnan Hospital of Wuhan University and Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, Wuhan, 3Department of Pathology, The Central Hospital of Wuhan, Wuhan, 4Medical School of Jingchu University of Technology, Jingmen, People’s Republic of China Background: Hormone receptors, including the estrogen receptor and progesterone receptor, human epidermal growth factor receptor 2 (HER2, and other biomarkers like Ki67, epidermal growth factor receptor (EGFR, also known as HER1, the androgen receptor, and p53, are key molecules in breast cancer. This study evaluated the relationship between HER2 and hormone receptors and explored the additional prognostic value of Ki67, EGFR, the androgen receptor, and p53. Methods: Quantitative determination of HER2 and EGFR was performed in 240 invasive breast cancer tissue microarray specimens using quantum dot (QD-based nanotechnology. We identified two subtypes of HER2, ie, high total HER2 load (HTH2 and low total HER2 load (LTH2, and three subtypes of hormone receptor, ie, high hormone receptor (HHR, low hormone receptor (LHR, and no hormone receptor (NHR. Therefore, breast cancer patients could be divided into five subtypes according to HER2 and hormone receptor status. Ki67, p53, and the androgen receptor were determined by traditional immunohistochemistry techniques. The relationship between hormone receptors and HER2 was investigated and the additional value of Ki67, EGFR, the androgen receptor, and p53 for prediction of 5-year disease-free survival was assessed. Results: In all patients, quantitative determination showed a statistically significant (P<0.001 negative correlation between HER2 and the hormone receptors and a significant

  20. The value of metabolic imaging to predict tumour response after chemoradiation in locally advanced rectal cancer

    Directory of Open Access Journals (Sweden)

    Gómez-Río Manuel

    2010-12-01

    Full Text Available Abstract Background We aim to investigate the possibility of using 18F-positron emission tomography/computer tomography (PET-CT to predict the histopathologic response in locally advanced rectal cancer (LARC treated with preoperative chemoradiation (CRT. Methods The study included 50 patients with LARC treated with preoperative CRT. All patients were evaluated by PET-CT before and after CRT, and results were compared to histopathologic response quantified by tumour regression grade (patients with TRG 1-2 being defined as responders and patients with grade 3-5 as non-responders. Furthermore, the predictive value of metabolic imaging for pathologic complete response (ypCR was investigated. Results Responders and non-responders showed statistically significant differences according to Mandard's criteria for maximum standardized uptake value (SUVmax before and after CRT with a specificity of 76,6% and a positive predictive value of 66,7%. Furthermore, SUVmax values after CRT were able to differentiate patients with ypCR with a sensitivity of 63% and a specificity of 74,4% (positive predictive value 41,2% and negative predictive value 87,9%; This rather low sensitivity and specificity determined that PET-CT was only able to distinguish 7 cases of ypCR from a total of 11 patients. Conclusions We conclude that 18-F PET-CT performed five to seven weeks after the end of CRT can visualise functional tumour response in LARC. In contrast, metabolic imaging with 18-F PET-CT is not able to predict patients with ypCR accurately.

  1. Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jiazhou; Zhao, Kuaike; Peng, Jiayuan; Xie, Jiang; Chen, Junchao; Zhang, Zhen; Hu, Weigang, E-mail: jackhuwg@gmail.com [Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032 (China); Jin, Xiance [The 1st Affiliated Hospital of Wenzhou Medical College, Wenzhou, Zhejiang 325000 (China); Studenski, Matthew [Department of Radiation Oncology, University of Miami-Miller School of Medicine, Miami, Florida 33136 (United States)

    2015-02-15

    Purpose: To investigate the feasibility of the dosimetric Pareto front (PF) prediction based on patient’s anatomic and dosimetric parameters for esophageal cancer patients. Methods: Eighty esophagus patients in the authors’ institution were enrolled in this study. A total of 2928 intensity-modulated radiotherapy plans were obtained and used to generate PF for each patient. On average, each patient had 36.6 plans. The anatomic and dosimetric features were extracted from these plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose, and PTV homogeneity index were recorded for each plan. Principal component analysis was used to extract overlap volume histogram (OVH) features between PTV and other organs at risk. The full dataset was separated into two parts; a training dataset and a validation dataset. The prediction outcomes were the MHD and MLD. The spearman’s rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The stepwise multiple regression method was used to fit the PF. The cross validation method was used to evaluate the model. Results: With 1000 repetitions, the mean prediction error of the MHD was 469 cGy. The most correlated factor was the first principal components of the OVH between heart and PTV and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 284 cGy. The most correlated factors were the first principal components of the OVH between heart and PTV and the overlap between lung and PTV in Z-axis. Conclusions: It is feasible to use patients’ anatomic and dosimetric features to generate a predicted Pareto front. Additional samples and further studies are required improve the prediction model.

  2. Dose-volumetric parameters for predicting hypothyroidism after radiotherapy for head and neck cancer

    International Nuclear Information System (INIS)

    To investigate predictors affecting the development of hypothyroidism after radiotherapy for head and neck cancer, focusing on radiation dose-volumetric parameters, and to determine the appropriate radiation dose-volumetric threshold of radiation-induced hypothyroidism. A total of 114 patients with head and neck cancer whose radiotherapy fields included the thyroid gland were analysed. The purpose of the radiotherapy was either definitive (n=81) or post-operative (n=33). Thyroid function was monitored before starting radiotherapy and after completion of radiotherapy at 1 month, 6 months, 1 year and 2 years. A diagnosis of hypothyroidism was based on a thyroid stimulating hormone value greater than the maximum value of laboratory range, regardless of symptoms. In all patients, dose volumetric parameters were analysed. Median follow-up duration was 25 months (range; 6-38). Forty-six percent of the patients were diagnosed as hypothyroidism after a median time of 8 months (range; 1-24). There were no significant differences in the distribution of age, gender, surgery, radiotherapy technique and chemotherapy between the euthyroid group and the hypothyroid group. In univariate analysis, the mean dose and V35-V50 results were significantly associated with hypothyroidism. The V45 is the only variable that independently contributes to the prediction of hypothyroidism in multivariate analysis and V45 of 50% was a threshold value. If V45 was <50%, the cumulative incidence of hypothyroidism at 1 year was 22.8%, whereas the incidence was 56.1% if V45 was ≥50%. (P=0.034). The V45 may predict risk of developing hypothyroidism after radiotherapy for head and neck cancer, and a V45 of 50% can be a useful dose-volumetric threshold of radiation-induced hypothyroidism. (author)

  3. Interrogating differences in expression of targeted gene sets to predict breast cancer outcome

    International Nuclear Information System (INIS)

    Genomics provides opportunities to develop precise tests for diagnostics, therapy selection and monitoring. From analyses of our studies and those of published results, 32 candidate genes were identified, whose expression appears related to clinical outcome of breast cancer. Expression of these genes was validated by qPCR and correlated with clinical follow-up to identify a gene subset for development of a prognostic test. RNA was isolated from 225 frozen invasive ductal carcinomas,and qRT-PCR was performed. Univariate hazard ratios and 95% confidence intervals for breast cancer mortality and recurrence were calculated for each of the 32 candidate genes. A multivariable gene expression model for predicting each outcome was determined using the LASSO, with 1000 splits of the data into training and testing sets to determine predictive accuracy based on the C-index. Models with gene expression data were compared to models with standard clinical covariates and models with both gene expression and clinical covariates. Univariate analyses revealed over-expression of RABEP1, PGR, NAT1, PTP4A2, SLC39A6, ESR1, EVL, TBC1D9, FUT8, and SCUBE2 were all associated with reduced time to disease-related mortality (HR between 0.8 and 0.91, adjusted p < 0.05), while RABEP1, PGR, SLC39A6, and FUT8 were also associated with reduced recurrence times. Multivariable analyses using the LASSO revealed PGR, ESR1, NAT1, GABRP, TBC1D9, SLC39A6, and LRBA to be the most important predictors for both disease mortality and recurrence. Median C-indexes on test data sets for the gene expression, clinical, and combined models were 0.65, 0.63, and 0.65 for disease mortality and 0.64, 0.63, and 0.66 for disease recurrence, respectively. Molecular signatures consisting of five genes (PGR, GABRP, TBC1D9, SLC39A6 and LRBA) for disease mortality and of six genes (PGR, ESR1, GABRP, TBC1D9, SLC39A6 and LRBA) for disease recurrence were identified. These signatures were as effective as standard clinical

  4. The Role of Prion Protein Expression in Predicting Gastric Cancer Prognosis

    Science.gov (United States)

    Tang, Zhaoqing; Ma, Ji; Zhang, Wei; Gong, Changguo; He, Jing; Wang, Ying; Yu, Guohua; Yuan, Chonggang; Wang, Xuefei; Sun, Yihong; Ma, Jiyan; Liu, Fenglin; Zhao, Yulan

    2016-01-01

    Previous reports indicated that prion protein (PrP) is involved in gastric cancer (GC) development and progression, but its role in GC prognosis has been poorly characterized. A total of 480 GC patients were recruited in this retrospective study. PrP expression in cancerous and non-cancerous gastric tissues was detected by using the tissue microarray and immunohistochemical staining techniques. Our results showed that the PrP expression in GC was significantly less frequent than that in the non-cancerous gastric tissue (44.4% vs 66.4%, P < 0.001). Cox regression analysis revealed that PrP expression was associated with TNM stage, survival status and survival time. GC patients with higher TNM stages (stages II, III and IV) had significantly lower PrP expression levels in tumors than those with lower TNM stages (stages 0 and I). Kaplan-Meier survival curves revealed that negative PrP expression was associated with poor overall survival (log-rank test: P < 0.001). The mean survival time for patients with negative PrP expression was significant lower than those with positive PrP expression (43.0±28.5m vs. 53.9±31.1m, P<0.001). In multivariate Cox hazard regression, PrP expression was an independent prognostic factor for GC survival, with a HR (hazard ratio) of 0.687 (95%CI:0.520-0.907, P=0.008). Our results revealed that negative PrP expression could independently predict worse outcome in GC and thereby could be used to guide the clinical practice. PMID:27313789

  5. Androgen receptor expression predicts breast cancer survival: the role of genetic and epigenetic events

    International Nuclear Information System (INIS)

    Breast cancer outcome, including response to therapy, risk of metastasis and survival, is difficult to predict using currently available methods, highlighting the urgent need for more informative biomarkers. Androgen receptor (AR) has been implicated in breast carcinogenesis however its potential to be an informative biomarker has yet to be fully explored. In this study, AR protein levels were determined in a cohort of 73 Grade III invasive breast ductal adenocarcinomas. The levels of Androgen receptor protein in a cohort of breast tumour samples was determined by immunohistochemistry and the results were compared with clinical characteristics, including survival. The role of defects in the regulation of Androgen receptor gene expression were examined by mutation and methylation screening of the 5' end of the gene, reporter assays of the 5' and 3' end of the AR gene, and searching for miRNAs that may regulate AR gene expression. AR was expressed in 56% of tumours and expression was significantly inversely associated with 10-year survival (P = 0.004). An investigation into the mechanisms responsible for the loss of AR expression revealed that hypermethylation of the AR promoter is associated with loss of AR expression in breast cancer cells but not in primary breast tumours. In AR negative breast tumours, mutation screening identified the same mutation (T105A) in the 5'UTR of two AR negative breast cancer patients but not reported in the normal human population. Reporter assay analysis of this mutation however found no evidence for a negative impact on AR 5'UTR activity. The role of miR-124 in regulating AR expression was also investigated, however no evidence for this was found. This study highlights the potential for AR expression to be an informative biomarker for breast cancer survival and sets the scene for a more comprehensive investigation of the molecular basis of this phenomenon

  6. Serum nucleosomes during neoadjuvant chemotherapy in patients with cervical cancer. Predictive and prognostic significance

    Directory of Open Access Journals (Sweden)

    Cetina Lucely

    2005-06-01

    Full Text Available Abstract Background It has been shown that free DNA circulates in serum plasma of patients with cancer and that at least part is present in the form of oligo- and monucleosomes, a marker of cell death. Preliminary data has shown a good correlation between decrease of nucleosomes with response and prognosis. Here, we performed pre- and post-chemotherapy determinations of serum nucleosomes with an enzyme-linked immunosorbent assay (ELISA method in a group of patients with cervical cancer receiving neoadjuvant chemotherapy. Methods From December 2000 to June 2001, 41 patients with cervical cancer staged as FIGO stages IB2-IIIB received three 21-day courses of carboplatin and paclitaxel, both administered at day 1; then, patients underwent radical hysterectomy. Nucleosomes were measured the day before (baseline, at day seven of the first course and day seven of the third course of chemotherapy. Values of nucleosomes were analyzed with regard to pathologic response and to time to progression-free and overall survival. Results All patients completed chemotherapy, were evaluable for pathologic response, and had nucleosome levels determined. At a mean follow-up of 23 months (range, 7–26 months, projected progression time and overall survival were 80.3 and 80.4%, respectively. Mean differential values of nucleosomes were lower in the third course as compared with the first course (p >0.001. The decrease in the third course correlated with pathologic response (p = 0.041. Survival analysis showed a statistically significant, better progression-free and survival time in patients who showed lower levels at the third course (p = 0.0243 and p = 0.0260, respectively. Cox regression analysis demonstrated that nucleosome increase in the third course increased risk of death to 6.86 (95% confidence interval [CI 95%], 0.84–56.0. Conclusion Serum nucleosomes may have a predictive role for response and prognostic significance in patients with cervical cancer

  7. Aneuploidy involving chromosome 1 may be an early predictive marker of intestinal type gastric cancer

    Energy Technology Data Exchange (ETDEWEB)

    Williams, L. [Royal Glamorgan Hospital, Ynysmaerdy, Llantrisant CF72 8XR (United Kingdom); Somasekar, A. [Institute of Life Science, Swansea School of Medicine, Swansea University, Swansea SA28PP (United Kingdom); Neath Port Talbot Hospital, Abertawe Bro Morgannwg University NHS Trust, Baglan Way, Port Talbot SA12 7BX (United Kingdom); Davies, D.J.; Cronin, J.; Doak, S.H. [Institute of Life Science, Swansea School of Medicine, Swansea University, Swansea SA28PP (United Kingdom); Alcolado, R. [Royal Glamorgan Hospital, Ynysmaerdy, Llantrisant CF72 8XR (United Kingdom); Williams, J.G. [Neath Port Talbot Hospital, Abertawe Bro Morgannwg University NHS Trust, Baglan Way, Port Talbot SA12 7BX (United Kingdom); Griffiths, A.P. [Department of Histopathology, Morriston Hospital, Abertawe Bro Morgannwg University NHS Trust, Morriston, SA66NL (United Kingdom); Baxter, J.N. [Department of Surgery, Morriston Hospital, Abertawe Bro Morgannwg University NHS Trust, Morriston, SA66NL (United Kingdom); Jenkins, G.J.S., E-mail: g.j.jenkins@swansea.ac.uk [Institute of Life Science, Swansea School of Medicine, Swansea University, Swansea SA28PP (United Kingdom)

    2009-10-02

    Intestinal type gastric cancer is a significant cause of mortality, therefore a better understanding of its molecular basis is required. We assessed if either aneuploidy or activity of the oncogenic transcription factor nuclear factor kappa B (NF-{kappa}B), increased incrementally during pre-malignant gastric histological progression and also if they correlated with each other in patient samples, as they are both induced by oxygen free radicals. In a prospective study of 54 (aneuploidy) and 59 (NF-{kappa}B) consecutive patients, aneuploidy was assessed by interphase fluorescent in situ hybridisation (FISH) for chromosome 1. NF-{kappa}B was assessed by expression of interleukin-8 (IL-8), and in a subset, by immunohistochemistry (IHC) for active p65. Aneuploidy levels increased incrementally across the histological series. 2.76% of cells with normal histology (95% CI, 2.14-3.38%) showed background levels of aneuploidy, this increased to averages of 3.78% (95% CI, 3.21-4.35%), 5.89% (95% CI, 3.72-8.06%) and 7.29% (95% CI, 4.73-9.85%) of cells from patients with gastritis, Helicobacter pylori positive gastritis and atrophy/intestinal metaplasia (IM) respectively. IL-8 expression was only increased in patients with current H. pylori infection. NF-{kappa}B analysis showed some increased p65 activity in inflamed tissues. IL-8 expression and aneuploidy level were not linked in individual patients. Aneuploidy levels increased incrementally during histological progression; were significantly elevated at very early stages of neoplastic progression and could well be linked to cancer development and used to assess cancer risk. Reactive oxygen species (ROS) induced in early gastric cancer are presumably responsible for the stepwise accumulation of this particular mutation, i.e. aneuploidy. Hence, aneuploidy measured by fluorescent in situ hybridisation (FISH) coupled to brush cytology, would be worthy of consideration as a predictive marker in gastric cancer and could be

  8. Predictive value of breast cancer cognitions and attitudes toward genetic testing on women’s interest in genetic testing for breast cancer risk

    OpenAIRE

    Bengel, Jürgen; Barth, Jürgen; Reitz, Frauke

    2004-01-01

    In the past years advances in genetic technologies have led to an increased interest in predictive genetic testing for breast cancer risk. Studies in the US and UK reported an increasing interest among women of the general public in genetic testing for breast cancer risk, although the benefit of such a test is questionable for low risk women. The aim of the present study was to identify factors that predict interest in genetic testing of German women in the general public. Women with neither ...

  9. Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI.

    Science.gov (United States)

    Tudorica, Alina; Oh, Karen Y; Chui, Stephen Y-C; Roy, Nicole; Troxell, Megan L; Naik, Arpana; Kemmer, Kathleen A; Chen, Yiyi; Holtorf, Megan L; Afzal, Aneela; Springer, Charles S; Li, Xin; Huang, Wei

    2016-02-01

    The purpose is to compare quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) metrics with imaging tumor size for early prediction of breast cancer response to neoadjuvant chemotherapy (NACT) and evaluation of residual cancer burden (RCB). Twenty-eight patients with 29 primary breast tumors underwent DCE-MRI exams before, after one cycle of, at midpoint of, and after NACT. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed with the standard Tofts and Shutter-Speed models (TM and SSM). After one NACT cycle the percent changes of DCE-MRI parameters K(trans) (contrast agent plasma/interstitium transfer rate constant), ve (extravascular and extracellular volume fraction), kep (intravasation rate constant), and SSM-unique τi (mean intracellular water lifetime) are good to excellent early predictors of pathologic complete response (pCR) vs. non-pCR, with univariate logistic regression C statistics value in the range of 0.804 to 0.967. ve values after one cycle and at NACT midpoint are also good predictors of response, with C ranging 0.845 to 0.897. However, RECIST LD changes are poor predictors with C = 0.609 and 0.673, respectively. Post-NACT K(trans), τi, and RECIST LD show statistically significant (P < .05) correlations with RCB. The performances of TM and SSM analyses for early prediction of response and RCB evaluation are comparable. In conclusion, quantitative DCE-MRI parameters are superior to imaging tumor size for early prediction of therapy response. Both TM and SSM analyses are effective for therapy response evaluation. However, the τi parameter derived only with SSM analysis allows the unique opportunity to potentially quantify therapy-induced changes in tumor energetic metabolism. PMID:26947876

  10. Non-coding RNAs Enabling Prognostic Stratification and Prediction of Therapeutic Response in Colorectal Cancer Patients.

    Science.gov (United States)

    Perakis, Samantha O; Thomas, Joseph E; Pichler, Martin

    2016-01-01

    Colorectal cancer (CRC) is a heterogeneous disease and current treatment options for patients are associated with a wide range of outcomes and tumor responses. Although the traditional TNM staging system continues to serve as a crucial tool for estimating CRC prognosis and for stratification of treatment choices and long-term survival, it remains limited as it relies on macroscopic features and cases of surgical resection, fails to incorporate new molecular data and information, and cannot perfectly predict the variety of outcomes and responses to treatment associated with tumors of the same stage. Although additional histopathologic features have recently been applied in order to better classify individual tumors, the future might incorporate the use of novel molecular and genetic markers in order to maximize therapeutic outcome and to provide accurate prognosis. Such novel biomarkers, in addition to individual patient tumor phenotyping and other validated genetic markers, could facilitate the prediction of risk of progression in CRC patients and help assess overall survival. Recent findings point to the emerging role of non-protein-coding regions of the genome in their contribution to the progression of cancer and tumor formation. Two major subclasses of non-coding RNAs (ncRNAs), microRNAs and long non-coding RNAs, are often dysregulated in CRC and have demonstrated their diagnostic and prognostic potential as biomarkers. These ncRNAs are promising molecular classifiers and could assist in the stratification of patients into appropriate risk groups to guide therapeutic decisions and their expression patterns could help determine prognosis and predict therapeutic options in CRC. PMID:27573901

  11. Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI

    Directory of Open Access Journals (Sweden)

    Alina Tudorica

    2016-02-01

    Full Text Available The purpose is to compare quantitative dynamic contrast-enhanced (DCE magnetic resonance imaging (MRI metrics with imaging tumor size for early prediction of breast cancer response to neoadjuvant chemotherapy (NACT and evaluation of residual cancer burden (RCB. Twenty-eight patients with 29 primary breast tumors underwent DCE-MRI exams before, after one cycle of, at midpoint of, and after NACT. MRI tumor size in the longest diameter (LD was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors guidelines. Pharmacokinetic analyses of DCE-MRI data were performed with the standard Tofts and Shutter-Speed models (TM and SSM. After one NACT cycle the percent changes of DCE-MRI parameters Ktrans (contrast agent plasma/interstitium transfer rate constant, ve (extravascular and extracellular volume fraction, kep (intravasation rate constant, and SSM-unique τi (mean intracellular water lifetime are good to excellent early predictors of pathologic complete response (pCR vs. non-pCR, with univariate logistic regression C statistics value in the range of 0.804 to 0.967. ve values after one cycle and at NACT midpoint are also good predictors of response, with C ranging 0.845 to 0.897. However, RECIST LD changes are poor predictors with C = 0.609 and 0.673, respectively. Post-NACT Ktrans, τi, and RECIST LD show statistically significant (P < .05 correlations with RCB. The performances of TM and SSM analyses for early prediction of response and RCB evaluation are comparable. In conclusion, quantitative DCE-MRI parameters are superior to imaging tumor size for early prediction of therapy response. Both TM and SSM analyses are effective for therapy response evaluation. However, the τi parameter derived only with SSM analysis allows the unique opportunity to potentially quantify therapy-induced changes in tumor energetic metabolism.

  12. Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures.

    Directory of Open Access Journals (Sweden)

    Thomas Karn

    Full Text Available BACKGROUND: Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes. METHODOLOGY/PRINCIPAL FINDINGS: We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases. CONCLUSIONS/SIGNIFICANCE: Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets and a smaller (n = 26 probesets prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71-9.48; P = 0.001 and 4.08 (95% CI 1.79-9.28; P = 0.001, respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588 to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.

  13. Clinical applications of gene-based risk prediction for Lung Cancer and the central role of Chronic Obstructive Pulmonary Disease.

    Directory of Open Access Journals (Sweden)

    Robert P Young

    2012-10-01

    Full Text Available Lung cancer is the leading cause of cancer death worldwide and nearly 90% of cases are attributable to smoking. Quitting smoking and early diagnosis of lung cancer, through computed tomographic screening, are the only ways to reduce mortality from lung cancer. Recent epidemiological studies show that risk prediction for lung cancer is optimized by using multivariate risk models that include age, smoking exposure, history of chronic obstructive pulmonary disease (COPD, family history of lung cancer and body mass index. Several recent epidemiological studies have shown that COPD predates lung cancer in 65-70% of cases and confers a 4-6 fold greater risk of lung cancer compared to smokers with normal lung function. In separate studies, genome-wide association studies have identified a number of genetic variants associated with COPD or lung cancer, several of which overlap. In a case control study, where smokers with normal lungs were compared to those who had spirometry-defined COPD and histology confirmed lung cancer, several of these overlapping variants were shown to confer the same susceptibility or protective effects on both COPD and lung cancer (independent of COPD status. In this perspective article, we demonstrate how combining clinical data with genetic variants can help identify heavy smokers at the greatest risk of lung cancer. Using this approach, we found that gene-based risk testing helped engage smokers in risk mitigating activities like quitting smoking and undertaking lung cancer screening. We suggest that such an approach could facilitate the targeted selection of smokers for cost-effective, life-saving interventions.

  14. Early prediction of lung cancer recurrence after stereotactic radiotherapy using second order texture statistics

    Science.gov (United States)

    Mattonen, Sarah A.; Palma, David A.; Haasbeek, Cornelis J. A.; Senan, Suresh; Ward, Aaron D.

    2014-03-01

    Benign radiation-induced lung injury is a common finding following stereotactic ablative radiotherapy (SABR) for lung cancer, and is often difficult to differentiate from a recurring tumour due to the ablative doses and highly conformal treatment with SABR. Current approaches to treatment response assessment have shown limited ability to predict recurrence within 6 months of treatment. The purpose of our study was to evaluate the accuracy of second order texture statistics for prediction of eventual recurrence based on computed tomography (CT) images acquired within 6 months of treatment, and compare with the performance of first order appearance and lesion size measures. Consolidative and ground-glass opacity (GGO) regions were manually delineated on post-SABR CT images. Automatic consolidation expansion was also investigated to act as a surrogate for GGO position. The top features for prediction of recurrence were all texture features within the GGO and included energy, entropy, correlation, inertia, and first order texture (standard deviation of density). These predicted recurrence with 2-fold cross validation (CV) accuracies of 70-77% at 2- 5 months post-SABR, with energy, entropy, and first order texture having leave-one-out CV accuracies greater than 80%. Our results also suggest that automatic expansion of the consolidation region could eliminate the need for manual delineation, and produced reproducible results when compared to manually delineated GGO. If validated on a larger data set, this could lead to a clinically useful computer-aided diagnosis system for prediction of recurrence within 6 months of SABR and allow for early salvage therapy for patients with recurrence.

  15. Validation that Metabolic Tumor Volume Predicts Outcome in Head-and-Neck Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Tang, Chad; Murphy, James D.; Khong, Brian; La, Trang H. [Department of Radiation Oncology, Stanford University, Stanford, CA (United States); Kong, Christina [Department of Pathology, Stanford University, Stanford, CA (United States); Fischbein, Nancy J. [Department of Radiology, Stanford University, Stanford, CA (United States); Colevas, A. Dimitrios [Division of Medical Oncology, Department of Medicine, Stanford University, Stanford, CA (United States); Iagaru, Andrei H. [Department of Radiology, Stanford University, Stanford, CA (United States); Graves, Edward E.; Loo, Billy W. [Department of Radiation Oncology, Stanford University, Stanford, CA (United States); Le, Quynh-Thu, E-mail: qle@stanford.edu [Department of Radiation Oncology, Stanford University, Stanford, CA (United States)

    2012-08-01

    Purpose: We have previously reported that metabolic tumor volume (MTV) obtained from pretreatment {sup 18}F-fluorodeoxydeglucose positron emission tomography (FDG PET)/ computed tomography (CT) predicted outcome in patients with head-and-neck cancer (HNC). The purpose of this study was to validate these results on an independent dataset, determine whether the primary tumor or nodal MTV drives this correlation, and explore the interaction with p16{sup INK4a} status as a surrogate marker for human papillomavirus (HPV). Methods and Materials: The validation dataset in this study included 83 patients with squamous cell HNC who had a FDG PET/CT scan before receiving definitive radiotherapy. MTV and maximum standardized uptake value (SUV{sub max}) were calculated for the primary tumor, the involved nodes, and the combination of both. The primary endpoint was to validate that MTV predicted progression-free survival and overall survival. Secondary analyses included determining the prognostic utility of primary tumor vs. nodal MTV. Results: Similarly to our prior findings, an increase in total MTV of 17 cm{sup 3} (difference between the 75th and 25th percentiles) was associated with a 2.1-fold increase in the risk of disease progression (p = 0.0002) and a 2.0-fold increase in the risk of death (p = 0.0048). SUV{sub max} was not associated with either outcome. Primary tumor MTV predicted progression-free (hazard ratio [HR] = 1.94; p < 0.0001) and overall (HR = 1.57; p < 0.0001) survival, whereas nodal MTV did not. In addition, MTV predicted progression-free (HR = 4.23; p < 0.0001) and overall (HR = 3.21; p = 0.0029) survival in patients with p16{sup INK4a}-positive oropharyngeal cancer. Conclusions: This study validates our previous findings that MTV independently predicts outcomes in HNC. MTV should be considered as a potential risk-stratifying biomarker in future studies of HNC.

  16. How accurate is our prediction of biopsy outcome? PCA3-based nomograms in personalized diagnosis of prostate cancer

    OpenAIRE

    Salagierski, Maciej; Sosnowski, Marek; Schalken, Jack A.

    2012-01-01

    Purpose The sensitivity and specificity of prostate-specific antigen (PSA) alone to select men for prostate biopsy remain suboptimal. This review aims at presenting a review of current prostate cancer (PCa) nomograms that incorporate Prostate Cancer Gene 3 (PCA3), which was designed to outperform PSA at predicting biopsy outcome. Materials and methods The PubMed database and current literature search was conducted for reports on PCA3-based nomograms and tools for examining the risk of a posit...

  17. C-reactive protein and procalcitonin predict anastomotic leaks following colorectal cancer resections – a prospective study

    OpenAIRE

    Zawadzki, Marek; Czarnecki, Roman; Rzaca, Marek; Obuszko, Zbigniew; Velchuru, Vamsi Ramana; Witkiewicz, Wojciech

    2016-01-01

    Introduction Early safe discharge is paramount for the success of ERAS following colorectal cancer resections. Anastomotic leakage (AL) has high morbidity, particularly if the patient has been discharged to the community. Aim To evaluate whether C-reactive protein (CRP) and procalcitonin (PCT) can predict AL before early discharge. Material and methods Fifty-five consecutive patients undergoing open and robotic colorectal cancer resections were included. C-reactive protein and PCT were measur...

  18. The value of preoperative lung spirometry test for predicting the operative risk in patients undergoing gastric cancer surgery

    OpenAIRE

    Jeong, Oh; Ryu, Seong Yeop; Park, Young Kyu

    2012-01-01

    Purpose We evaluated the predictive value of preoperative lung spirometry test for postoperative morbidity and the nature of complications related to an abnormal pulmonary function after gastric cancer surgery. Methods Between February 2009 and March 2010, 538 gastric cancer patients who underwent laparoscopic (n = 247) and open gastrectomy (n = 291) were divided into the normal (forced expiratory volume in 1 second [FEV1]/forced vital capacity [FVC] ≥ 0.7, n = 441) and abnormal pulmonary fun...

  19. The clinical value of hybrid sentinel lymphoscintigraphy to predict metastatic sentinel lymph nodes in breast cancer

    International Nuclear Information System (INIS)

    Hybrid imaging techniques can provide functional and anatomical information about sentinel lymph nodes in breast cancer. Our aim in this study was to evaluate which imaging parameters on hybrid sentinel lymphoscintigraphy predicted metastatic involvement of sentinel lymph nodes (SLNs) in patients with breast cancer. Among 56 patients who underwent conventional sentinel lymphoscintigraphy, 45 patients (age, 53.1 ± 9.5 years) underwent hybrid sentinel lymphoscintigraphy using a single-photon emission computed tomography (SPECT)/computed tomography (CT) gamma camera. On hybrid SPECT/CT images, we compared the shape and size (long-to-short axis [L/S] ratio) of the SLN, and SLN/periareolar injection site (S/P) count ratio between metastatic and non-metastatic SLNs. Metastatic involvement of sentinel lymph nodes was confirmed by pathological biopsy. Pathological biopsy revealed that 21 patients (46.7 %) had metastatic SLNs, while 24 (53.3 %) had non-metastatic SLNs. In the 21 patients with metastatic SLNs, the SLN was mostly round (57.1 %) or had an eccentric cortical rim (38.1 %). Of 24 patients with non-metastatic SLNs, 13 patients (54.1 %) had an SLN with a C-shape rim or eccentric cortex. L/S ratio was 2.04 for metastatic SLNs and 2.38 for non-metastatic SLNs. Seven (33 %) patients had T1 primary tumors and 14 (66 %) had T2 primary tumors in the metastatic SLN group. In contrast, 18 (75 %) patients had T1 primary tumors and six (25 %) had T2 tumors in the non-metastatic SLN group. S/P count ratio was significantly lower in the metastatic SLN group than the non-metastatic SLN group for those patients with a T1 primary tumor (p = 0.007). Hybrid SPECT/CT offers the physiologic data of SPECT together with the anatomic data of CT in a single image. This hybrid imaging improved the anatomic localization of SLNs in breast cancer patients and predicted the metastatic involvement of SLNs in the subgroup of breast cancer patients with T1 primary tumors

  20. Predicting the efficacy of trastuzumab-based therapy in breast cancer: current standards and future strategies.

    Science.gov (United States)

    Singer, Christian F; Köstler, Wolfgang J; Hudelist, Gernot

    2008-12-01

    Breast cancer is the most common female malignancy in many industrialized countries. Approximately one fourth of all women diagnosed with early breast cancer present with tumors that are characterized by erbB2 amplification. While the associated Her-2/neu receptor overexpression results in a high risk of relapse and poor prognosis, these tumors also represent a target for a selective monoclonal antibody therapy with trastuzumab (Herceptin). The combination of trastuzumab with chemotherapy has led to a considerable reduction of recurrences and to a significant reduction in breast cancer mortality both in the adjuvant and metastatic setting. Unfortunately, despite Her-2/neu overexpression, not all patients equally benefit from trastuzumab treatment, and almost all women with metastatic breast cancer eventually progress during antibody therapy. Moreover, trastuzumab is burdened with cardiotoxicity, thus increasing the risk of symptomatic congestive heart failure. In addition, the marginal costs for a 1 year therapy of trastuzumab-based therapy, which is currently considered to be the most effective treatment regimen in the adjuvant setting, may amount for up to US$ 40.000. Testing for erbB2 oncogene amplification by fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH), respectively, and staining for Her-2/neu receptor overexpression by immunohistochemistry (IHC) represent the current standard for determining patient eligibility for trastuzumab-based therapy. However, while the negative predictive value of these assays for predicting the absence of benefit from trastuzumab-based therapy is sufficiently high, their positive predictive value remains insufficient, i.e. only a proportion of patients selected by these tests substantially benefit from trastuzumab-containing regimen. Accordingly, over the last years a number of biomarkers have been evaluated in their potential to predict response to trastuzumab-based therapies. These include

  1. A Nomogram for Predicting the Pathological Response of Axillary Lymph Node Metastasis in Breast Cancer Patients.

    Science.gov (United States)

    Jin, Xi; Jiang, Yi-Zhou; Chen, Sheng; Shao, Zhi-Ming; Di, Gen-Hong

    2016-01-01

    The value of sentinel lymph node biopsy (SLNB) in post-neoadjuvant chemotherapy (NCT) patients is still controversial. We aimed to identify predictors and construct a nomogram for predicting the pathologically complete response (pCR) of axillary lymph nodes (ALNs) after NCT in node positive breast cancer patients. In total, 426 patients with pathologically proven ALN metastasis before NCT were enrolled, randomized 1:1 and divided into a training set and a validation set. We developed a nomogram based on independent predictors for ALN pCR identified by multivariate logistic regression as well as clinical significant predictors. The multivariate logistic regression analysis showed that hormone receptor (HR) status, human epidermal growth factor 2 (HER2) status and Ki67 index were independent predictors. The nomogram was thereby constructed by those independent predictors as well as tumor size and NCT regimens. The areas under the receiver operating characteristic curve of the training set and the validation set were 0.804 and 0.749, respectively. We constructed a nomogram for predicting ALN pCR in patients who received NCT. Our nomogram can improve risk stratification, accurately predict post-NCT ALN status and avoid unnecessary ALN dissection. PMID:27576704

  2. Predictive genetic testing in children: constitutional mismatch repair deficiency cancer predisposing syndrome.

    Science.gov (United States)

    Bruwer, Zandrè; Algar, Ursula; Vorster, Alvera; Fieggen, Karen; Davidson, Alan; Goldberg, Paul; Wainwright, Helen; Ramesar, Rajkumar

    2014-04-01

    Biallelic germline mutations in mismatch repair genes predispose to constitutional mismatch repair deficiency syndrome (CMMR-D). The condition is characterized by a broad spectrum of early-onset tumors, including hematological, brain and bowel and is frequently associated with features of Neurofibromatosis type 1. Few definitive screening recommendations have been suggested and no published reports have described predictive testing. We report on the first case of predictive testing for CMMR-D following the identification of two non-consanguineous parents, with the same heterozygous mutation in MLH1: c.1528C > T. The genetic counseling offered to the family, for their two at-risk daughters, is discussed with a focus on the ethical considerations of testing children for known cancer-causing variants. The challenges that are encountered when reporting on heterozygosity in a child younger than 18 years (disclosure of carrier status and risk for Lynch syndrome), when discovered during testing for homozygosity, are addressed. In addition, the identification of CMMR-D in a three year old, and the recommended clinical surveillance that was proposed for this individual is discussed. Despite predictive testing and presymptomatic screening, the sudden death of the child with CMMR-D syndrome occurred 6 months after her last surveillance MRI. This report further highlights the difficulty of developing guidelines, as a result of the rarity of cases and diversity of presentation.

  3. Current measures of metabolic heterogeneity within cervical cancer do not predict disease outcome

    Directory of Open Access Journals (Sweden)

    Brooks Frank J

    2011-06-01

    Full Text Available Abstract Background A previous study evaluated the intra-tumoral heterogeneity observed in the uptake of F-18 fluorodeoxyglucose (FDG in pre-treatment positron emission tomography (PET scans of cancers of the uterine cervix as an indicator of disease outcome. This was done via a novel statistic which ostensibly measured the spatial variations in intra-tumoral metabolic activity. In this work, we argue that statistic is intrinsically non-spatial, and that the apparent delineation between unsuccessfully- and successfully-treated patient groups via that statistic is spurious. Methods We first offer a straightforward mathematical demonstration of our argument. Next, we recapitulate an assiduous re-analysis of the originally published data which was derived from FDG-PET imagery. Finally, we present the results of a principal component analysis of FDG-PET images similar to those previously analyzed. Results We find that the previously published measure of intra-tumoral heterogeneity is intrinsically non-spatial, and actually is only a surrogate for tumor volume. We also find that an optimized linear combination of more canonical heterogeneity quantifiers does not predict disease outcome. Conclusions Current measures of intra-tumoral metabolic activity are not predictive of disease outcome as has been claimed previously. The implications of this finding are: clinical categorization of patients based upon these statistics is invalid; more sophisticated, and perhaps innately-geometric, quantifications of metabolic activity are required for predicting disease outcome.

  4. PREDICTION OF NON-SENTINEL LYMPH NODE METASTASES IN BREAST CANCER

    Institute of Scientific and Technical Information of China (English)

    张杰; 沈坤炜; 尼尔马; 柳光宇; 吴炅; 邵志敏; 沈镇宙

    2003-01-01

    Objective. To identify a subset of breast cancer patients in whom metastatic disease is confined on- ly to the sentinel lymph node(SLn). Methods. Sentinel lymph node biopsy is performed with the injecetion of Tc99m-SC, and a gamma probe. Sentinel node biopsy was compared with standard axillary dissection for its ability to reflect the final pathological status of the axillary nodes. The factors associated with non-SLN metastases were assessed in the univariate and multivariate analysis. Result. We successfully identified 91 out of 95 patients for SLN(95.8%). The accuracy of sentinel lymph node to predict the axillary lymph node status was 93.4%. Clinical tumor size and tumor grade were proved to be the independent predictive factors for non-SLN metastases by logistic regression model. Conclusio.In most cases, the gamma probe guided method is technically feasible for detecting sentinel nodes, accurately predicting the axillary lymph node status. A subset of the patients identified who have a low risk of non-SLN metastases may not require axillary lymph node dissection.

  5. Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression

    Science.gov (United States)

    Shayan, Zahra; Mezerji, Naser Mohammad Gholi; Shayan, Leila; Naseri, Parisa

    2016-01-01

    Background: Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. Methods: This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. Results: CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. Conclusion: The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

  6. Software engineering

    CERN Document Server

    Sommerville, Ian

    2010-01-01

    The ninth edition of Software Engineering presents a broad perspective of software engineering, focusing on the processes and techniques fundamental to the creation of reliable, software systems. Increased coverage of agile methods and software reuse, along with coverage of 'traditional' plan-driven software engineering, gives readers the most up-to-date view of the field currently available. Practical case studies, a full set of easy-to-access supplements, and extensive web resources make teaching the course easier than ever.

  7. Radiomics versus physician assessment for the early prediction of local cancer recurrence after stereotactic radiotherapy for lung cancer

    Science.gov (United States)

    Mattonen, Sarah A.; Johnson, Carol; Palma, David A.; Rodrigues, George; Louie, Alexander V.; Senan, Suresh; Yeung, Timothy P. C.; Ward, Aaron D.

    2016-03-01

    Stereotactic ablative radiotherapy (SABR) has recently become a standard treatment option for patients with early-stage lung cancer, which achieves local control rates similar to surgery. Local recurrence following SABR typically presents after one year post-treatment. However, benign radiological changes mimicking local recurrence can appear on CT imaging following SABR, complicating the assessment of response. We hypothesize that subtle changes on early post- SABR CT images are important in predicting the eventual incidence of local recurrence and would be extremely valuable to support timely salvage interventions. The objective of this study was to extract radiomic image features on post-SABR follow-up images for 45 patients (15 with local recurrence and 30 without) to aid in the early prediction of local recurrence. Three blinded thoracic radiation oncologists were also asked to score follow-up images as benign injury or local recurrence. A radiomic signature consisting of five image features demonstrated a classification error of 24%, false positive rate (FPR) of 24%, false negative rate (FNR) of 23%, and area under the receiver operating characteristic curve (AUC) of 0.85 at 2-5 months post-SABR. At the same time point, three physicians assessed the majority of images as benign injury for overall errors of 34-37%, FPRs of 0-4%, and FNRs of 100%. These results suggest that radiomics can detect early changes associated with local recurrence which are not typically considered by physicians. We aim to develop a decision support system which could potentially allow for early salvage therapy of patients with local recurrence following SABR.

  8. Methylation status at HYAL2 predicts overall and progression-free survival of colon cancer patients under 5-FU chemotherapy.

    Science.gov (United States)

    Pfütze, Katrin; Benner, Axel; Hoffmeister, Michael; Jansen, Lina; Yang, Rongxi; Bläker, Hendrik; Herpel, Esther; Ulrich, Alexis; Ulrich, Cornelia M; Chang-Claude, Jenny; Brenner, Hermann; Burwinkel, Barbara

    2015-12-01

    DNA methylation variations in gene promoter regions are well documented tumor-specific alterations in human malignancies including colon cancer, which may influence tumor behavior and clinical outcome. As a subset of colon cancer patients does not benefit from adjuvant chemotherapy, predictive biomarkers are desirable. Here, we describe that DNA methylation levels at CpG loci of hyaluronoglucosaminidase 2 (HYLA2) could be used to identify stage II and III colon cancer patients who are most likely to benefit from 5-flourouracil (5-FU) chemotherapy with respect to overall survival and progression-free survival. PMID:26453961

  9. Serum CA125 is a novel predictive marker for pancreatic cancer metastasis and correlates with the metastasis-associated burden

    OpenAIRE

    Liu, Liang; Xu, Hua-Xiang; Wang, Wen-Quan; Wu, Chun-tao; Xiang, Jin-Feng; Liu, Chen; Long, Jiang; Xu, Jin; Fu, De-Liang; Ni, Quan-Xing; Houchen, Courtney W.; Postier, Russell G.; Li, Min; Yu, Xian-Jun

    2016-01-01

    This study evaluated potential of serum tumor markers to predict the incidence and intensity of pancreatic cancer metastasis as well as patient survival. Retrospective records from 905 patients and prospective data from 142 patients were collected from two high-volume institutions. The levels of eight serum tumor markers (CA19-9, CEA, CA242, CA72-4, CA50, CA125, CA153, and AFP) commonly used in gastroenterological cancer were analyzed in all stages of pancreatic cancer. Serum CA125 levels wer...

  10. A BRCA1-mutation associated DNA methylation signature in blood cells predicts sporadic breast cancer incidence and survival.

    OpenAIRE

    Anjum, S; Fourkala, E O; Zikan, M.; Wong, A.; Gentry-Maharaj, A.; Jones, A.; HARDY, R.; Cibula, D.; Kuh, D.; Jacobs, I. J.; Teschendorff, A.E.; Menon, U; Widschwendter, M

    2014-01-01

    Background BRCA1 mutation carriers have an 85% risk of developing breast cancer but the risk of developing non-hereditary breast cancer is difficult to assess. Our objective is to test whether a DNA methylation (DNAme) signature derived from BRCA1 mutation carriers is able to predict non-hereditary breast cancer. Methods In a case/control setting (72 BRCA1 mutation carriers and 72 BRCA1/2 wild type controls) blood cell DNA samples were profiled on the Illumina 27 k methylation array. Using th...

  11. Prealbumin/CRP Based Prognostic Score, a New Tool for Predicting Metastasis in Patients with Inoperable Gastric Cancer

    Directory of Open Access Journals (Sweden)

    Ali Esfahani

    2016-01-01

    Full Text Available Background. There is a considerable dissimilarity in the survival duration of the patients with gastric cancer. We aimed to assess the systemic inflammatory response (SIR and nutritional status of these patients before the commencement of chemotherapy to find the appropriate prognostic factors and define a new score for predicting metastasis. Methods. SIR was assessed using Glasgow Prognostic Score (GPS. Then a score was defined as prealbumin/CRP based prognostic score (PCPS to be compared with GPS for predicting metastasis and nutritional status. Results. 71 patients with gastric cancer were recruited in the study. 87% of patients had malnutrition. There was a statistical difference between those with metastatic (n=43 and those with nonmetastatic (n=28 gastric cancer according to levels of prealbumin and CRP; however they were not different regarding patient generated subjective global assessment (PG-SGA and GPS. The best cut-off value for prealbumin was determined at 0.20 mg/dL and PCPS could predict metastasis with 76.5% sensitivity, 63.6% specificity, and 71.4% accuracy. Metastatic and nonmetastatic gastric cancer patients were different in terms of PCPS (P=0.005. Conclusion. PCPS has been suggested for predicting metastasis in patients with gastric cancer. Future studies with larger sample size have been warranted.

  12. Prealbumin/CRP Based Prognostic Score, a New Tool for Predicting Metastasis in Patients with Inoperable Gastric Cancer.

    Science.gov (United States)

    Esfahani, Ali; Makhdami, Nima; Faramarzi, Elnaz; Asghari Jafarabadi, Mohammad; Ostadrahimi, Alireza; Ghayour Nahand, Mousa; Ghoreishi, Zohreh

    2016-01-01

    Background. There is a considerable dissimilarity in the survival duration of the patients with gastric cancer. We aimed to assess the systemic inflammatory response (SIR) and nutritional status of these patients before the commencement of chemotherapy to find the appropriate prognostic factors and define a new score for predicting metastasis. Methods. SIR was assessed using Glasgow Prognostic Score (GPS). Then a score was defined as prealbumin/CRP based prognostic score (PCPS) to be compared with GPS for predicting metastasis and nutritional status. Results. 71 patients with gastric cancer were recruited in the study. 87% of patients had malnutrition. There was a statistical difference between those with metastatic (n = 43) and those with nonmetastatic (n = 28) gastric cancer according to levels of prealbumin and CRP; however they were not different regarding patient generated subjective global assessment (PG-SGA) and GPS. The best cut-off value for prealbumin was determined at 0.20 mg/dL and PCPS could predict metastasis with 76.5% sensitivity, 63.6% specificity, and 71.4% accuracy. Metastatic and nonmetastatic gastric cancer patients were different in terms of PCPS (P = 0.005). Conclusion. PCPS has been suggested for predicting metastasis in patients with gastric cancer. Future studies with larger sample size have been warranted. PMID:26904109

  13. HER family kinase domain mutations promote tumor progression and can predict response to treatment in human breast cancer

    KAUST Repository

    Boulbes, Delphine R.

    2014-11-11

    Resistance to HER2-targeted therapies remains a major obstacle in the treatment of HER2-overexpressing breast cancer. Understanding the molecular pathways that contribute to the development of drug resistance is needed to improve the clinical utility of novel agents, and to predict the success of targeted personalized therapy based on tumor-specific mutations. Little is known about the clinical significance of HER family mutations in breast cancer. Because mutations within HER1/EGFR are predictive of response to tyrosine kinase inhibitors (TKI) in lung cancer, we investigated whether mutations in HER family kinase domains are predictive of response to targeted therapy in HER2-overexpressing breast cancer. We sequenced the HER family kinase domains from 76 HER2-overexpressing invasive carcinomas and identified 12 missense variants. Patients whose tumors carried any of these mutations did not respond to HER2 directed therapy in the metastatic setting. We developed mutant cell lines and used structural analyses to determine whether changes in protein conformation could explain the lack of response to therapy. We also functionally studied all HER2 mutants and showed that they conferred an aggressive phenotype and altered effects of the TKI lapatinib. Our data demonstrate that mutations in the finely tuned HER kinase domains play a critical function in breast cancer progression and may serve as prognostic and predictive markers.

  14. Curriculum design based on software development for prediction of coal gas omitting%瓦斯涌出量预测软件开发课程设计

    Institute of Scientific and Technical Information of China (English)

    崔炯屏; 唐亮; 王永友

    2014-01-01

    为培养学生独立认识和解决问题的能力,提高学生学习兴趣和团队合作精神,提出了一个基于瓦斯涌出量预测软件开发的课程设计。将瓦斯涌出量预测软件的开发过程进行分解,以使得实践过程可以分段进行,同时对软件开发难度进行适当调整,以保证课程实践的可行性。该课程设计可以充分锻炼学生学习新的知识和运用本专业知识的能力,对学生进一步学习和就业都具有积极的意义。%Aiming at enhancing the capability for recognizing and solving problems of students ,and improving their learning interest ,this paper puts forward a course exercise based on the software development for prediction of the gas omitting .The procedure is divided into smaller parts so that the process could run step by step .And the difficulty of the software development is reduced in order to guarantee the feasibility of this procedure .Students could enhance their ability to learn new knowledge and apply the professional knowledge . It shows some positive properties for the learning and job hunting for the students .

  15. Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer

    Directory of Open Access Journals (Sweden)

    La Macchia Mariangela

    2012-09-01

    Full Text Available Abstract Purpose To validate, in the context of adaptive radiotherapy, three commercial software solutions for atlas-based segmentation. Methods and materials Fifteen patients, five for each group, with cancer of the Head&Neck, pleura, and prostate were enrolled in the study. In addition to the treatment planning CT (pCT images, one replanning CT (rCT image set was acquired for each patient during the RT course. Three experienced physicians outlined on the pCT and rCT all the volumes of interest (VOIs. We used three software solutions (VelocityAI 2.6.2 (V, MIM 5.1.1 (M by MIMVista and ABAS 2.0 (A by CMS-Elekta to generate the automatic contouring on the repeated CT. All the VOIs obtained with automatic contouring (AC were successively corrected manually. We recorded the time needed for: 1 ex novo ROIs definition on rCT; 2 generation of AC by the three software solutions; 3 manual correction of AC. To compare the quality of the volumes obtained automatically by the software and manually corrected with those drawn from scratch on rCT, we used the following indexes: overlap coefficient (DICE, sensitivity, inclusiveness index, difference in volume, and displacement differences on three axes (x, y, z from the isocenter. Results The time saved by the three software solutions for all the sites, compared to the manual contouring from scratch, is statistically significant and similar for all the three software solutions. The time saved for each site are as follows: about an hour for Head&Neck, about 40 minutes for prostate, and about 20 minutes for mesothelioma. The best DICE similarity coefficient index was obtained with the manual correction for: A (contours for prostate, A and M (contours for H&N, and M (contours for mesothelioma. Conclusions From a clinical point of view, the automated contouring workflow was shown to be significantly shorter than the manual contouring process, even though manual correction of the VOIs is always needed.

  16. Early hematologic changes during prostate cancer radiotherapy predictive for late urinary and bowel toxicity

    Energy Technology Data Exchange (ETDEWEB)

    Pinkawa, Michael; Djukic, Victoria; Klotz, Jens; Holy, Richard; Eble, Michael J. [RWTH Aachen University, Department of Radiation Oncology, Aachen (Germany); Ribbing, Carolina [RWTH Aachen University, Department of Diagnostic and Interventional Radiology, Aachen (Germany)

    2015-10-15

    The primary objective of the study was to identify early hematologic changes predictive for radiotherapy (RT)-associated genitourinary and gastrointestinal toxicity. In a group of 91 prostate cancer patients presenting for primary (n = 51) or postoperative (n = 40) curative RT, blood samples (blood count, acute phase proteins, and cytokines) were analyzed before (T1), three times during (T2-T4), and 6-8 weeks after (T5) radiotherapy. Before RT (baseline), on the last day (acute toxicity), a median of 2 months and 16 months (late toxicity) after RT, patients responded to a validated questionnaire (Expanded Prostate Cancer Index Composite). Acute score changes > 20 points and late changes > 10 points were considered clinically relevant. Radiotherapy resulted in significant changes of hematologic parameters, with the largest effect on lymphocytes (mean decrease of 31-45 %) and significant dependence on target volume. C-reactive protein (CRP) elevation > 5 mg/l and hemoglobin level decrease ≥ 5 G/1 at T2 were found to be independently predictive for acute urinary toxicity (p < 0.01, respectively). CRP elevation was predominantly detected in primary prostate RT (p = 0.02). Early lymphocyte level elevation ≥ 0.3G/l at T2 was protective against late urinary and bowel toxicity (p = 0.02, respectively). Other significant predictive factors for late bowel toxicity were decreasing hemoglobin levels (cut-off ≥ 5 G/l) at T2 (p = 0.04); changes of TNF-α (tumor necrosis factor; p = 0.03) and ferritin levels (p = 0.02) at T5. All patients with late bowel toxicity had interleukin (IL)-6 levels < 1.5 ng/l at T2 (63 % without; p = 0.01). Early hematologic changes during prostate cancer radiotherapy are predictive for late urinary and bowel toxicity. (orig.) [German] Das primaere Ziel der Studie war die Identifikation von fruehen haematologischen Veraenderungen mit praediktiver Bedeutung fuer radiotherapieassoziierte genitourinale und gastrointestinale Toxizitaet. In einer

  17. Predictive Modelling of Toxicity Resulting from Radiotherapy Treatments of Head and Neck Cancer

    CERN Document Server

    Dean, Jamie A; Harrington, Kevin J; Nutting, Christopher M; Gulliford, Sarah L

    2014-01-01

    In radiotherapy for head and neck cancer, the radiation dose delivered to the pharyngeal mucosa (mucosal lining of the throat) is thought to be a major contributing factor to dysphagia (swallowing dysfunction), the most commonly reported severe toxicity. There is a variation in the severity of dysphagia experienced by patients. Understanding the role of the dose distribution in dysphagia would allow improvements in the radiotherapy technique to be explored. The 3D dose distributions delivered to the pharyngeal mucosa of 249 patients treated as part of clinical trials were reconstructed. Pydicom was used to extract DICOM (digital imaging and communications in medicine) data (the standard file formats for medical imaging and radiotherapy data). NumPy and SciPy were used to manipulate the data to generate 3D maps of the dose distribution delivered to the pharyngeal mucosa and calculate metrics describing the dose distribution. Multivariate predictive modelling of severe dysphagia, including descriptions of the d...

  18. The prediction and measurement of microdosimetric spectra relating to neutron cancer therapy

    CERN Document Server

    Taylor, G C

    2003-01-01

    The primary aim of this work has been to characterise the beam of the MRC's high energy neutron cancer therapy cyclotron at the Clatterbridge Hospital, Bebington, the Wirral, by measuring a series of microdosimetric spectra for a variety of irradiation conditions. In order to interpret the variation between these spectra, so that the underlying physics of the neutron beam could be determined, it was necessary to identify the most influential factors in the production of microdosimetric responses. Experimental procedures were tested in a series of measurements using 14 and 15 MeV monoenergetic neutrons from the Birmingham Dynamitron; these were instrumental in establishing the rigorous calibration regime necessary for the Clatterbridge measurement programme. The (analytical) predictive code NESLES was used to investigate the effect on microdosimetric spectra of having a low energy neutron component in the primary beam,, and also to highlight the shortcomings of the tissue-equivalent media used in microdosimetr...

  19. Rs488087 single nucleotide polymorphism as predictive risk factor for pancreatic cancers.

    Science.gov (United States)

    Martinez, Emmanuelle; Silvy, Françoise; Fina, Fréderic; Bartoli, Marc; Krahn, Martin; Barlesi, Fabrice; Figarella-Branger, Dominique; Iovanna, Juan; Laugier, René; Ouaissi, Mehdi; Lombardo, Dominique; Mas, Eric

    2015-11-24

    Pancreatic cancer (PC) is a devastating disease progressing asymptomatically until death within months after diagnosis. Defining at-risk populations should promote its earlier diagnosis and hence also avoid its development. Considering the known involvement in pancreatic disease of exon 11 of the bile salt-dependent lipase (BSDL) gene that encodes variable number of tandem repeat (VNTR) sequences, we hypothesized upon the existence of a genetic link between predisposition to PC and mutations in VNTR loci. To test this, BSDL VNTR were amplified by touchdown-PCR performed on genomic DNA extracted from cancer tissue or blood samples from a French patient cohort and amplicons were Sanger sequenced. A robust method using probes for droplet digital (dd)-PCR was designed to discriminate the C/C major from C/T or T/T minor genotypes. We report that the c.1719C > T transition (SNP rs488087) present in BSDL VNTR may be a useful marker for defining a population at risk of developing PC (occurrence: 63.90% in the PC versus 27.30% in the control group). The odds ratio of 4.7 for the T allele was larger than those already determined for other SNPs suspected to be predictive of PC. Further studies on tumor pancreatic tissue suggested that a germline T allele may favor Kras G12R/G12D somatic mutations which represent negative prognostic factors associated with reduced survival. We propose that the detection of the T allele in rs488087 SNP should lead to an in-depth follow-up of patients in whom an association with other potential risk factors of pancreatic cancer may be present.

  20. Positive margins prediction in breast cancer conservative surgery: Assessment of a preoperative web-based nomogram.

    Science.gov (United States)

    Alves-Ribeiro, Lídia; Osório, Fernando; Amendoeira, Isabel; Fougo, José Luís

    2016-08-01

    Margin status of the surgical specimen has been shown to be a prognostic and risk factor for local recurrence in breast cancer surgery. It has been studied as a topic of intervention to diminish reoperation rates and reduce the probability of local recurrence in breast conservative surgery (BCS). This study aims to validate the Dutch BreastConservation! nomogram, created by Pleijhus et al., which predicts preoperative probability of positive margins in BCS. Patients with diagnosis of breast cancer stages cT1-2, who underwent BCS at the Breast Center of São João University Hospital (BC-CHSJ) in 2013-2014, were included. Association and correlation were evaluated for clinical, radiological, pathological and surgical variables. Multivariable logistic regression and ROC curves were used to assess nomogram parameters and discrimination. In our series of 253 patients, no associations were found between margin status and other studied variables (such as age or family history of breast cancer), except for weight (p-value = 0.045) and volume (p-value = 0.012) of the surgical specimen. Regarding the nomogram, a statistically significant association was shown between cN1 status and positive margins (p-value = 0.014). No differences were registered between the scores of patients with positive versus negative margins. Discrimination analysis showed an AUC of 0.474 for the basic and 0.508 for the expanded models. We cannot assume its external validation or its applicability to our cohort. Further studies are needed to determine the validity of this nomogram and achieve a broader view of currently available tools. PMID:27326978

  1. DNA Repair Biomarkers Predict Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Alexander, Brian M., E-mail: bmalexander@lroc.harvard.edu [Department of Radiation Oncology, Dana-Farber/Brigham and Women' s Cancer Center, Boston, Massachusetts (United States); Wang Xiaozhe [On-Q-ity, Inc., Waltham, Massachusetts (United States); Niemierko, Andrzej [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts (United States); Weaver, David T. [On-Q-ity, Inc., Waltham, Massachusetts (United States); Mak, Raymond H. [Department of Radiation Oncology, Dana-Farber/Brigham and Women' s Cancer Center, Boston, Massachusetts (United States); Roof, Kevin S. [Southeast Radiation Oncology, Charlotte, North Carolina (United States); Fidias, Panagiotis [Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts (United States); Wain, John [Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts (United States); Choi, Noah C. [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts (United States)

    2012-05-01

    Purpose: The addition of neoadjuvant chemoradiotherapy prior to surgical resection for esophageal cancer has improved clinical outcomes in some trials. Pathologic complete response (pCR) following neoadjuvant therapy is associated with better clinical outcome in these patients, but only 22% to 40% of patients achieve pCR. Because both chemotherapy and radiotherapy act by inducing DNA damage, we analyzed proteins selected from multiple DNA repair pathways, using quantitative immunohistochemistry coupled with a digital pathology platform, as possible biomarkers of treatment response and clinical outcome. Methods and Materials: We identified 79 patients diagnosed with esophageal cancer between October 1994 and September 2002, with biopsy tissue available, who underwent neoadjuvant chemoradiotherapy prior to surgery at the Massachusetts General Hospital and used their archived, formalin-fixed, paraffin-embedded biopsy samples to create tissue microarrays (TMA). TMA sections were stained using antibodies against proteins in various DNA repair pathways including XPF, FANCD2, PAR, MLH1, PARP1, and phosphorylated MAPKAP kinase 2 (pMK2). Stained TMA slides were evaluated using machine-based image analysis, and scoring incorporated both the intensity and the quantity of positive tumor nuclei. Biomarker scores and clinical data were assessed for correlations with clinical outcome. Results: Higher scores for MLH1 (p = 0.018) and lower scores for FANCD2 (p = 0.037) were associated with pathologic response to neoadjuvant chemoradiation on multivariable analysis. Staining of MLH1, PARP1, XPF, and PAR was associated with recurrence-free survival, and staining of PARP1 and FANCD2 was associated with overall survival on multivariable analysis. Conclusions: DNA repair proteins analyzed by immunohistochemistry may be useful as predictive markers for response to neoadjuvant chemoradiotherapy in patients with esophageal cancer. These results are hypothesis generating and need

  2. Are KRAS/BRAF mutations potent prognostic and/or predictive biomarkers in colorectal cancers?

    Science.gov (United States)

    Yokota, Tomoya

    2012-02-01

    KRAS and BRAF mutations lead to the constitutive activation of EGFR signaling through the oncogenic Ras/Raf/Mek/Erk pathway. Currently, KRAS is the only potential biomarker for predicting the efficacy of anti-EGFR monoclonal antibodies (mAb) in colorectal cancer (CRC). However, a recent report suggested that the use of cetuximab was associated with survival benefit among patients with p.G13D-mutated tumors. Furthermore, although the presence of mutated BRAF is one of the most powerful prognostic factors for advanced and recurrent CRC, it remains unknown whether patients with BRAF-mutated tumors experience a survival benefit from treatment with anti-EGFR mAb. Thus, the prognostic or predictive relevance of the KRAS and BRAF genotype in CRC remains controversial despite several investigations. Routine KRAS/BRAF screening of pathological specimens is required to promote the appropriate clinical use of anti-EGFR mAb and to determine malignant phenotypes in CRC. The significance of KRAS/BRAF mutations as predictive or prognostic biomarkers should be taken into consideration when selecting a KRAS/BRAF screening assay. This article will review the spectrum of KRAS/BRAF genotype and the impact of KRAS/BRAF mutations on the clinicopathological features and prognosis of patients with CRC, particularly when differentiating between the mutations at KRAS codons 12 and 13. Furthermore, the predictive role of KRAS/BRAF mutations in treatments with anti-EGFR mAb will be verified, focusing on KRAS p.G13D and BRAF mutations.

  3. Xenograft assessment of predictive biomarkers for standard head and neck cancer therapies.

    Science.gov (United States)

    Stein, Andrew P; Swick, Adam D; Smith, Molly A; Blitzer, Grace C; Yang, Robert Z; Saha, Sandeep; Harari, Paul M; Lambert, Paul F; Liu, Cheng Z; Kimple, Randall J

    2015-05-01

    Head and neck squamous cell carcinoma (HNSCC) remains a challenging cancer to treat with overall 5-year survival on the order of 50-60%. Therefore, predictive biomarkers for this disease would be valuable to provide more effective and individualized therapeutic approaches for these patients. While prognostic biomarkers such as p16 expression correlate with outcome; to date, no predictive biomarkers have been clinically validated for HNSCC. We generated xenografts in immunocompromised mice from six established HNSCC cell lines and evaluated response to cisplatin, cetuximab, and radiation. Tissue microarrays were constructed from pre- and posttreatment tumor samples derived from each xenograft experiment. Quantitative immunohistochemistry was performed using a semiautomated imaging and analysis platform to determine the relative expression of five potential predictive biomarkers: epidermal growth factor receptor (EGFR), phospho-EGFR, phospho-Akt, phospho-ERK, and excision repair cross-complementation group 1 (ERCC1). Biomarker levels were compared between xenografts that were sensitive versus resistant to a specific therapy utilizing a two-sample t-test with equal standard deviations. Indeed the xenografts displayed heterogeneous responses to each treatment, and we linked a number of baseline biomarker levels to response. This included low ERCC1 being associated with cisplatin sensitivity, low phospho-Akt correlated with cetuximab sensitivity, and high total EGFR was related to radiation resistance. Overall, we developed a systematic approach to identifying predictive biomarkers and demonstrated several connections between biomarker levels and treatment response. Despite these promising initial results, this work requires additional preclinical validation, likely involving the use of patient-derived xenografts, prior to moving into the clinical realm for confirmation among patients with HNSCC.

  4. Predicting infectious complications in neutropenic children and young people with cancer (IPD protocol

    Directory of Open Access Journals (Sweden)

    Phillips Robert S

    2012-02-01

    Full Text Available Abstract Background A common and potentially life-threatening complication of the treatment of childhood cancer is infection, which frequently presents as fever with neutropenia. The standard management of such episodes is the extensive use of intravenous antibiotics, and though it produces excellent survival rates of over 95%, it greatly inconveniences the three-fourths of patients who do not require such aggressive treatment. There have been a number of studies which have aimed to develop risk prediction models to stratify treatment. Individual participant data (IPD meta-analysis in therapeutic studies has been developed to improve the precision and reliability of answers to questions of treatment effect and recently have been suggested to be used to answer questions regarding prognosis and diagnosis to gain greater power from the frequently small individual studies. Design In the IPD protocol, we will collect and synthesise IPD from multiple studies and examine the outcomes of episodes of febrile neutropenia as a consequence of their treatment for malignant disease. We will develop and evaluate a risk stratification model using hierarchical regression models to stratify patients by their risk of experiencing adverse outcomes during an episode. We will also explore specific practical and methodological issues regarding adaptation of established techniques of IPD meta-analysis of interventions for use in synthesising evidence derived from IPD from multiple studies for use in predictive modelling contexts. Discussion Our aim in using this model is to define a group of individuals at low risk for febrile neutropenia who might be treated with reduced intensity or duration of antibiotic therapy and so reduce the inconvenience and cost of these episodes, as well as to define a group of patients at very high risk of complications who could be subject to more intensive therapies. The project will also help develop methods of IPD predictive modelling

  5. A Software Reliability Estimation Method to Nuclear Safety Software

    Energy Technology Data Exchange (ETDEWEB)

    Park, Geeyong; Jang, Seung Cheol [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2014-02-15

    A method for estimating software reliability for nuclear safety software is proposed in this paper. This method is based on the software reliability growth model (SRGM), where the behavior of software failure is assumed to follow a nonhomogeneous Poisson process. Two types of modeling schemes based on a particular underlying method are proposed in order to more precisely estimate and predict the number of software defects based on very rare software failure data. The Bayesian statistical inference is employed to estimate the model parameters by incorporating software test cases as a covariate into the model. It was identified that these models are capable of reasonably estimating the remaining number of software defects which directly affects the reactor trip functions. The software reliability might be estimated from these modeling equations, and one approach of obtaining software reliability value is proposed in this paper.

  6. XPG rs2296147 T>C polymorphism predicted clinical outcome in colorectal cancer.

    Science.gov (United States)

    Wang, Fang; Zhang, Shao-Dan; Xu, Hong-Mei; Zhu, Jin-Hong; Hua, Rui-Xi; Xue, Wen-Qiong; Li, Xi-Zhao; Wang, Tong-Min; He, Jing; Jia, Wei-Hua

    2016-03-01

    Xeroderma pigmentosum group G (XPG), one of key components of nucleotide excision repair pathway (NER), is involved in excision repair of UV-induced DNA damage. Single nucleotide polymorphisms (SNPs) in the XPG gene have been reported to associate with the clinical outcome of various cancer patients. We aimed to assess the impact of four potentially functional SNPs (rs2094258 C>T, rs2296147 T>C, rs751402 G>A, and rs873601 G>A) in the XPG gene on prognosis in colorectal cancer (CRC) patients. A total of 1901 patients diagnosed with pathologically confirmed CRC were genotyped for four XPG polymorphisms. Cox proportional hazards model analysis controlled for several confounding factors was conducted to compute hazard ratios (HRs) and 95% confidence intervals (CIs). Of the four included SNPs, only rs2296147 was shown to significantly affect progression-free survival (PFS) in CRC. Patients carrying rs2296147 CT/TT genotype had a significantly shorter median 10 years PFS than those carrying CC genotype (88.5 months vs. 118.1 months), and an increased progression risk were observed with rs2296147 (HR = 1.324, 95% CI = 1.046-1.667). Moreover, none of the four SNPs were associated with overall survival. In conclusion, our study showed that XPG rs2296147 CT/TT variants conferred significant survival disadvantage in CRC patients in term of PFS. XPG rs2296147 polymorphism could be predictive of unfavorable prognosis of CRC patients. PMID:26887052

  7. ZEB1 Expression in Endometrial Biopsy Predicts Lymph Node Metastases in Patient with Endometrial Cancer

    Directory of Open Access Journals (Sweden)

    Gang Feng

    2014-01-01

    Full Text Available Purpose. The purpose of this study was to analyze the expression of zinc-finger E-box-binding homeobox 1 (ZEB1 in endometrial biopsy and its correlation with preoperative characteristics, including lymph node metastases in patient with endometrial cancer. Methods. Using quantitative RT-PCR, ZEB1 expressions in endometrial biopsy from 452 patients were measured. The relationship between ZEB1 expression and preoperative characteristics was analyzed. Results. ZEB1 expressions were significantly associated with subtype, grade, myometrial invasion, and lymph node metastases. Lymph node metastases could be identified with a sensitivity of 57.8% at specificity of 74.1% by ZEB1 expression in endometrial biopsy. Based on combination of preoperative characteristics and ZEB1 expression, lymph node metastases could be identified with a sensitivity of 62.1% at specificity of 96.2% prior to hysterectomy. Conclusion. ZEB1 expression in endometrial biopsy could help physicians to better predict the lymph node metastasis in patients with endometrial cancer prior to hysterectomy.

  8. ZEB1 Expression in Endometrial Biopsy Predicts Lymph Node Metastases in Patient with Endometrial Cancer

    Science.gov (United States)

    Feng, Gang; Wang, Xiangming; Cao, Xiaozhi; Shen, Lijuan; Zhu, Jiansheng

    2014-01-01

    Purpose. The purpose of this study was to analyze the expression of zinc-finger E-box-binding homeobox 1 (ZEB1) in endometrial biopsy and its correlation with preoperative characteristics, including lymph node metastases in patient with endometrial cancer. Methods. Using quantitative RT-PCR, ZEB1 expressions in endometrial biopsy from 452 patients were measured. The relationship between ZEB1 expression and preoperative characteristics was analyzed. Results. ZEB1 expressions were significantly associated with subtype, grade, myometrial invasion, and lymph node metastases. Lymph node metastases could be identified with a sensitivity of 57.8% at specificity of 74.1% by ZEB1 expression in endometrial biopsy. Based on combination of preoperative characteristics and ZEB1 expression, lymph node metastases could be identified with a sensitivity of 62.1% at specificity of 96.2% prior to hysterectomy. Conclusion. ZEB1 expression in endometrial biopsy could help physicians to better predict the lymph node metastasis in patients with endometrial cancer prior to hysterectomy. PMID:25544793

  9. Meta-Prediction of MTHFR Gene Polymorphism Mutations and Associated Risk for Colorectal Cancer.

    Science.gov (United States)

    Shiao, S P K; Yu, C H

    2016-07-01

    The methylenetetrahydrofolate reductase (MTHFR) gene is one of the most investigated of the genes associated with chronic human diseases because of its associations with hyperhomocysteinemia and toxicity. It has been proposed as a prototype gene for the prevention of colorectal cancer (CRC). The major objectives of this meta-analysis were to examine the polymorphism-mutation patterns of MTHFR and their associations with risk for CRC as well as potential contributing factors for mutations and disease risks. This analysis included 33,626 CRC cases and 48,688 controls across 92 studies for MTHFR 677 and 16,367 cases and 24,874 controls across 54 studies for MTHFR 1298, comprising data for various racial and ethnic groups, both genders, and multiple cancer sites. MTHFR 677 homozygous TT genotype was protective (p Meta-predictive analyses revealed that air pollution levels were associated with gene polymorphisms for both genotypes. Future nursing research should be conducted to develop proactive measures to protect populations in cities where air pollution causes more deaths. PMID:26858257

  10. Outcome and Predictive Factors in Uterine Carcinosarcoma Using Postoperative Radiotherapy: A Rare Cancer Network Study.

    Science.gov (United States)

    Zwahlen, Daniel R; Schick, Ulrike; Bolukbasi, Yasemin; Thariat, Juliette; Abdah-Bortnyak, Roxolyana; Kuten, Abraham; Igdem, Sefik; Caglar, Hale; Ozsaran, Zeynep; Loessl, Kristina; Belkaaloul, Kaouthar Khanfir; Villette, Sylviane; Vees, Hansjörg

    2016-06-28

    Uterine carcinosarcomas (UCS) are rare tumors. Consensus regarding therapeutic management in non-metastatic disease is lacking. This study reports on outcome and predictive factors when using postoperative radiotherapy. We analyzed a retrospective analysis in 124 women treated between 1987-2007 in the framework of the Rare-Cancer-Network. Median follow-up was 27 months. Postoperative pelvic EBRT was administered in 105 women (85%) and 92 patients (74%) received exclusive or additional vaginal brachytherapy. Five-year overall survival (OS), disease-free survival (DFS), cancer specific survival (CSS) and locoregional control (LRC) were 51.6% (95% CI 35-73%), 53.7% (39-71%), 58.6% (38-74%) and 48% (38-67%). Multivariate analysis showed that external beam radiation therapy (EBRT) >50Gy was an independent prognostic factor for better OS (P=0.03), CSS (P=0.02) and LRC (P=0.01). Relative risks (RR) for better OS (P=0.02), DFS (P=0.04) and LRC (P=0.01) were significantly associated with younger age (≤60 years). Higher brachytherapy (BT)-dose (>9Gy) improved DFS (P=0.04) and LRC (P=0.008). We concluded that UCS has high systemic failure rate. Local relapse was reduced by a relative risk factor of over three in all stages of diseases when using higher doses for EBRT and brachytherapy. Postoperative RT was most effective in UCS stage I/II-diseases. PMID:27441069

  11. Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome

    Directory of Open Access Journals (Sweden)

    Fenstermacher David A

    2007-09-01

    Full Text Available Abstract Background Gene expression profiles based on microarray data have been suggested by many studies as potential molecular prognostic indexes of breast cancer. However, due to the confounding effect of clinical background, independent studies often obtained inconsistent results. The current study investigated the possibility to improve the quality and generality of expression profiles by integrated analysis of multiple datasets. Profiles of recurrence outcome were derived from two independent datasets and validated by a third dataset. Results The clinical background of patients significantly influenced the content and performance of expression profiles when the training samples were unbalanced. The integrated profiling of two independent datasets lead to higher classification accuracy (71.11% vs. 70.59% and larger ROC curve area (0.789 vs. 0.767 of the testing samples. Cell cycle, especially M phase mitosis, was significantly overrepresented by the 60-gene profile obtained from integrated analysis (p Conclusion The current study confirmed that the gene expression profile generated by integrated analysis of multiple datasets achieved better prediction of breast cancer recurrence. However, the content and performance of profiles was confounded by clinical background of training patients. In future studies, prognostic profile applicable to the general population should be derived from more diversified and balanced patient cohorts in larger scale.

  12. Predictive value of serum medroxyprogesterone acetate concentration for response in advanced or recurrent breast cancer.

    Science.gov (United States)

    Nishimura, R; Nagao, K; Matsuda, M; Baba, K; Matsuoka, Y; Yamashita, H; Fukuda, M; Higuchi, A; Ikeda, K

    1997-08-01

    Medroxyprogesterone acetate (MPA) is one of the most commonly prescribed drugs for endocrine therapy of metastatic breast cancer. In this study, the serum MPA concentration was measured by high-performance liquid chromatography (HPLC) and evaluated for its usefulness in predicting the response in 79 cases of advanced or recurrent breast cancers. Overall, 29 patients (37%) achieved an objective response. The response rate correlated significantly with the oestrogen receptor (ER) status (P = 0.03), proliferative activity determined by DNA polymerase alpha (P = 0.04), the disease-free interval (DFI) (P = 0.05) and the serum MPA concentration (P < 0.001). Patients with ER-positive tumours, lower proliferative activity, a longer (DFI) or a higher serum MPA concentration responded more frequently. The mean serum MPA concentration in the responders with ER-positive tumours (P = 0.01) or tumours with a lower proliferative activity (P = 0.008) were significantly lower than in cases with ER-negative tumours or tumours with a higher proliferative activity, respectively. Cases with soft tissue metastases showed responses at significantly lower MPA concentrations (P = 0.003) than those with bone or visceral metastases. Furthermore, there was a dramatic decrease in the MPA concentration when a responder with a high concentration became unresponsive to the therapy. Thus, the serum MPA concentration is a determining factor for the response to treatment. PMID:9337682

  13. DNA Methylation-Guided Prediction of Clinical Failure in High-Risk Prostate Cancer.

    Directory of Open Access Journals (Sweden)

    Kirill Litovkin

    Full Text Available Prostate cancer (PCa is a very heterogeneous disease with respect to clinical outcome. This study explored differential DNA methylation in a priori selected genes to diagnose PCa and predict clinical failure (CF in high-risk patients.A quantitative multiplex, methylation-specific PCR assay was developed to assess promoter methylation of the APC, CCND2, GSTP1, PTGS2 and RARB genes in formalin-fixed, paraffin-embedded tissue samples from 42 patients with benign prostatic hyperplasia and radical prostatectomy specimens of patients with high-risk PCa, encompassing training and validation cohorts of 147 and 71 patients, respectively. Log-rank tests, univariate and multivariate Cox models were used to investigate the prognostic value of the DNA methylation.Hypermethylation of APC, CCND2, GSTP1, PTGS2 and RARB was highly cancer-specific. However, only GSTP1 methylation was significantly associated with CF in both independent high-risk PCa cohorts. Importantly, trichotomization into low, moderate and high GSTP1 methylation level subgroups was highly predictive for CF. Patients with either a low or high GSTP1 methylation level, as compared to the moderate methylation groups, were at a higher risk for CF in both the training (Hazard ratio [HR], 3.65; 95% CI, 1.65 to 8.07 and validation sets (HR, 4.27; 95% CI, 1.03 to 17.72 as well as in the combined cohort (HR, 2.74; 95% CI, 1.42 to 5.27 in multivariate analysis.Classification of primary high-risk tumors into three subtypes based on DNA methylation can be combined with clinico-pathological parameters for a more informative risk-stratification of these PCa patients.

  14. Predictive Factors of Radiation-Induced Lung Toxicity in Lung Cancer Patients: A Retrospective Study

    Directory of Open Access Journals (Sweden)

    Maher Soliman

    2016-07-01

    Full Text Available Background: Radiation-induced lung toxicity is an important dose-limiting toxicity in lung cancer radiotherapy, for which there are no generally accepted predictive factors. This study seeks to identify risk factors associated with the development of severe radiation-induced lung toxicity using clinical and dosimetric parameters. Methods: We reviewed the medical records of 54 patients with histologically proven stage III non-small cell lung cancer treated with three dimensional-conformal radiotherapy at Alexandria Main University Hospital between January 2008 and December 2011. The original treatment plans for those patients were restored and imported to a treatment planning system. Lung dose–volume histograms and various dosimetric parameters were calculated. Univariate and multivariate logistic regression analyses were performed. Results: The following grades of radiation-induced lung toxicity were observed in patients - grade 0: 17 (31.5%, grade 1: 5 (9.3%, grade 2: 13 (24.1%, grade 3: 15 (27.8%, and grade 5: 4 (7.4%. A total of 19 (35.2% patients developed grade ≥3 and were considered to have an event. Univariate analysis showed that age, presence of chronic obstructive pulmonary disease and location of the primary tumor had significant associations with severe radiation-induced lung toxicity. Other dosimetric variables such as tumor side, histology, forced expiratory volume in 1 s, smoking, and gender showed no significant correlations with severe radiation-induced lung toxicity. Multivariate analysis showed that the presence of chronic obstructive pulmonary disease (P=0.001 and location of the primary tumor (P=0.010 were the only predictive factors for severe radiation-induced lung toxicity. Conclusion: This study demonstrates that patients with chronic obstructive pulmonary disease and lower lung lobe tumors have a high risk of severe radiationinduced lung toxicity when treated with combined chemoradiotherapy. These easily obtained

  15. Nomograms for predicting prognostic value of inflammatory biomarkers in colorectal cancer patients after radical resection.

    Science.gov (United States)

    Li, Yaqi; Jia, Huixun; Yu, Wencheng; Xu, Ye; Li, Xinxiang; Li, Qingguo; Cai, Sanjun

    2016-07-01

    Increasing evidence indicates that inflammation plays a vital role in tumorigenesis and progression. However, the prognostic value of inflammatory biomarkers in colorectal cancer (CRC) has not been established. In this study, a retrospective analysis was conducted in patients with CRC in Fudan University Shanghai Cancer Center (FUSCC) between April 1, 2007 and April 30, 2014, and 5,336 patients were identified eligible. Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and albumin/globulin ratio (AGR) were analyzed. Kaplan-Meier analysis was used to calculate the 5-year overall survival (OS) and disease-free survival (DFS). Cox regression analysis was performed to assess the prognostic factors. Nomograms were established to predict OS and DFS, and Harrell's concordance index (c-index) was adopted to evaluate prediction accuracy. As results, the 5-year OS was 79.2% and the 5-year DFS was 56.0% in the cohort. Patients were stratified into 2 groups by NLR (≤2.72 and >2.72), PLR (≤219.00 and >219.00), LMR (≤2.83 and >2.83) and AGR ( 2.72, PLR > 219.00, LMR ≤ 2.83 and AGR nomograms on OS and DFS were established according to all significant factors, and c-indexes were 0.765 (95% CI: 0.744-0.785) and 0.735 (95% CI: 0.721-0.749), respectively. Nomograms based on OS and DFS can be recommended as practical models to evaluate prognosis for CRC patients. PMID:26933932

  16. Pre-radiotherapy FDG PET predicts radiation pneumonitis in lung cancer

    International Nuclear Information System (INIS)

    A retrospective analysis is performed to determine if pre-treatment [18 F]-2-fluoro-2-deoxyglucose positron emission tomography/computed tomography (FDG PET/CT) image derived parameters can predict radiation pneumonitis (RP) clinical symptoms in lung cancer patients. We retrospectively studied 100 non-small cell lung cancer (NSCLC) patients who underwent FDG PET/CT imaging before initiation of radiotherapy (RT). Pneumonitis symptoms were evaluated using the Common Terminology Criteria for Adverse Events version 4.0 (CTCAEv4) from the consensus of 5 clinicians. Using the cumulative distribution of pre-treatment standard uptake values (SUV) within the lungs, the 80th to 95th percentile SUV values (SUV80 to SUV95) were determined. The effect of pre-RT FDG uptake, dose, patient and treatment characteristics on pulmonary toxicity was studied using multiple logistic regression. The study subjects were treated with 3D conformal RT (n = 23), intensity modulated RT (n = 64), and proton therapy (n = 13). Multiple logistic regression analysis demonstrated that elevated pre-RT lung FDG uptake on staging FDG PET was related to development of RP symptoms after RT. A patient of average age and V30 with SUV95 = 1.5 was an estimated 6.9 times more likely to develop grade ≥ 2 radiation pneumonitis when compared to a patient with SUV95 = 0.5 of the same age and identical V30. Receiver operating characteristic curve analysis showed the area under the curve was 0.78 (95% CI = 0.69 – 0.87). The CT imaging and dosimetry parameters were found to be poor predictors of RP symptoms. The pretreatment pulmonary FDG uptake, as quantified by the SUV95, predicted symptoms of RP in this study. Elevation in this pre-treatment biomarker identifies a patient group at high risk for post-treatment symptomatic RP

  17. Overall expression of beta-catenin outperforms its nuclear accumulation in predicting outcomes of colorectal cancers

    Institute of Scientific and Technical Information of China (English)

    Worrawit Wanitsuwan; Samommas Kanngum; Teeranut Boonpipattanapong; Rassamee Sangthong; Surasak Sangkhathat

    2008-01-01

    AIM: To examine the expression of beta-catenin in colorectal cancer and look for association with other clinico-pathological parameters.METHODS: Tumor samples from 163 cases of colorectal cancer (CRC) who had undergone primary colectomy between May, 1998 and November, 2002 with complete follow-up data for either 5 years or until death were recruited for a beta-catenin immunohistochemical study. The percentage of immunoreacted tumor cells was defined as overall staining density (OSD) and percentage of cells having nuclear localization was counted as nuclear staining density (NSD). Univariate exploration used log-rank test and multivariate survival analysis used Cox's hazard regression model.RESULTS: Beta-catenin immunoreactivity was detected in 161 samples (98.8%), of which 131 cases had nuclear staining. High OSD (≥t 75%), detected in 123 cases (75.5%), was significantly associated with earlier clinical staging (P<0.01), lower nodal status (P=0.02), non-metastatic status (P < 0.01) and better differentiation (P = 0.02). Multivariate analysis found that high OSD was independently associated with better survival [Cox's hazard ratio 0.51, 95% confidence interval (CI) 0.31-0.83]. Although high NSD (≥ 75%) was correlated with high pre-operative serum CEA (P = 0.03), well differentiation (P < 0.01), and increased staining intensity (P < 0.01), the parameter was not significantly associated with survival.CONCLUSION: Unlike previous reports, the study did not find a predictive value of nuclear beta-catenin in CRC. Instead, the overall expression of beta-catenin in CRC showed an association with better differentiation and earlier staging. Moreover, the parameter also independently predicted superior survival.

  18. Evaluating predictive pharmacogenetic signatures of adverse events in colorectal cancer patients treated with fluoropyrimidines.

    Directory of Open Access Journals (Sweden)

    Barbara A Jennings

    Full Text Available The potential clinical utility of genetic markers associated with response to fluoropyrimidine treatment in colorectal cancer patients remains controversial despite extensive study. Our aim was to test the clinical validity of both novel and previously identified markers of adverse events in a broad clinical setting. We have conducted an observational pharmacogenetic study of early adverse events in a cohort study of 254 colorectal cancer patients treated with 5-fluorouracil or capecitabine. Sixteen variants of nine key folate (pharmacodynamic and drug metabolising (pharmacokinetic enzymes have been analysed as individual markers and/or signatures of markers. We found a significant association between TYMP S471L (rs11479 and early dose modifications and/or severe adverse events (adjusted OR = 2.02 [1.03; 4.00], p = 0.042, adjusted OR = 2.70 [1.23; 5.92], p = 0.01 respectively. There was also a significant association between these phenotypes and a signature of DPYD mutations (Adjusted OR = 3.96 [1.17; 13.33], p = 0.03, adjusted OR = 6.76 [1.99; 22.96], p = 0.002 respectively. We did not identify any significant associations between the individual candidate pharmacodynamic markers and toxicity. If a predictive test for early adverse events analysed the TYMP and DPYD variants as a signature, the sensitivity would be 45.5 %, with a positive predictive value of just 33.9 % and thus poor clinical validity. Most studies to date have been under-powered to consider multiple pharmacokinetic and pharmacodynamic variants simultaneously but this and similar individualised data sets could be pooled in meta-analyses to resolve uncertainties about the potential clinical utility of these markers.

  19. Serum Amyloid A as a Predictive Marker for Radiation Pneumonitis in Lung Cancer Patients

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yu-Shan [Department of Radiation Therapy and Oncology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan (China); Department of Animal Science, National Ilan University, Ilan, Taiwan (China); Chang, Heng-Jui [Department of Radiation Therapy and Oncology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan (China); Chang, Yue-Cune [Department of Mathematics, Tamkang University, Taipei, Taiwan (China); Huang, Su-Chen; Ko, Hui-Ling; Chang, Chih-Chia [Department of Radiation Therapy and Oncology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan (China); Yeh, Yu-Wung; Jiang, Jiunn-Song [Department of Chest Medicine, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan (China); Lee, Cheng-Yen; Chi, Mau-Shin [Department of Radiation Therapy and Oncology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan (China); Chi, Kwan-Hwa, E-mail: M006565@ms.skh.org.tw [Department of Radiation Therapy and Oncology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan (China); Institute of Radiation Science and School of Medicine, National Yang-Ming University, Taipei, Taiwan (China)

    2013-03-01

    Purpose: To investigate serum markers associated with radiation pneumonitis (RP) grade ≥3 in patients with lung cancer who were treated with radiation therapy. Methods and Materials: Pretreatment serum samples from patients with stage Ib-IV lung cancer who developed RP within 1 year after radiation therapy were analyzed to identify a proteome marker able to stratify patients prone to develop severe RP by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Dosimetric parameters and 3 biological factors were compared. Results: Serum samples from 16 patients (28%) with severe RP (grade 3-4) and 42 patients (72%) with no or mild RP (grade 0-2) were collected for analysis. All patients received a median of 54 Gy (range, 42-70 Gy) of three-dimensional conformal radiation therapy with a mean lung dose (MLD) of 1502 cGy (range, 700-2794 cGy). An m/z peak of 11,480 Da was identified by SELDI-TOF-MS, and serum amyloid A (SAA) was the primary splitter serum marker. The receiver operating characteristic area under the curve of SAA (0.94; 95% confidence interval [CI], 0.87-1.00) was higher than those of C-reactive protein (0.83; 95% CI, 0.72-0.94), interleukin-6 (0.79; 95% CI, 0.65-0.94), and MLD (0.57; 95% CI, 0.37-0.77). The best sensitivity and specificity of combined SAA and MLD for predicting RP were 88.9% and 96.0%, respectively. Conclusions: Baseline SAA could be used as an auxiliary marker for predicting severe RP. Extreme care should be taken to limit the lung irradiation dose in patients with high SAA.

  20. Prediction of the 10-year probability of gastric cancer occurrence in the Japanese population: the JPHC study cohort II.

    Science.gov (United States)

    Charvat, Hadrien; Sasazuki, Shizuka; Inoue, Manami; Iwasaki, Motoki; Sawada, Norie; Shimazu, Taichi; Yamaji, Taiki; Tsugane, Shoichiro

    2016-01-15

    Gastric cancer is a particularly important issue in Japan, where incidence rates are among the highest observed. In this work, we provide a risk prediction model allowing the estimation of the 10-year cumulative probability of gastric cancer occurrence. The study population consisted of 19,028 individuals from the Japanese Public Health Center cohort II who were followed-up from 1993 to 2009. A parametric survival model was used to assess the impact on the probability of gastric cancer of clinical and lifestyle-related risk factors in combination with serum anti-Helicobacter pylori antibody titres and pepsinogen I and pepsinogen II levels. Based on the resulting model, cumulative probability estimates were calculated and a simple risk scoring system was developed. A total of 412 cases of gastric cancer occurred during 270,854 person-years of follow-up. The final model included (besides the biological markers) age, gender, smoking status, family history of gastric cancer and consumption of highly salted food. The developed prediction model showed good predictive performance in terms of discrimination (optimism-corrected c-index: 0.768) and calibration (Nam and d'Agostino's χ(2) test: 14.78; p values = 0.06). Estimates of the 10-year probability of gastric cancer occurrence ranged from 0.04% (0.02, 0.1) to 14.87% (8.96, 24.14) for men and from 0.03% (0.02, 0.07) to 4.91% (2.71, 8.81) for women. In conclusion, we developed a risk prediction model for gastric cancer that combines clinical and biological markers. It might prompt individuals to modify their lifestyle habits, attend regular check-up visits or participate in screening programmes.

  1. Generation of a predictive melphalan resistance index by drug screen of B-cell cancer cell lines.

    Directory of Open Access Journals (Sweden)

    Martin Boegsted

    Full Text Available BACKGROUND: Recent reports indicate that in vitro drug screens combined with gene expression profiles (GEP of cancer cell lines may generate informative signatures predicting the clinical outcome of chemotherapy. In multiple myeloma (MM a range of new drugs have been introduced and now challenge conventional therapy including high dose melphalan. Consequently, the generation of predictive signatures for response to melphalan may have a clinical impact. The hypothesis is that melphalan screens and GEPs of B-cell cancer cell lines combined with multivariate statistics may provide predictive clinical information. MATERIALS AND METHODS: Microarray based GEPs and a melphalan growth inhibition screen of 59 cancer cell lines were downloaded from the National Cancer Institute database. Equivalent data were generated for 18 B-cell cancer cell lines. Linear discriminant analyses (LDA, sparse partial least squares (SPLS and pairwise comparisons of cell line data were used to build resistance signatures from both cell line panels. A melphalan resistance index was defined and estimated for each MM patient in a publicly available clinical data set and evaluated retrospectively by Cox proportional hazards and Kaplan-Meier survival analysis. PRINCIPAL FINDINGS: Both cell line panels performed well with respect to internal validation of the SPLS approach but only the B-cell panel was able to predict a significantly higher risk of relapse and death with increasing resistance index in the clinical data sets. The most sensitive and resistant cell lines, MOLP-2 and RPMI-8226 LR5, respectively, had high leverage, which suggests their differentially expressed genes to possess important predictive value. CONCLUSION: The present study presents a melphalan resistance index generated by analysis of a B-cell panel of cancer cell lines. However, the resistance index needs to be functionally validated and correlated to known MM biomarkers in independent data sets in order to

  2. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

    Energy Technology Data Exchange (ETDEWEB)

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic

  3. Role of p-glycoprotein expression in predicting response to neoadjuvant chemotherapy in breast cancer-a prospective clinical study

    OpenAIRE

    Bhatia Ashima; Bansal Anju; Saxena Sunita; Mittal Mahesh K; Singh Jai; Chintamani,; Kulshreshtha Pranjal

    2005-01-01

    Abstract Background Neoadjuvant chemotherapy (NACT) is an integral part of multi-modality approach in the management of locally advanced breast cancer. It is vital to predict response to chemotherapy in order to tailor the regime for a particular patient. The prediction would help in avoiding the toxicity induced by an ineffective chemotherapeutic regime in a non-responder and would also help in the planning of an alternate regime. Development of resistance to chemotherapeutic agents is a maj...

  4. Sarcopenia and body mass index predict sunitinib-induced early dose-limiting toxicities in renal cancer patients

    OpenAIRE

    Huillard, O; Mir, O.; Peyromaure, M; Tlemsani, C; Giroux, J.; Boudou-Rouquette, P; Ropert, S; Delongchamps, N Barry; Zerbib, M; Goldwasser, F

    2013-01-01

    Background: Little is known on factors predicting sunitinib toxicity. Recently, the condition of low muscle mass, named sarcopenia, was identified as a significant predictor of toxicity in metastatic renal cell cancer (mRCC) patients treated with sorafenib. We investigated whether sarcopenia could predict early dose-limiting toxicities (DLTs) occurrence in mRCC patients treated with sunitinib. Methods: Consecutive mRCC patients treated with sunitinib were retrospectively reviewed. A DLT was d...

  5. Prediction of additional lymph node involvement in breast cancer patients with positive sentinel lymph nodes.

    Science.gov (United States)

    Pohlodek, K; Bozikova, S; Meciarova, I; Mucha, V; Bartova, M; Ondrias, F

    2016-01-01

    Axillary lymph node dissection (ALND) has traditionally been the principal method for evaluating axillary lymph node status in breast cancer patients. In the past decades sentinel lymph nodes biopsy after lymphatic mapping has been used to stage the disease. The majority of sentinel lymph nodes (SLN) positive patients do not have additional metastases in non-sentinel nodes (non-SLN) after additional ALND. These patients are exposed to the morbidity of ALND without any benefit from additional axillary clearence. In the present study we would like to asses the criteria for selecting those patients, who have high risk for non-SLN metastases in the axilla in cases of positive SLN. In this retrospective analysis, clinical and pathologic data from 163 patients who underwent SLN biopsy followed by ALND were collected. Following clinical and pathological characteristics were analyzed to predict the likehood of non-SLN metastases: age, staging, histologic type and grading of the tumors, hormonal receptor status, HER-2 receptor status and Ki-67 protein, angioinvasion, metastases in SLN and non-SLN. Relative frequencies of individual characteristics between sample groups were statistically tested by Chi-square test at significance level p=0.5, when sample sizes in groups were small (≤5) by Fisher´s exact test. Metastasis in SLN were present in 67 (41%) of patients, 48 patients (29,4%) had metastasis also in non-SLN. The ratio between non-SLN positive / non-SLN negative lymph nodes in patients with positive SLN increases with the stage of the disease, the difference between values for the pT1c and pT2 stadium was statistically significant (p = 0.0296). The same applies to grading, but the differences were not significant (p>0.05). We could not find significant differences for angioinvasion of the tumor, probably for small number of patients with angioinvasion (p>0.05).Only the stage of the tumor was shown to be significant in predicting the metastasis in non-SLN in our

  6. Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Pingzhao Hu

    2006-01-01

    biological pathways. In particular, we observed that by integrating information from the insulin signalling pathway into our prediction model, we achieved better prediction of prostate cancer. Conclusions: Our data integration methodology provides an efficient way to identify biologically sound and statistically significant pathways from gene expression data. The significant gene expression phenotypes identified in our study have the potential to characterize complex genetic alterations in prostate cancer.

  7. Impact of D-Dimer for Prediction of Incident Occult Cancer in Patients with Unprovoked Venous Thromboembolism

    Science.gov (United States)

    Han, Donghee; ó Hartaigh, Bríain; Lee, Ji Hyun; Cho, In-Jeong; Shim, Chi Young; Chang, Hyuk-Jae; Hong, Geu-Ru; Ha, Jong-Won; Chung, Namsik

    2016-01-01

    Background Unprovoked venous thromboembolism (VTE) is related to a higher incidence of occult cancer. D-dimer is clinically used for screening VTE, and has often been shown to be present in patients with malignancy. We explored the predictive value of D-dimer for detecting occult cancer in patients with unprovoked VTE. Methods We retrospectively examined data from 824 patients diagnosed with deep vein thrombosis or pulmonary thromboembolism. Of these, 169 (20.5%) patients diagnosed with unprovoked VTE were selected to participate in this study. D-dimer was categorized into three groups as: 4,000 ng/ml. Cox regression analysis was employed to estimate the odds of occult cancer and metastatic state of cancer according to D-dimer categories. Results During a median 5.3 (interquartile range: 3.4–6.7) years of follow-up, 24 (14%) patients with unprovoked VTE were diagnosed with cancer. Of these patients, 16 (67%) were identified as having been diagnosed with metastatic cancer. Log transformed D-dimer levels were significantly higher in those with occult cancer as compared with patients without diagnosis of occult cancer (3.5±0.5 vs. 3.2±0.5, P-value = 0.009, respectively). D-dimer levels >4,000 ng/ml was independently associated with occult cancer (HR: 4.12, 95% CI: 1.54–11.04, P-value = 0.005) when compared with D-dimer levels 4000 ng/ml were also associated with a higher likelihood of metastatic cancer (HR: 9.55, 95% CI: 2.46–37.17, P-value 4000 ng/ml are independently associated with the likelihood of occult cancer among patients with unprovoked VTE. PMID:27073982

  8. Improving Software Engineering on NASA Projects

    Science.gov (United States)

    Crumbley, Tim; Kelly, John C.

    2010-01-01

    Software Engineering Initiative: Reduces risk of software failure -Increases mission safety. More predictable software cost estimates and delivery schedules. Smarter buyer of contracted out software. More defects found and removed earlier. Reduces duplication of efforts between projects. Increases ability to meet the challenges of evolving software technology.

  9. Age-Adjusted PSA Levels in Prostate Cancer Prediction: Updated Results of the Tyrol Prostate Cancer Early Detection Program.

    Directory of Open Access Journals (Sweden)

    Isabel Heidegger

    Full Text Available To reduce the number of unnecessary biopsies in patients with benign prostatic disease, however, without missing significant PCa the present study re-evaluates the age-dependent PSA cut-offs in the Tyrol Prostate Cancer (PCa early detection program.The study population included 2225 patients who underwent prostate biopsy due to elevated PSA levels at our department. We divided our patient collective into four age groups: ≤49 years (n = 178, 50-59 years (n = 597, 60-69 years (n = 962 and ≥70 years (n = 488. We simulated different scenarios for PSA cut-off values between 1.25 and 6 ng/mL and fPSA% between 15 and 21% for all four age groups and calculated sensitivity, specificity, confidence intervals and predictive values.PCa was detected in 1218 men (54.7%. We found that in combination with free PSA ≤21% the following PSA cut-offs had the best cancer specificity: 1.75 ng/ml for men ≤49 years and 50-59 years, 2.25 ng/ml for men aged 60-69 years and 3.25 ng/ml for men ≥70 years. Using these adjusted PSA cut-off values all significant tumors are recognized in all age groups, yet the number of biopsies is reduced. Overall, one biopsy is avoided in 13 to 14 men (number needed to screen = 13.3, reduction of biopsies = 7.5% when decision regarding biopsy is done according to the "new" cut-off values instead of the "old" ones. For the different age groups the number needed to screen to avoid one biopsy varied between 9.2 (≤49 years and 17.4 (50-59 years.With "new", fine-tuned PSA cut-offs we detect all relevant PCa with a significant reduction of biopsies compared to the "old" cut-off values. Optimization of age-specific PSA cut-offs is one step towards a smarter strategy in the Tyrol PCa Early Detection Program.

  10. Circulating plasma MiR-141 is a novel biomarker for metastatic colon cancer and predicts poor prognosis.

    Directory of Open Access Journals (Sweden)

    Hanyin Cheng

    Full Text Available BACKGROUND: Colorectal cancer (CRC remains one of the major cancer types and cancer related death worldwide. Sensitive, non-invasive biomarkers that can facilitate disease detection, staging and prediction of therapeutic outcome are highly desirable to improve survival rate and help to determine optimized treatment for CRC. The small non-coding RNAs, microRNAs (miRNAs, have recently been identified as critical regulators for various diseases including cancer and may represent a novel class of cancer biomarkers. The purpose of this study was to identify and validate circulating microRNAs in human plasma for use as such biomarkers in colon cancer. METHODOLOGY/PRINCIPAL FINDINGS: By using quantitative reverse transcription-polymerase chain reaction, we found that circulating miR-141 was significantly associated with stage IV colon cancer in a cohort of 102 plasma samples. Receiver operating characteristic (ROC analysis was used to evaluate the sensitivity and specificity of candidate plasma microRNA markers. We observed that combination of miR-141 and carcinoembryonic antigen (CEA, a widely used marker for CRC, further improved the accuracy of detection. These findings were validated in an independent cohort of 156 plasma samples collected at Tianjin, China. Furthermore, our analysis showed that high levels of plasma miR-141 predicted poor survival in both cohorts and that miR-141 was an independent prognostic factor for advanced colon cancer. CONCLUSIONS/SIGNIFICANCE: We propose that plasma miR-141 may represent a novel biomarker that complements CEA in detecting colon cancer with distant metastasis and that high levels of miR-141 in plasma were associated with poor prognosis.

  11. Circulating Plasma MiR-141 Is a Novel Biomarker for Metastatic Colon Cancer and Predicts Poor Prognosis

    Science.gov (United States)

    Cogdell, David E.; Zheng, Hong; Schetter, Aaron J.; Nykter, Matti; Harris, Curtis C.; Chen, Kexin; Hamilton, Stanley R.; Zhang, Wei

    2011-01-01

    Background Colorectal cancer (CRC) remains one of the major cancer types and cancer related death worldwide. Sensitive, non-invasive biomarkers that can facilitate disease detection, staging and prediction of therapeutic outcome are highly desirable to improve survival rate and help to determine optimized treatment for CRC. The small non-coding RNAs, microRNAs (miRNAs), have recently been identified as critical regulators for various diseases including cancer and may represent a novel class of cancer biomarkers. The purpose of this study was to identify and validate circulating microRNAs in human plasma for use as such biomarkers in colon cancer. Methodology/Principal Findings By using quantitative reverse transcription-polymerase chain reaction, we found that circulating miR-141 was significantly associated with stage IV colon cancer in a cohort of 102 plasma samples. Receiver operating characteristic (ROC) analysis was used to evaluate the sensitivity and specificity of candidate plasma microRNA markers. We observed that combination of miR-141 and carcinoembryonic antigen (CEA), a widely used marker for CRC, further improved the accuracy of detection. These findings were validated in an independent cohort of 156 plasma samples collected at Tianjin, China. Furthermore, our analysis showed that high levels of plasma miR-141 predicted poor survival in both cohorts and that miR-141 was an independent prognostic factor for advanced colon cancer. Conclusions/Significance We propose that plasma miR-141 may represent a novel biomarker that complements CEA in detecting colon cancer with distant metastasis and that high levels of miR-141 in plasma were associated with poor prognosis. PMID:21445232

  12. No benefit for consensus double reading at baseline screening for lung cancer with the use of semiautomated volumetry software

    NARCIS (Netherlands)

    Y. Wang (Ying); R.J. van Klaveren (Rob); G.H. de Bock (Geertruida); Y. Zhao (Yingru); R. Vernhout; A.L.M. Leusveld (Anne); E.T. Scholten (Ernst); J. Verschakelen (Johny); W.P. Mali (Willem); H.J. de Koning (Harry); M. Oudkerk (Matthijs)

    2012-01-01

    textabstractPurpose: To retrospectively evaluate the performance of consensus double reading compared with single reading at baseline screening of a lung cancer computed tomography (CT) screening trial. Materials and Methods: The study was approved by the Dutch Minister of Health and ethical committ

  13. No Benefit for Consensus Double Reading at Baseline Screening for Lung Cancer with the Use of Semiautomated Volumetry Software

    NARCIS (Netherlands)

    Wang, Y.; van Klaveren, R.J.; de Bock, G.H.; Zhao, Y.; Vernhout, R.; Leusveld, A.; Scholten, E.; Verschakelen, J.; Mali, W.; de Koning, H.; Oudkerk, M.

    2012-01-01

    Purpose: To retrospectively evaluate the performance of consensus double reading compared with single reading at baseline screening of a lung cancer computed tomography (CT) screening trial. Materials and Methods: The study was approved by the Dutch Minister of Health and ethical committees. Written

  14. Clinicopathological Analysis as Predictive Factors for Recurrence in Early Gastric Cancer

    Institute of Scientific and Technical Information of China (English)

    Hua Li; Ping Lu; Caigang Liu; Huimian Xu; Shubao Wang; Junqing Chen

    2008-01-01

    OBJECTIVE To identify clinicopathological characteristics as predictive factors for recurrence in early gastric cancer (EGC), and to determine which lesions should be removed by gastrectomy by means other than endoscopic mucosal resection (EMR).METHODS Data from 249 patients with EGC were collected and the relationship between their clinicopathological characteristics and postoperative recurrence was retrospectively analyzed by univariate analysis.RESULTS Of the 249 patients after gastrectomy, 19 cases (7.6%)experienced a recurrence. The postoperative recurrence rate was 18.9%(7/37) in patients with lymph node metastasis, and 5.7%(12/212) in those without. Lymph node metastases were found to be significantly related to recurrence in EGC (P = 0.005).CONCLUSION Lymph node metastases were the only predictive factor for recurrence in EGC. However, this was not the determining factor for performing gastrectomy rather than EMR. Although after gastrectomy with lymphadenectomy of EGC, patients with lymph node metastasis should be considered as candidates for adjuvant treatment. For lymph-node metastatic EGCs, adjuvant therapy is recommended following gastrectomy with lymphadenectomy.

  15. Predictive Factors of the Response of Rectal Cancer to Neoadjuvant Radiochemotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Spolverato, Gaya; Pucciarelli, Salvatore [Department of Oncology and Surgical Sciences, Section of Surgery, University of Padova, Padova 35128 (Italy); Bertorelle, Roberta [Istituto Oncologico Veneto-IRCCS, Padova 35128 (Italy); De Rossi, Anita, E-mail: anita.derossi@unipd.it [Istituto Oncologico Veneto-IRCCS, Padova 35128 (Italy); Department of Oncology and Surgical Sciences, Section of Oncology, University of Padova, Padova 35128 (Italy); Nitti, Donato [Department of Oncology and Surgical Sciences, Section of Surgery, University of Padova, Padova 35128 (Italy)

    2011-04-26

    Locally advanced rectal cancer is currently treated with pre-operative radiochemotherapy (pRCT), but the response is not uniform. Identification of patients with higher likelihood of responding to pRCT is clinically relevant, as patients with resistant tumors could be spared exposure to radiation or DNA-damaging drugs that are associated with adverse side effects. To highlight predictive biomarkers of response to pRCT, a systematic search of PubMed was conducted with a combination of the following terms: “rectal”, “predictive”, “radiochemotherapy”, “neoadjuvant”, “response” and “biomarkers”. Genetic polymorphisms in epithelial growth factor receptor (EGFR) and thymidylate synthase (TS) genes, the expression of several markers, such as EGFR, bcl-2/bax and cyclooxygenase (COX)-2, and circulating biomarkers, such as serum carcinoembryonic antigen (CEA) level, are promising as predictor markers, but need to be further evaluated. The majority of the studies did not support the predictive value of p53, while the values of Ki-67, TS and p21 is still controversial. Gene expression profiles of thousands of genes using microarrays, microRNA studies and the search for new circulating molecules, such as human telomerase reverse transcriptase mRNA and cell-free DNA, are providing interesting results that might lead to the identification of new useful biomarkers. Evaluation of biomarkers in larger, prospective trials are required to guide therapeutic strategies.

  16. Can primary optimal cytoreduction be predicted in advanced epithelial ovarian cancer preoperatively?

    Directory of Open Access Journals (Sweden)

    Behtash Nadereh

    2010-02-01

    Full Text Available Abstract Introduction Prediction of optimal cytoreduction in patients with advanced epithelial ovarian caner preoperatively. Methods Patients with advanced epithelial ovarian cancer who underwent surgery for the first time from Jan. to June 2008 at gynecologic oncology ward of TUMS (Tehran University of Medical Sciences were eligible for this study. The possibility of predicting primary optimal cytoreduction considering multiple variables was evaluated. Variables were peritoneal carcinomatosis, serum CA125, ascites, pleural effusion, physical status and imaging findings. Univariate comparisons of patients underwent suboptimal cytoreduction carried out using Fisher's exact test for each of the potential predictors. The wilcoxon rank sum test was used to compare variables between patients with optimal versus suboptimal cytoreduction. Results 41 patients met study inclusion criteria. Statistically significant association was noted between peritoneal carcinomatosis and suboptimal cytoreduction. There were no statistically significant differences between physical status, pleural effusion, imaging findings, serum CA125 and ascites of individuals with optimal cytoreduction compared to those with suboptimal cytoreduction. Conclusions Because of small populations in our study the results are not reproducible in alternate populations. Only the patient who is most unlikely to undergo optimal cytoreduction should be offered neoadjuvant chemotherapy, unless her medical condition renders her unsuitable for primary surgery.

  17. Predicting the need for adaptive radiotherapy in head and neck cancer

    International Nuclear Information System (INIS)

    Background and purpose: Adaptive radiotherapy (ART) can account for the dosimetric impact of anatomical change in head and neck cancer patients; however it can be resource intensive. Consequently, it is imperative that patients likely to require ART are identified. The purpose of this study was to find predictive factors that identify oropharyngeal squamous cell carcinoma (OPC) and nasopharyngeal carcinoma (NPC) patients more likely to need ART. Materials and methods: One hundred and ten patients with OPC or NPC were analysed. Patient demographics and tumour characteristics were compared between patients who were replanned and those that were not. Factors found to be significant were included in logistic regression models. Risk profiles were developed from these models. A dosimetric analysis was performed. Results: Nodal disease stage, pre-treatment largest involved node size, diagnosis and initial weight (categorised in 2 groups) were identified as significant for inclusion in the model. Two models were found to be significant (p = 0.001), correctly classifying 98.2% and 96.1% of patients respectively. Three ART risk profiles were developed. Conclusion: Predictive factors identifying OPC or NPC patients more likely to require ART were reported. A risk profile approach could facilitate the effective implementation of ART into radiotherapy departments through forward planning and appropriate resource allocation

  18. Software Reviews.

    Science.gov (United States)

    Smith, Richard L., Ed.

    1985-01-01

    Reviews software packages by providing extensive descriptions and discussions of their strengths and weaknesses. Software reviewed include (1) "VISIFROG: Vertebrate Anatomy" (grade seven-adult); (2) "Fraction Bars Computer Program" (grades three to six) and (3) four telecommunications utilities. (JN)

  19. Prediction of sensitivity to anticancer agents for patients with advanced or recurrent breast cancer by Tc-99m sestamibi

    International Nuclear Information System (INIS)

    Tc-99m Sestamibi (99mTc-MIBI) is known to be a substrate of P-glycoprotein (P-gp) that effluxes the drugs out of cancer cells. The overexpression of P-gp involved in multidrug resistance phenomenon in patients with advanced or recurrent breast cancers was shown in the plasma membrane of breast cancer cells. In this study, we examined the usefulness of 99mTc-MIBI scintigraphy for the prediction of sensitivity to anticancer agents in 8 cases with advanced or recurrent breast cancer. The retrospective analysis showed that the sensitivity to the chemotherapy could be evaluated in 3 cases by 99mTc-MIBI scintigraphy, but in the other 5 cases 99mTc-MIBI scintigraphy was not eligible for the prediction of sensitivity. Two out of 3 cases showed over 50% in reduction rate of target tumors (PR) with higher accumulation of 99mTc-MIBI, while another case with PD showed lower. These results suggest that the accumulation of 99mTc-MIBI could be associated with the sensitivity to P-gp-related anticancer agents, and that the functional analysis of P-gp by 99mTc-MIBI might be useful for the prediction of responsiveness of chemotherapy in patients with breast cancer. (author)

  20. Locally advanced rectal cancer: Value of ADC mapping in prediction of tumor response to radiochemotherapy

    International Nuclear Information System (INIS)

    Purpose: To evaluate the diagnostic performance of quantitative apparent diffusion coefficient (ADC) measurements, in the assessment of the therapeutic response to chemo-radiation therapy (CRT) in patients with locally advanced rectal cancer, by analyzing post CRT values of ADC, in relation to tumor regression grade (TRG) obtained by histopathologic evaluation of the rectal specimen. Methods: This prospective study was approved by an Institutional Review Board, and informed consent was obtained from all patients. Thirty-one patients with locally advanced rectal cancer underwent pre and post CRT MR imaging at 1.5 T. ADC values were measured in regions of interest (ROIs) drawn independently by two radiologists, blinded to the pathology results, on three slices of the pre and post CRT DW-MR image sets with the corresponding T2 weighted images (T2WI) available for anatomic reference. The two readers’ measurements were compared for differences in ADC values, inter-observer variability (measured as the intraclass correlation coefficient; ICC) and the ADC distributions of responders vs non-responders. The diagnostic performance of ADC in the prediction of the response to CRT was evaluated by calculating the area under the ROC curve (AUC) and the optimal cut-off values. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were assessed. Results: The two readers showed an overall strong agreement in measuring ADC values. For both readers, no differences in ADC pre-treatment measurements were observed between responders and non-responders. For reader 1, the post-CRT ADC and the ΔADC presented the higher AUC (0.823 and 0.803, respectively), while Δ%ADC provided the lower AUC value (0.682). The optimal cutoff point was 1.294 s/mm2 for post-CRT measures (sensitivity = 86.4%, specificity = 66.7%, PPV = 86.4%, NPV = 66.7%), 0.500 for ΔADC (sensitivity = 81.8%, specificity = 66.7%, PPV = 85.7%, NPV = 60.0%) and 59.3% for