WorldWideScience

Sample records for cancer prediction software

  1. Predictive software design measures

    OpenAIRE

    Love, Randall James

    1994-01-01

    This research develops a set of predictive measures enabling software testers and designers to identify and target potential problem areas for additional and/or enhanced testing. Predictions are available as early in the design process as requirements allocation and as late as code walk-throughs. These predictions are based on characteristics of the design artifacts prior to coding. Prediction equations are formed at established points in the software development process...

  2. Improving software quality with software error prediction

    OpenAIRE

    Taipale, T. (Taneli)

    2015-01-01

    Today's agile software development can be a complicated process, especially when dealing with a large-scale project with demands for tight communication. The tools used in software development, while aiding the process itself, can also offer meaningful statistics. With the aid of machine learning, these statistics can be used for predicting the behavior patterns of the development process. The starting point of this thesis is a software project developed to be a part of a large telecommun...

  3. Software Structure and WCET Predictability

    OpenAIRE

    Gebhard, Gernot; Cullmann, Christoph; Heckmann, Reinhold

    2011-01-01

    Being able to compute worst-case execution time bounds for tasks of an embedded software system with hard real-time constraints is crucial to ensure the correct (timing) behavior of the overall system. Any means to increase the (static) time predictability of the embedded software are of high interest -- especially due to the ever-growing complexity of such software systems. In this paper we study existing coding proposals and guidelines, such as MISRA-C, and investigate whether they simplify...

  4. Predicting User Actions in Software Processes

    CERN Document Server

    Deynet, Michael

    2011-01-01

    This paper describes an approach for user (e.g. SW architect) assisting in software processes. The approach observes the user's action and tries to predict his next step. For this we use approaches in the area of machine learning (sequence learning) and adopt these for the use in software processes. Keywords: Software engineering, Software process description languages, Software processes, Machine learning, Sequence prediction

  5. PIPS: pathogenicity island prediction software.

    Directory of Open Access Journals (Sweden)

    Siomar C Soares

    Full Text Available The adaptability of pathogenic bacteria to hosts is influenced by the genomic plasticity of the bacteria, which can be increased by such mechanisms as horizontal gene transfer. Pathogenicity islands play a major role in this type of gene transfer because they are large, horizontally acquired regions that harbor clusters of virulence genes that mediate the adhesion, colonization, invasion, immune system evasion, and toxigenic properties of the acceptor organism. Currently, pathogenicity islands are mainly identified in silico based on various characteristic features: (1 deviations in codon usage, G+C content or dinucleotide frequency and (2 insertion sequences and/or tRNA genetic flanking regions together with transposase coding genes. Several computational techniques for identifying pathogenicity islands exist. However, most of these techniques are only directed at the detection of horizontally transferred genes and/or the absence of certain genomic regions of the pathogenic bacterium in closely related non-pathogenic species. Here, we present a novel software suite designed for the prediction of pathogenicity islands (pathogenicity island prediction software, or PIPS. In contrast to other existing tools, our approach is capable of utilizing multiple features for pathogenicity island detection in an integrative manner. We show that PIPS provides better accuracy than other available software packages. As an example, we used PIPS to study the veterinary pathogen Corynebacterium pseudotuberculosis, in which we identified seven putative pathogenicity islands.

  6. Software architecture and design for reliability predictability

    CERN Document Server

    Semegn, Assefa D

    2011-01-01

    Reliability prediction of a software product is complex due to interdependence and interactions among components and the difficulty of representing this behavior with tractable models. Models developed by making simplifying assumptions about the software

  7. 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.

  8. 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.

  9. Evaluation of competing software reliability predictions

    Science.gov (United States)

    Abdel-Ghaly, A. A.; Chan, P. Y.; Littlewood, B.

    1986-01-01

    Different software reliability models can produce very different answers when called upon to predict future reliability in a reliability growth context. Users need to know which, if any, of the competing predictions are trustworthy. Some techniques are presented which form the basis of a partial solution to this problem. Rather than attempting to decide which model is generally best, the approach adopted here allows a user to decide upon the most appropriate model for each application.

  10. 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...

  11. Software analysis handbook: Software complexity analysis and software reliability estimation and prediction

    Science.gov (United States)

    Lee, Alice T.; Gunn, Todd; Pham, Tuan; Ricaldi, Ron

    1994-01-01

    This handbook documents the three software analysis processes the Space Station Software Analysis team uses to assess space station software, including their backgrounds, theories, tools, and analysis procedures. Potential applications of these analysis results are also presented. The first section describes how software complexity analysis provides quantitative information on code, such as code structure and risk areas, throughout the software life cycle. Software complexity analysis allows an analyst to understand the software structure, identify critical software components, assess risk areas within a software system, identify testing deficiencies, and recommend program improvements. Performing this type of analysis during the early design phases of software development can positively affect the process, and may prevent later, much larger, difficulties. The second section describes how software reliability estimation and prediction analysis, or software reliability, provides a quantitative means to measure the probability of failure-free operation of a computer program, and describes the two tools used by JSC to determine failure rates and design tradeoffs between reliability, costs, performance, and schedule.

  12. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  13. Bottlenecks in Software Defect Prediction Implementation in Industrial Projects

    Directory of Open Access Journals (Sweden)

    Hryszko Jarosław

    2015-03-01

    Full Text Available Case studies focused on software defect prediction in real, industrial software development projects are extremely rare. We report on dedicated R&D project established in cooperation between Wroclaw University of Technology and one of the leading automotive software development companies to research possibilities of introduction of software defect prediction using an open source, extensible software measurement and defect prediction framework called DePress (Defect Prediction in Software Systems the authors are involved in. In the first stage of the R&D project, we verified what kind of problems can be encountered. This work summarizes results of that phase.

  14. 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...

  15. Software Reliability Prediction – An Evaluation of a Novel Technique

    OpenAIRE

    Andersson, Björn; Persson, Marie

    2004-01-01

    Along with continuously increasing computerization, our expectations on software and hardware reliability increase considerably. Therefore, software reliability has become one of the most important software quality attributes. Software reliability modeling based on test data is done to estimate whether the current reliability level meets the requirements for the product. Software reliability modeling also provides possibilities to predict reliability. Costs of software developing and tests to...

  16. 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.

  17. 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.

  18. 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

  19. 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

  20. 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…

  1. Software Defect Association Mining and Defect Correction Effort Prediction

    OpenAIRE

    Song, Q; Shepperd, MJ; Cartwright, MH; Mair, C.

    2006-01-01

    Much current software defect prediction work concentrates on the number of defects remaining in software system. In this paper, we present association rule mining based methods to predict defect associations and defect-correction effort. This is to help developers detect software defects and assist project managers in allocating testing resources more effectively. We applied the proposed methods to the SEL defect data consisting of more than 200 projects over more than 15 years. The results s...

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    Sanaa Elyassami

    2012-03-01

    Full Text Available Many cost estimation models have been proposed over the last three decades. In this study, we investigate fuzzy ID3 decision tree as a method for software effort estimation. Fuzzy ID software effort estimation model 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 software projects. MMRE (Mean Magnitude of Relative Error and Pred(l (Prediction at level l are used, as measures of prediction accuracy, for this study. A series of experiments is reported using ISBSG software projects dataset. Fuzzy trees are grown using different fuzziness control thresholds. Results showed that optimizing the fuzzy ID3 parameters can improve greatly the accuracy of the generated software cost estimate.

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

    CERN Document Server

    Elyassami, Sanaa

    2012-01-01

    Many cost estimation models have been proposed over the last three decades. In this study, we investigate fuzzy ID3 decision tree as a method for software effort estimation. Fuzzy ID software effort estimation model 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 software projects. MMRE (Mean Magnitude of Relative Error) and Pred(l) (Prediction at level l) are used, as measures of prediction accuracy, for this study. A series of experiments is reported using ISBSG software projects dataset. Fuzzy trees are grown using different fuzziness control thresholds. Results showed that optimizing the fuzzy ID3 parameters can improve greatly the accuracy of the generated software cost estimate.

  5. Predictive Usability Evaluation: Aligning HCI and Software Engineering Practices

    OpenAIRE

    Káthia, Marçal De Oliveira; Sophie, Lepreux; Christophe, Kolski; Ahmed, Seffah

    2014-01-01

    Can we - software developers, usability experts, user interface designers - predict usability from the early user interface (UI) design artifacts and models? Can we define predictive measures to evaluate usability without a concrete UI? These questions seemed natural for us since UI modeling (task, user, concepts, etc.) is being largely explored in recent years for the automatic generation of final UI. To answer those questions we propose a model-based predictive usability evaluation approach...

  6. Logistic Regression Approach to Software Reliability Engineering with Failure Prediction

    Directory of Open Access Journals (Sweden)

    K.Venkata Subba Reddy

    2013-02-01

    Full Text Available In this paper using the main feature of our proposed Model in its inflection point, we propose a softwarereliability growth model, which relatively early in the testing and debugging phase, provides accurateparameters estimation, gives a very good failure behavior prediction and enable software developers topredict when to conclude testing, release the software and avoid over testing in order to cut the cost duringthe development and the maintenance of the software. Two real world experimental data previouslyanalyzed have been used to compare our proposed Early Estimation Logistic Model effectiveness withseveral pre-existing models.

  7. Practical application of predictive microbiology software programs to HACCP plans.

    Science.gov (United States)

    Fujikawa, H; Kokubo, Y

    2001-08-01

    We studied how predictive microbiology models could practically be applied to HACCP plans with two predictive software programs that are currently available. The software programs were the Food Micromodel elaborated by the Ministry of Agriculture, Fisheries, and Food, U.K. and the Pathogen Modeling Program of Eastern Regional Research Center, U.S. Department of Agriculture. They successfully provided useful information on (i) the determination of Critical Control Points (CCPs), (ii) the estimation of critical limits at CCPs, (iii) the decision of abused products, (iv) the assessment of equivalence of HACCP plans, and further (v) the development of new products. With the information simulated by the software programs, HACCP teams could make scientific and objective decisions for developing their individual plans. It was also confirmed that microbiological process standards for food processing are indispensable for the application of the predictive programs to HACCP plans. PMID:11817141

  8. Prediction of software operational reliability using testing environment factor

    International Nuclear Information System (INIS)

    Software reliability is especially important to customers these days. The need to quantify software reliability of safety-critical systems has been received very special attention and the reliability is rated as one of software's most important attributes. Since the software is an intellectual product of human activity and since it is logically complex, the failures are inevitable. No standard models have been established to prove the correctness and to estimate the reliability of software systems by analysis and/or testing. For many years, many researches have focused on the quantification of software reliability and there are many models developed to quantify software reliability. Most software reliability models estimate the reliability with the failure data collected during the test assuming that the test environments well represent the operation profile. User's interest is on the operational reliability rather than on the test reliability, however. The experiences show that the operational reliability is higher than the test reliability. With the assumption that the difference in reliability results from the change of environment, testing environment factor comprising the aging factor and the coverage factor are defined in this work to predict the ultimate operational reliability with the failure data. It is by incorporating test environments applied beyond the operational profile into testing environment factor Test reliability can also be estimated with this approach without any model change. The application results are close to the actual data. The approach used in this thesis is expected to be applicable to ultra high reliable software systems that are used in nuclear power plants, airplanes, and other safety-critical applications

  9. 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.

  10. 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...

  11. 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.

  12. Development of pipe wall thinning prediction software 'FALSET'

    International Nuclear Information System (INIS)

    Pipe wall thinning in power plants has been managed for maintaining plant integrity and safety with great importance. The target thinning phenomena are Flow Accelerated Corrosion (FAC) and Liquid Droplet Impingement Erosion (LDI). At present, the management is based on thinning rate and residual lifetime evaluation using pipe wall thickness measurement results. For the future, more safety and improvement in the management is required, and in this sense, prediction method of wall thinning is willing to be introduced. Therefore, prediction model of FAC and LDI have been constructed in CRIEPI, and to utilize these models to actual plant piping management easily, prediction software 'FALSET' is developed. FALSET has equipped with essential function for pipe wall thinning management in power plants, as follows; (1) Information and condition input of plant piping system and its component, (2) Wall thinning rate evaluation with CRIEPI's FAC/LDI prediction model, (3) Loading of wall thickness measurement data files and graphics of data trend, (4) Residual lifetime evaluation considering both measured and predicted thinning rate, (5) Statistical process and graphics of thinning rate and residual lifetime for multi-piping systems. With further verification and improvement of each function, there will be a perspective for this FALSET to be utilized as a management tool in power plants. (author)

  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. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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)

  2. Predicting Numbers of Problems in Development of Software

    Science.gov (United States)

    Simonds, Charles H.

    2005-01-01

    A method has been formulated to enable prediction of the amount of work that remains to be performed in developing flight software for a spacecraft. The basic concept embodied in the method is that of using an idealized curve (specifically, the Weibull function) to interpolate from (1) the numbers of problems discovered thus far to (2) a goal of discovering no new problems after launch (or six months into the future for software already in use in orbit). The steps of the method can be summarized as follows: 1. Take raw data in the form of problem reports (PRs), including the dates on which they are generated. 2. Remove, from the data collection, PRs that are subsequently withdrawn or to which no response is required. 3. Count the numbers of PRs created in 1-week periods and the running total number of PRs each week. 4. Perform the interpolation by making a least-squares fit of the Weibull function to (a) the cumulative distribution of PRs gathered thus far and (b) the goal of no more PRs after the currently anticipated launch date. The interpolation and the anticipated launch date are subject to iterative re-estimation.

  3. 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.

  4. Search-based approaches to software fault prediction and software testing

    OpenAIRE

    Afzal, Wasif

    2009-01-01

    Software verification and validation activities are essential for software quality but also constitute a large part of software development costs. Therefore efficient and cost-effective software verification and validation activities are both a priority and a necessity considering the pressure to decrease time-to-market and intense competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions related to software quality, when to stop testing, testing...

  5. 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.

  6. 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.

  7. 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

  8. 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...... procedure type, age and performance status. CONCLUSIONS: Neither score performs well enough to be advocated for individual risk stratification prior to lung cancer surgery. It may be that additional physiological parameters are required; however this is a further project. In the interim we propose the use...

  9. Androgen receptor profiling predicts prostate cancer outcome

    OpenAIRE

    Stelloo, Suzan; Nevedomskaya, Ekaterina; van der Poel, Henk G.; de Jong, Jeroen; van Leenders, Geert JLH; Jenster, Guido; Wessels, Lodewyk FA; Bergman, Andries M; Zwart, Wilbert

    2015-01-01

    Prostate cancer is the second most prevalent malignancy in men. Biomarkers for outcome prediction are urgently needed, so that high-risk patients could be monitored more closely postoperatively. To identify prognostic markers and to determine causal players in prostate cancer progression, we assessed changes in chromatin state during tumor development and progression. Based on this, we assessed genomewide androgen receptor/chromatin binding and identified a distinct androgen receptor/chromati...

  10. Prediction of Human Performance Capability during Software Development using Classification

    OpenAIRE

    Gupta, Sangita; V, Suma.

    2014-01-01

    The quality of human capital is crucial for software companies to maintain competitive advantages in knowledge economy era. Software companies recognize superior talent as a business advantage. They increasingly recognize the critical linkage between effective talent and business success. However, software companies suffering from high turnover rates often find it hard to recruit the right talents. There is an urgent need to develop a personnel selection mechanism to find the talents who are ...

  11. A Machine Learning based Efficient Software Reusability Prediction Model for Java Based Object Oriented Software

    OpenAIRE

    Surbhi Maggo; Chetna Gupta

    2014-01-01

    Software reuse refers to the development of new software systems with the likelihood of completely or partially using existing components or resources with or without modification. Reusability is the measure of the ease with which previously acquired concepts and objects can be used in new contexts. It is a promising strategy for improvements in software quality, productivity and maintainability as it provides for cost effective, reliable (with the consideration that prior testing and use has...

  12. Safety prediction for basic components of safety critical software based on static testing

    International Nuclear Information System (INIS)

    The purpose of this work is to develop a safety prediction method, with which we can predict the risk of software components based on static testing results at the early development stage. The predictive model combines the major factor with the quality factor for the components, both of which are calculated based on the measures proposed in this work. The application to a safety-critical software system demonstrates the feasibility of the safety prediction method. (authors)

  13. Safety prediction for basic components of safety-critical software based on static testing

    International Nuclear Information System (INIS)

    The purpose of this work is to develop a safety prediction method, with which we can predict the risk of software components based on static testing results at the early development stage. The predictive model combines the major factor with the quality factor for the components, which are calculated based on the measures proposed in this work. The application to a safety-critical software system demonstrates the feasibility of the safety prediction method. (authors)

  14. 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

  15. A Machine Learning based Efficient Software Reusability Prediction Model for Java Based Object Oriented Software

    Directory of Open Access Journals (Sweden)

    Surbhi Maggo

    2014-01-01

    Full Text Available Software reuse refers to the development of new software systems with the likelihood of completely or partially using existing components or resources with or without modification. Reusability is the measure of the ease with which previously acquired concepts and objects can be used in new contexts. It is a promising strategy for improvements in software quality, productivity and maintainability as it provides for cost effective, reliable (with the consideration that prior testing and use has eliminated bugs and accelerated (reduced time to market development of the software products. In this paper we present an efficient automation model for the identification and evaluation of reusable software components to measure the reusability levels (high, medium or low of procedure oriented Java based (object oriented software systems. The presented model uses a metric framework for the functional analysis of the Object oriented software components that target essential attributes of reusability analysis also taking into consideration Maintainability Index to account for partial reuse. Further machine learning algorithm LMNN is explored to establish relationships between the functional attributes. The model works at functional level rather than at structural level. The system is implemented as a tool in Java and the performance of the automation tool developed is recorded using criteria like precision, recall, accuracy and error rate. The results gathered indicate that the model can be effectively used as an efficient, accurate, fast and economic model for the identification of procedure based reusable components from the existing inventory of software resources.

  16. 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...

  17. 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

  18. 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 ...

  19. Validating Model-Driven Performance Predictions on Random Software Systems

    Czech Academy of Sciences Publication Activity Database

    Babka, V.; Tůma, P.; Bulej, Lubomír

    Berlin : Springer, 2010 - (Heineman, G.; Kofroň, J.; Plášil, F.), s. 3-19 ISBN 978-3-642-13820-1. ISSN 0302-9743. - (Lecture Notes in Computer Science. 6093). [QoSA 2010. International Conference on the Quality of Software Architectures /6./. Prague (CZ), 23.06.2010-25.06.2010] R&D Projects: GA ČR GD201/09/H057 Institutional research plan: CEZ:AV0Z10300504 Keywords : performance modeling * performance validation * MDD Subject RIV: JC - Computer Hardware ; Software

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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...

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

    OpenAIRE

    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 metrics and hardware characteristics. The resulting prediction models provide essential information for such Co-design tasks, as identifying resource intensive parts of the application, helping to evalua...

  7. 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.

  8. 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

  9. Prediction Model for Gastric Cancer Incidence in Korean Population

    OpenAIRE

    Eom, Bang Wool; Joo, Jungnam; Kim, Sohee; Shin, Aesun; Yang, Hye-Ryung; Park, Junghyun; Choi, Il Ju; Kim, Young-Woo; Kim, Jeongseon; Nam, Byung-Ho

    2015-01-01

    Background Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea. Method Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific ...

  10. 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...

  11. 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

  12. 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.

  13. 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

  14. New predictions for Chernobyl childhood thyroid cancers

    International Nuclear Information System (INIS)

    New, firmer predictions are presented for the number of childhood thyroid cancers caused by Chernobyl: between 3300 and 7600 over all time, with a central estimate of 4400. The high efficacy of medical treatment suggests that at least 70% of the sufferers should survive the illness, with 95% or better survival a realistic target given early and skilled surgery and treatment. In view of the reported lack of evidence for other long-term health effects and the comparatively small number of early deaths, the total figure for deaths attributable to the Chernobyl accident may currently be estimated as from a few hundreds to a few thousands, with one thousand as a reasonable central estimate. (author)

  15. 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

  16. 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...

  17. 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.

  18. 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

  19. 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.

  20. Biomarkers for predicting complete debulking in ovarian cancer

    DEFF Research Database (Denmark)

    Fagö-Olsen, Carsten Lindberg; Ottesen, Bent; Christensen, Ib Jarle; Høgdall, Estrid; Lundvall, Lene; Nedergaard, Lotte; Engelholm, Svend-Aage; Antonsen, Sofie Leisby; Lydolph, Magnus; Høgdall, Claus

    2014-01-01

    AIM: We aimed to construct and validate a model based on biomarkers to predict complete primary debulking surgery for ovarian cancer patients. PATIENTS AND METHODS: The study consisted of three parts: Part I: Biomarker data obtained from mass spectrometry, baseline data and, surgical outcome were.......64. CONCLUSION: Our validated model based on biomarkers was unable to predict surgical outcome for patients with ovarian cancer....

  1. 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...

  2. 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...

  3. 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...

  4. Comparing Standard Regression Modeling to Ensemble Modeling: How Data Mining Software Can Improve Economists' Predictions

    OpenAIRE

    Joyce P. Jacobsen; Laurence M. Levin; Zachary Tausanovitch

    2014-01-01

    Economists’ wariness of data mining may be misplaced, even in cases where economic theory provides a well-specified model for estimation. We discuss how new data mining/ensemble modeling software, for example the program TreeNet, can be used to create predictive models. We then show how for a standard labor economics problem, the estimation of wage equations, TreeNet outperforms standard OLS regression in terms of lower prediction error. Ensemble modeling also resists the tendency to overfit ...

  5. Use of NMR and NMR Prediction Software to Identify Components in Red Bull Energy Drinks

    Science.gov (United States)

    Simpson, Andre J.; Shirzadi, Azadeh; Burrow, Timothy E.; Dicks, Andrew P.; Lefebvre, Brent; Corrin, Tricia

    2009-01-01

    A laboratory experiment designed as part of an upper-level undergraduate analytical chemistry course is described. Students investigate two popular soft drinks (Red Bull Energy Drink and sugar-free Red Bull Energy Drink) by NMR spectroscopy. With assistance of modern NMR prediction software they identify and quantify major components in each…

  6. Predicting Software Faults in Large Space Systems using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Bhekisipho Twala

    2011-07-01

    Full Text Available Recently, the use of machine learning (ML algorithms has proven to be of great practical value in solving a variety of engineering problems including the prediction of failure, fault, and defect-proneness as the space system software becomes complex. One of the most active areas of recent research in ML has been the use of ensemble classifiers. How ML techniques (or classifiers could be used to predict software faults in space systems, including many aerospace systems is shown, and further use ensemble individual classifiers by having them vote for the most popular class to improve system software fault-proneness prediction. Benchmarking results on four NASA public datasets show the Naive Bayes classifier as more robust software fault prediction while most ensembles with a decision tree classifier as one of its components achieve higher accuracy rates.Defence Science Journal, 2011, 61(4, pp.306-316, DOI:http://dx.doi.org/10.14429/dsj.61.1088

  7. Data and Cost handling Techniques for Software Quality Prediction Through Clustering

    Directory of Open Access Journals (Sweden)

    Saifi Bawahir , Mohsin Sheikh

    2012-12-01

    Full Text Available Analysis of Data quality is an important issue which has been addressed as data warehousing, data mining and information systems. It has been agreed that poor data quality will impact the quality of results of analyses and that it will therefore impact on decisions made on the basis of these results. An attempt to improve classification accuracy by pre-clustering did not succeed. However, error rates within clusters from training sets were strongly correlated with error rates within the same clusters on the test sets. This phenomenon could perhaps be used to develop confidence levels for predictions. The main and the common problem that the software industry has to face is the maintenance cost of industrial software systems. One of the main reasons for the high cost of maintenance is the inherent difficulty of understanding software systems that are large, complex, inconsistent and integrated. The main reason behind the above phenomena is because of different size and level of arrangements. Decomposing a software system into smaller, more manageable subsystems can aid the process of understanding it significantly. Different algorithms construct different decompositions. Therefore, it is important to have methods that evaluate the quality of such automatic decompositions. In our paper we present a brief survey on software quality prediction through clustering.

  8. 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.

  9. Active Mirror Predictive and Requirements Verification Software (AMP-ReVS)

    Science.gov (United States)

    Basinger, Scott A.

    2012-01-01

    This software is designed to predict large active mirror performance at various stages in the fabrication lifecycle of the mirror. It was developed for 1-meter class powered mirrors for astronomical purposes, but is extensible to other geometries. The package accepts finite element model (FEM) inputs and laboratory measured data for large optical-quality mirrors with active figure control. It computes phenomenological contributions to the surface figure error using several built-in optimization techniques. These phenomena include stresses induced in the mirror by the manufacturing process and the support structure, the test procedure, high spatial frequency errors introduced by the polishing process, and other process-dependent deleterious effects due to light-weighting of the mirror. Then, depending on the maturity of the mirror, it either predicts the best surface figure error that the mirror will attain, or it verifies that the requirements for the error sources have been met once the best surface figure error has been measured. The unique feature of this software is that it ties together physical phenomenology with wavefront sensing and control techniques and various optimization methods including convex optimization, Kalman filtering, and quadratic programming to both generate predictive models and to do requirements verification. This software combines three distinct disciplines: wavefront control, predictive models based on FEM, and requirements verification using measured data in a robust, reusable code that is applicable to any large optics for ground and space telescopes. The software also includes state-of-the-art wavefront control algorithms that allow closed-loop performance to be computed. It allows for quantitative trade studies to be performed for optical systems engineering, including computing the best surface figure error under various testing and operating conditions. After the mirror manufacturing process and testing have been completed, the

  10. 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...

  11. Predicting post-treatment survivability of patients with breast cancer using Artificial Neural Network methods.

    Science.gov (United States)

    Wang, Tan-Nai; Cheng, Chung-Hao; Chiu, Hung-Wen

    2013-01-01

    In the last decade, the use of data mining techniques has become widely accepted in medical applications, especially in predicting cancer patients' survival. In this study, we attempted to train an Artificial Neural Network (ANN) to predict the patients' five-year survivability. Breast cancer patients who were diagnosed and received standard treatment in one hospital during 2000 to 2003 in Taiwan were collected for train and test the ANN. There were 604 patients in this dataset excluding died not in breast cancer. Among them 140 patients died within five years after their first radiotherapy treatment. The artificial neural networks were created by STATISTICA(®) software. Five variables (age, surgery and radiotherapy type, tumor size, regional lymph nodes, distant metastasis) were selected as the input features for ANN to predict the five-year survivability of breast cancer patients. We trained 100 artificial neural networks and chose the best one to analyze. The accuracy rate is 85% and area under the receiver operating characteristic (ROC) curve is 0.79. It shows that artificial neural network is a good tool to predict the five-year survivability of breast cancer patients. PMID:24109931

  12. 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....

  13. 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.

  14. 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.

  15. Predictivity of human papillomavirus positivity in advanced oral cancer

    OpenAIRE

    Kane, S.; V M Patil; V Noronha; Joshi, A.; S Dhumal; Cruz, A D; Bhattacharjee, A; K Prabhash

    2015-01-01

    Background And Objective: Human papillomavirus (HPV) is a known prognostic factor world over in patients of carcinoma oropharynx. The role of HPV in oral cancers has not been investigated adequately. We tried to identify standard clinicopathological features in oral cancer, which would predict HPV-positivity. Methods: This was a retrospective analysis of 124 cases of T4 oral cancer patients at our center. HPV-positive was defined in accordance with positive p16 immunohistochemistry done on pr...

  16. 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 ...

  17. Software system for assessment and prediction of radiation situation in Chornobyl exclusion zone

    International Nuclear Information System (INIS)

    The structure and capabilities of the software system designed for assessment and prediction of radiation situation in Chornobyl exclusion zone are described. The system enables calculation of concentration fields of radionuclides in near-surface air and deposition density, exposure doses both after emergency releases from radiation-dangerous facilities within the exclusion zone and in the case of increased radionuclide emission under extreme weather conditions (including re-suspension and forest fires).

  18. Prediction of cancer drugs by chemical-chemical interactions.

    Directory of Open Access Journals (Sweden)

    Jing Lu

    Full Text Available Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug. The 1(st order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the 'wrong' predicted indications, indicating that some 'wrong' drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications.

  19. 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.

  20. 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

  1. 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

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. Software tools for simultaneous data visualization and T cell epitopes and disorder prediction in proteins.

    Science.gov (United States)

    Jandrlić, Davorka R; Lazić, Goran M; Mitić, Nenad S; Pavlović, Mirjana D

    2016-04-01

    We have developed EpDis and MassPred, extendable open source software tools that support bioinformatic research and enable parallel use of different methods for the prediction of T cell epitopes, disorder and disordered binding regions and hydropathy calculation. These tools offer a semi-automated installation of chosen sets of external predictors and an interface allowing for easy application of the prediction methods, which can be applied either to individual proteins or to datasets of a large number of proteins. In addition to access to prediction methods, the tools also provide visualization of the obtained results, calculation of consensus from results of different methods, as well as import of experimental data and their comparison with results obtained with different predictors. The tools also offer a graphical user interface and the possibility to store data and the results obtained using all of the integrated methods in the relational database or flat file for further analysis. The MassPred part enables a massive parallel application of all integrated predictors to the set of proteins. Both tools can be downloaded from http://bioinfo.matf.bg.ac.rs/home/downloads.wafl?cat=Software. Appendix A includes the technical description of the created tools and a list of supported predictors. PMID:26851400

  7. 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.

  8. 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...

  9. 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...

  10. 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...

  11. 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.

  12. Different methods and software tools for short-term prediction of wind energy production

    Energy Technology Data Exchange (ETDEWEB)

    Ibrahim, Hussein [Wind Energy TechnoCentre (Canada)

    2011-07-01

    This paper discusses the different methods and software tools used for short-term prediction of wind energy production. Forecasts of the production of wind farms are important for a variety of reasons, especially to guarantee security of supply of the power system. There are two types of short-term predictions, physical and statistical. Physical methods use physical considerations before using model output statistics to reduce error, while statistical methods, using recursive techniques, tries to find relationships between measured results. The different types of physical and statistical models and their performance factors are discussed in detail. There are models that use both physical and statistical considerations and these are called combined models. The different wind power forecasting tools available in the market are also mentioned. It can be concluded that there are several operational tools available to meet the diversity of end-user requirements. Further investments on wind installations should provide security of supply, lower financial risks and higher acceptability.

  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. Blood Epigenetic Age may Predict Cancer Incidence and Mortality

    Directory of Open Access Journals (Sweden)

    Yinan Zheng

    2016-03-01

    Full Text Available Biological measures of aging are important for understanding the health of an aging population, with epigenetics particularly promising. Previous studies found that tumor tissue is epigenetically older than its donors are chronologically. We examined whether blood Δage (the discrepancy between epigenetic and chronological ages can predict cancer incidence or mortality, thus assessing its potential as a cancer biomarker. In a prospective cohort, Δage and its rate of change over time were calculated in 834 blood leukocyte samples collected from 442 participants free of cancer at blood draw. About 3–5 years before cancer onset or death, Δage was associated with cancer risks in a dose-responsive manner (P = 0.02 and a one-year increase in Δage was associated with cancer incidence (HR: 1.06, 95% CI: 1.02–1.10 and mortality (HR: 1.17, 95% CI: 1.07–1.28. Participants with smaller Δage and decelerated epigenetic aging over time had the lowest risks of cancer incidence (P = 0.003 and mortality (P = 0.02. Δage was associated with cancer incidence in a ‘J-shaped’ manner for subjects examined pre-2003, and with cancer mortality in a time-varying manner. We conclude that blood epigenetic age may mirror epigenetic abnormalities related to cancer development, potentially serving as a minimally invasive biomarker for cancer early detection.

  15. 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

  16. 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.

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

    OpenAIRE

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

    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 popu...

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  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. Development of a software for predicting the effects of nuclear and radiological terrorism events in city areas

    International Nuclear Information System (INIS)

    Objective: To develop a new software system that can directly display the predicted results on an electronic map, in order to get a directly perceived understanding of the affected areas of nuclear and radiological terrorism events in city areas. Methods: Three scenarios of events including spreading radioactive materials, dirty bomb attack, and explosion or arson attacks on the radiation facilities were assumed. Gaussian diffusion model was employed to predict the spread and deposition of radioactive pollutants, and both the internal and external doses were estimated for the representative person by using the corresponding dose conversion factors. Through integration of the computing system and Mapinfo geographic information system (GIS), the predicted results were visually displayed on the electronic maps of a city. Results: The new software system could visually display the predicted results on the electronic map of a city, and the predicted results were consistent with those calculated by the similar software Hotspot®. The deviation between this system and Hotspot was less than 0.2 km for predicted isoplethic curves of dose rate downwind. Conclusions: The newly developed software system is of the practical value in predicting the effects of nuclear and radiological terrorism events in city areas. (authors)

  5. 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.

  6. Total Quality Maintenance (TQMain) A predictive and proactive maintenance concept for software

    OpenAIRE

    Williamsson, Ia

    2006-01-01

    This thesis describes an investigation of the possibility to apply a maintenance concept originally developed for the industry, on software maintenance. Today a large amount of software development models exist but not many of them treat maintenance as a part of the software life cycle. In most cases maintenance is depicted as an activity towards the end of the software life cycle. The high cost ascribed to software maintenance motivates for improvements. The maintenance concept TQMain propos...

  7. 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.

  8. Preoperative thrombocytosis predicts prognosis in stage II colorectal cancer patients

    Science.gov (United States)

    Lee, Yong Sun; Suh, Kwang Wook

    2016-01-01

    Purpose Thrombocytosis is known to be a poor prognostic factor in several types of solid tumors. The prognostic role of preoperative thrombocytosis in colorectal cancer remains limited. The aim of this study is to investigate the prognostic role of preoperative thrombocytosis in stage II colorectal cancer. Methods Two hundred eighty-four patients with stage II colorectal cancer who underwent surgical resection between December 2003 and December 2009 were retrospectively reviewed. Thrombocytosis was defined as platelet > 450 × 109/L. We compared patients with thrombocytosis and those without thrombocytosis in terms of survival. Results The 5-year disease-free survival (DFS) rates were lower in patients with thrombocytosis compared to those without thrombocytosis in stage II colorectal cancer (73.3% vs. 89.6%, P = 0.021). Cox multivariate analysis demonstrated that thrombocytosis (hazard ratio, 2.945; 95% confidence interval, 1.127–7.697; P = 0.028) was independently associated with DFS in patients with stage II colorectal cancer. Conclusion This study showed that thrombocytosis is a prognostic factor predicting DFS in stage II colorectal cancer patients. PMID:27274508

  9. Prediction of survival in thyroid cancer using data mining technique.

    Science.gov (United States)

    Jajroudi, M; Baniasadi, T; Kamkar, L; Arbabi, F; Sanei, M; Ahmadzade, M

    2014-08-01

    Cancer is the second leading cause of death after cardiovascular diseases in the world. Health professionals are seeking ways for suitable treatment and quality of care in these groups of patients. Survival prediction is important for both physicians and patients in order to choose the best way of management. Artificial Neural Network (ANN) is one of the most efficient data mining methods. This technique is able to evaluate the relationship between different variables spontaneously without any prevalent data. In our study ANN and Logistic Regression were used to predict survival in thyroid cancer and compare these results. SEER (Surveillance, Epidemiology and End Result) data were got from SEER site1. Effective features in thyroid cancer have been selected based on supervision by radiation oncologists and evidence. After data pruning 7706 samples were studied with 16 attributes. Multi Layer Prediction (MLP) was used as the chosen neural network and survival was predicted for 1-, 3- and 5-years. Accuracy, sensitivity and specificity were parameters to evaluate the model. The results of MLP and Logistic Regression models for one year are defined as for 1-year (92.9%, 92.8, 93%), (81.2%, 88.9%, 72.5%), for 3-year as (85.1%, 87.8%, 82.8%), (88.6%, 90.2%, 87.2%) and for 5-year as (86.8%, 96%, 74.3%), (90.7%, 95.9%, 83.7) respectively. According to our results ANN could efficiently represent a suitable method of survival prediction in thyroid cancer patients and the results were comparable with statistical models. PMID:24206207

  10. 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

  11. Predicting sentinel lymph node metastasis in breast cancer with lymphoscintigraphy

    International Nuclear Information System (INIS)

    Lymphoscintigraphy is an effective method for detecting sentinel lymph nodes (SLNs). However, the rate and degree of SLN detection is not uniform. We quantified SLNs detected with lymphoscintigraphy, and investigated correlations with factors that may influence detection. We then attempted to predict SLN metastasis from lymph node counts, comparing the predictions to subsequent biopsy results. We assessed lymph node counts in 100 breast cancer patients in whom a single SLN was detected with a fixed lymphoscintigraphy procedure. We examined correlations between the counts and factors known to influence lymphoscintigraphic SLN detection (age, body mass index, tumor size, and presence or absence of metastasis), and determined reference values (lymph node counts of 10.0, 19.4 and 53.0) which were used to predict SLN metastasis in 100 subsequent patients. The predictions were then compared with the SLN biopsy findings. SLN counts correlated strongly with the presence or absence of metastasis, with metastasis-positive lymph nodes showing significantly lower counts than negative nodes (p<0.001). Prediction of SLN metastasis achieved a 100% positive predictive value at a reference value of 10.0, and a 100% negative predictive value at a reference value of 53.0. At a reference value of 19.4, the sensitivity, specificity, and diagnostic accuracy were 77.8, 73.2, and 74.0%, respectively. The SLN counts detected with lymphoscintigraphy were significantly lower in metastasis-positive lymph nodes than in metastasis-negative lymph nodes. This suggests that prediction of SLN metastasis in breast cancer is possible using lymphoscintigraphy. (author)

  12. Predicting Chernobyl childhood thyroid cancers from incoming data

    International Nuclear Information System (INIS)

    Data on childhood thyroid cancers contracted in Belarus, the Ukraine and Russia's Bryansk and Kaluga regions have been analysed under the working hypothesis that the excess cancers have been caused by iodine-131 from Chernobyl fallout. It is postulated that the variation in latency period between different individuals is most likely to conform to either a normal or a normal logarithmic distribution. Optimal values of the mean and geometric mean latency period, together with their associated standard deviations, have been found using Belarus data. Both resulting distributions predict significant incidence of childhood thyroid cancer much earlier than ten years after the accident, a length of time widely understood in the past to be the approximate minimum for the development of a radiation-induced, solid tumour. The two distributions incorporating these optimal values have been tested against independent data from the Ukraine and Russian and each distribution has passed the statistical tests to date. Predictions are given for the annual incidence of childhood thyroid cancer in each country and for the total number of excess cases over all years. Tolerances are assigned to the latter figure. (Author)

  13. Performance assessment of the commercial CFD software for the prediction of the PWR internal flow - Corrected version

    International Nuclear Information System (INIS)

    As the computer hardware technology develops the license applicants for nuclear power plant use the commercial CFD software with the aim of reducing the excessive conservatism associated with using simplified and conservative analysis tools. Even if some of CFD software developers and its users think that a state of the art CFD software can be used to solve reasonably at least the single-phase nuclear reactor safety problems there is still the limitations and the uncertainties in the calculation result. From a regulatory perspective, Korea Institute of Nuclear Safety (KINS) has been presently conducting the performance assessment of the commercial CFD software for the nuclear reactor safety problems. In this study, in order to examine the prediction performance of the commercial CFD software with the porous model in the analysis of the scale-down APR+ (Advanced Power Reactor Plus) internal flow, 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 some-what different. Although there was a limitation in estimating the prediction performance of the commercial CFD software due to the limited number of the measured data, CFXR.14 showed the more reasonable predicted results in comparison with FLUENT R.14. Meanwhile, due to the difference of 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 the massive parallel computation. (authors)

  14. 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

  15. [Auto-analysis of immunohistochemical findings for breast cancer using specified software and virtual microscopy].

    Science.gov (United States)

    Tanaka, Miho; Kuraoka, Kazuya; Sakane, Junichi; Kodama, Yoko; Nishimura, Toshinao; Tanaka, Masazumi; Tatsushima, Junji; Saitou, Akihisa; Taniyama, Kiyomi

    2012-03-01

    Currently, the therapeutic strategy for a breast cancer patient is designed according to their histopathological, immunohistochemical and molecular findings. These findings are obtained through the collected efforts of many individual pathologists or medical technologists (MTs) and are, thus, limited by intra-observer error and potentially subjective decision making. Twenty five breast cancer specimens collected between November 2009 and February 2010 were examined for immunohistochemical expressions of estrogen receptor (ER), progesterone receptor (PgR), HER2, Ki-67, Topoisomerase II alpha (Topo IIalpha). Fifty one cancer specimens collected November 2009 and June 2010 were examined for human epidermal growth factor 2 (HER2). Immunohistochemical staining was performed using auto-stainers (Ventana) and the results were stored digitally after examination by virtual microscopy (Hamamatsu Photonics). Data analysis was performed with the Genie/Aperio software package on a desk-top computer. For all the antibodies used expect for HER2, concordant results were obtained in 100% of 24 ER positive cases. Ki-67 index (r=0.96) and Topo IIalpha index (r=0.95) also showed a significant correlation (p<0.001). For HER2, all four specimens with Hercep-score 2 by ocular observation but auto-analysis score 1 revealed no HER2 gene amplification. Well-organized auto-analysis is more likely to result in an objective observation and to provide a means by which to standardize the methods for immunohistochemical detection of breast cancer. PMID:22568082

  16. Strategy for the Development of a DNB Local Predictive Approach Based on Neptune CFD Software

    International Nuclear Information System (INIS)

    The NEPTUNE project constitutes the thermal-hydraulics part of a long-term joint development program for the next generation of nuclear reactor simulation tools. This project is being carried through by EDF (Electricite de France) and CEA (Commissariat a l'Energie Atomique), with the co-sponsorship of IRSN (Institut de Radioprotection et de Surete Nucleaire) and AREVA NP. NEPTUNE is a multi-phase flow software platform that includes advanced physical models and numerical methods for each simulation scale (CFD, component, system). NEPTUNE also provides new multi-scale and multi-disciplinary coupling functionalities. This new generation of two-phase flow simulation tools aims at meeting major industrial needs. DNB (Departure from Nucleate Boiling) prediction in PWRs is one of the high priority needs, and this paper focuses on its anticipated improvement by means of a so-called 'Local Predictive Approach' using the NEPTUNE CFD code. We firstly present the ambitious 'Local Predictive Approach' anticipated for a better prediction of DNB, i.e. an approach that intends to result in CHF correlations based on relevant local parameters as provided by the CFD modeling. The associated requirements for the two-phase flow modeling are underlined as well as those for the good level of performance of the NEPTUNE CFD code; hence, the code validation strategy based on different experimental data base types (including separated effect and integral-type tests data) is depicted. Secondly, we present comparisons between low pressure adiabatic bubbly flow experimental data obtained on the DEDALE experiment and the associated numerical simulation results. This study anew shows the high potential of NEPTUNE CFD code, even if, with respect to the aforementioned DNB-related aim, there is still a need for some modeling improvements involving new validation data obtained in thermal-hydraulics conditions representative of PWR ones. Finally, we deal with one of these new experimental data needs

  17. SeaTrack: Ground station orbit prediction and planning software for sea-viewing satellites

    Science.gov (United States)

    Lambert, Kenneth S.; Gregg, Watson W.; Hoisington, Charles M.; Patt, Frederick S.

    1993-01-01

    An orbit prediction software package (Sea Track) was designed to assist High Resolution Picture Transmission (HRPT) stations in the acquisition of direct broadcast data from sea-viewing spacecraft. Such spacecraft will be common in the near future, with the launch of the Sea viewing Wide Field-of-view Sensor (SeaWiFS) in 1994, along with the continued Advanced Very High Resolution Radiometer (AVHRR) series on NOAA platforms. The Brouwer-Lyddane model was chosen for orbit prediction because it meets the needs of HRPT tracking accuracies, provided orbital elements can be obtained frequently (up to within 1 week). Sea Track requires elements from the U.S. Space Command (NORAD Two-Line Elements) for the satellite's initial position. Updated Two-Line Elements are routinely available from many electronic sources (some are listed in the Appendix). Sea Track is a menu-driven program that allows users to alter input and output formats. The propagation period is entered by a start date and end date with times in either Greenwich Mean Time (GMT) or local time. Antenna pointing information is provided in tabular form and includes azimuth/elevation pointing angles, sub-satellite longitude/latitude, acquisition of signal (AOS), loss of signal (LOS), pass orbit number, and other pertinent pointing information. One version of Sea Track (non-graphical) allows operation under DOS (for IBM-compatible personal computers) and UNIX (for Sun and Silicon Graphics workstations). A second, graphical, version displays orbit tracks, and azimuth-elevation for IBM-compatible PC's, but requires a VGA card and Microsoft FORTRAN.

  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 and Validation of a Prediction Model to Estimate Individual Risk of Pancreatic Cancer

    OpenAIRE

    Yu, Ami; Woo, Sang Myung; Joo, Jungnam; Yang, Hye-Ryung; Lee, Woo Jin; Park, Sang-Jae; Nam, Byung-Ho

    2016-01-01

    Introduction There is no reliable screening tool to identify people with high risk of developing pancreatic cancer even though pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea. The goal of this study was to develop an individualized risk prediction model that can be used to screen for asymptomatic pancreatic cancer in Korean men and women. Materials and Methods Gender-specific risk prediction models for pancreatic cancer were developed using the Cox propor...

  20. The Predictive Accuracy of PREDICT: A Personalized Decision-Making Tool for Southeast Asian Women With Breast Cancer

    OpenAIRE

    Wong, Hoong-Seam; Subramaniam, ShriDevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M.; Hartman, Mikael; Bhoo-Pathy, Nirmala

    2015-01-01

    Abstract Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medica...

  1. A prediction model for colon cancer surveillance data.

    Science.gov (United States)

    Good, Norm M; Suresh, Krithika; Young, Graeme P; Lockett, Trevor J; Macrae, Finlay A; Taylor, Jeremy M G

    2015-08-15

    Dynamic prediction models make use of patient-specific longitudinal data to update individualized survival probability predictions based on current and past information. Colonoscopy (COL) and fecal occult blood test (FOBT) results were collected from two Australian surveillance studies on individuals characterized as high-risk based on a personal or family history of colorectal cancer. Motivated by a Poisson process, this paper proposes a generalized nonlinear model with a complementary log-log link as a dynamic prediction tool that produces individualized probabilities for the risk of developing advanced adenoma or colorectal cancer (AAC). This model allows predicted risk to depend on a patient's baseline characteristics and time-dependent covariates. Information on the dates and results of COLs and FOBTs were incorporated using time-dependent covariates that contributed to patient risk of AAC for a specified period following the test result. These covariates serve to update a person's risk as additional COL, and FOBT test information becomes available. Model selection was conducted systematically through the comparison of Akaike information criterion. Goodness-of-fit was assessed with the use of calibration plots to compare the predicted probability of event occurrence with the proportion of events observed. Abnormal COL results were found to significantly increase risk of AAC for 1 year following the test. Positive FOBTs were found to significantly increase the risk of AAC for 3 months following the result. The covariates that incorporated the updated test results were of greater significance and had a larger effect on risk than the baseline variables. PMID:25851283

  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. 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

  4. CYC1 Predicts Poor Prognosis in Patients with Breast Cancer

    Science.gov (United States)

    Han, Yingyan; Sun, Shujuan; Zhao, Meisong; Zhang, Zeyu; Gong, Song; Gao, Peipei; Liu, Jia; Zhou, Jianfeng; Ma, Ding; Gao, Qinglei; Wu, Peng

    2016-01-01

    Cytochrome c-1 (CYC1) is an important subunit of mitochondrial complex III. However, its role in tumor progression is unclear. We found that CYC1 was upregulated in breast tumor tissues, especially in tissues with lymph node metastasis. And higher expression of CYC1 correlates with poor prognosis in breast cancer patients using online databases and tools. Then we confirmed that CYC1 contributed to metastasis and proliferation in two highly metastatic human breast cancer cell lines. Digging into the biological function of CYC1, we found the activity of mitochondrial complex III decreased due to silencing CYC1. Then the ratio of AMP to ATP increased and AMPK was activated. Analyzing units of other mitochondrial complexes, we did not find knockdown of CYC1 expression reduced expression of any other unit of OXPHOS. We concluded that CYC1 promoted tumor metastasis via suppressing activation of AMPK and contributed to tumor growth via facilitating production of ATP. Our results indicated that CYC1 plays crucial roles in breast cancer progression and might be a predictive factor assisting future patient diagnosis.

  5. 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.

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

    International Nuclear Information System (INIS)

    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. The interaction blockade of PD-1 and PD-L1 demonstrated promising activity and antitumor efficacy 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. Therefore, 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 specificity 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

  7. Computer-assisted immunohistochemical analysis of cervical cancer biomarkers using low-cost and simple software.

    Science.gov (United States)

    Hammes, Luciano Serpa; Korte, Jeffrey E; Tekmal, Rajeshwar Rao; Naud, Paulo; Edelweiss, Maria Isabel; Valente, Philip T; Longatto-Filho, Adhemar; Kirma, Nameer; Cunha-Filho, João Sabino

    2007-12-01

    The study of biomarkers by immunohistochemistry (IHC) for cervical cancer and intraepithelial lesions is a promising field. However, manual interpretation of IHC and reproducibility of the scoring systems can be highly subjective. In this article, we present a novel and simple computer-assisted IHC interpretation method based on cyan-magenta-yellow-black (CMYK) color format, for tissues with diaminobenzidine cytoplasmatic staining counterstained with methyl green. This novel method is more easily interpreted than previous computer-assisted methods based on red-green-blue (RGB) color format and presents a strong correlation with the manual H-score. It is simple, objective, and requires only low-cost software and minimal computer skills. Briefly, a total of 67 microscopic images of cervical carcinoma, normal cervix, and negative controls were analyzed in Corel Photo Paint X3 software in CMYK and RGB color format, and compared with manual H-score IHC assessments. The clearest and best positive correlation with the H-score was obtained using the image in CMYK color format and crude values of magenta color (Spearman correlation coefficient=0.84; agreement of 93.33%, PCMYK format, select the area of interest for analysis, and open the color histogram tool to visualize the magenta value. PMID:18091391

  8. Performance Assessment of the Commercial CFD Software for the Prediction of the Reactor Internal Flow

    Science.gov (United States)

    Lee, Gong Hee; Bang, Young Seok; Woo, Sweng Woong; Kim, Do Hyeong; Kang, Min Ku

    2014-06-01

    As the computer hardware technology develops the license applicants for nuclear power plant use the commercial CFD software with the aim of reducing the excessive conservatism associated with using simplified and conservative analysis tools. Even if some of CFD software developer and its user think that a state of the art CFD software can be used to solve reasonably at least the single-phase nuclear reactor problems, there is still limitation and uncertainty in the calculation result. From a regulatory perspective, Korea Institute of Nuclear Safety (KINS) is presently conducting the performance assessment of the commercial CFD software for nuclear reactor problems. In this study, in order to examine the validity of the results of 1/5 scaled APR+ (Advanced Power Reactor Plus) flow distribution tests and the applicability of CFD in the analysis of reactor internal flow, the simulation was conducted with the two commercial CFD software (ANSYS CFX V.14 and FLUENT V.14) among the numerous commercial CFD software and was compared with the measurement. In addition, what needs to be improved in CFD for the accurate simulation of reactor core inlet flow was discussed.

  9. Prediction of Cancer Incidence and Mortality in Korea, 2016

    OpenAIRE

    Jung, Kyu-Won; Won, Young-Joo; Oh, Chang-Mo; Kong, Hyun-Joo; Cho, Hyunsoon; Lee, Jong-Keun; Lee, Duk Hyoung; Lee, Kang Hyun

    2016-01-01

    Purpose: To estimate of Korea’s current cancer burden, this study aimed to report on projected cancer incidence and mortality rates for the year 2016. Materials and Methods: Cancer incidence data from 1999 to 2013 were obtained from the Korea National Cancer Incidence Database, and cancer mortality data from 1993 to 2014 were acquired from Statistics Korea. Cancer incidence in 2016 was projected by fitting a linear regression model to observed age-specific cancer incidence rates against obser...

  10. 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...

  11. 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.

  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. 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.

  14. 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

  15. 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

  16. 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

  17. Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types

    Directory of Open Access Journals (Sweden)

    George C. Tseng

    2009-01-01

    Full Text Available Microarray technology has been widely applied to the analysis of many malignancies, however, integrative analyses across multiple studies are rarely investigated. In this study we performed a meta-analysis on the expression profiles of four published studies analyzing organ donor, benign tissues adjacent to tumor and tumor tissues from liver, prostate, lung and bladder samples. We identified 99 distinct multi-cancer biomarkers in the comparison of all three tissues in liver and prostate and 44 in the comparison of normal versus tumor in liver, prostate and lung. The bladder samples appeared to have a different list of biomarkers from the other three cancer types. The identified multi-cancer biomarkers achieved high accuracy similar to using whole genome in the within-cancer-type prediction. They also performed superior than the one using whole genome in inter-cancer-type prediction. To test the validity of the multi-cancer biomarkers, 23 independent prostate cancer samples were evaluated and 96% accuracy was achieved in inter-study prediction from the original prostate, liver and lung cancer data sets respectively. The result suggests that the compact lists of multi-cancer biomarkers are important in cancer development and represent the common signatures of malignancies of multiple cancer types. Pathway analysis revealed important tumorogenesis functional categories.

  18. 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

  19. 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

  20. 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.

  1. Development of a new model to predict indoor daylighting: Integration in CODYRUN software and validation

    Energy Technology Data Exchange (ETDEWEB)

    Fakra, A.H., E-mail: fakra@univ-reunion.f [Physics and Mathematical Engineering Laboratory for Energy and Environment (PIMENT), University of La Reunion, 117 rue du General Ailleret, 97430 Le Tampon (French Overseas Dpt.), Reunion (France); Miranville, F.; Boyer, H.; Guichard, S. [Physics and Mathematical Engineering Laboratory for Energy and Environment (PIMENT), University of La Reunion, 117 rue du General Ailleret, 97430 Le Tampon (French Overseas Dpt.), Reunion (France)

    2011-07-15

    Research highlights: {yields} This study presents a new model capable to simulate indoor daylighting. {yields} The model was introduced in research software called CODYRUN. {yields} The validation of the code was realized from a lot of tests cases. -- Abstract: Many models exist in the scientific literature for determining indoor daylighting values. They are classified in three categories: numerical, simplified and empirical models. Nevertheless, each of these categories of models are not convenient for every application. Indeed, the numerical model requires high calculation time; conditions of use of the simplified models are limited, and experimental models need not only important financial resources but also a perfect control of experimental devices (e.g. scale model), as well as climatic characteristics of the location (e.g. in situ experiment). In this article, a new model based on a combination of multiple simplified models is established. The objective is to improve this category of model. The originality of our paper relies on the coupling of several simplified models of indoor daylighting calculations. The accuracy of the simulation code, introduced into CODYRUN software to simulate correctly indoor illuminance, is then verified. Besides, the software consists of a numerical building simulation code, developed in the Physics and Mathematical Engineering Laboratory for Energy and Environment (PIMENT) at the University of Reunion. Initially dedicated to the thermal, airflow and hydrous phenomena in the buildings, the software has been completed for the calculation of indoor daylighting. New models and algorithms - which rely on a semi-detailed approach - will be presented in this paper. In order to validate the accuracy of the integrated models, many test cases have been considered as analytical, inter-software comparisons and experimental comparisons. In order to prove the accuracy of the new model - which can properly simulate the illuminance - a

  2. 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

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

    Science.gov (United States)

    2011-01-01

    Purpose 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. Methods and Materials 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. Results 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. Conclusions 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. PMID:21702952

  4. 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

    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 of...

  5. A Prediction Packetizing Scheme for Reducing Channel Traffic in Transaction-Level Hardware/Software Co-Emulation

    CERN Document Server

    Lee, Jae-Gon; Ahn, Ki-Yong; Lee, Sang-Heon; Kyung, Chong-Min

    2011-01-01

    This paper presents a scheme for efficient channel usage between simulator and accelerator where the accelerator models some RTL sub-blocks in the accelerator-based hardware/software co-simulation while the simulator runs transaction-level model of the remaining part of the whole chip being verified. With conventional simulation accelerator, evaluations of simulator and accelerator alternate at every valid simulation time, which results in poor simulation performance due to startup overhead of simulator-accelerator channel access. The startup overhead can be reduced by merging multiple transactions on the channel into a single burst traffic. We propose a predictive packetizing scheme for reducing channel traffic by merging as many transactions into a burst traffic as possible based on 'prediction and rollback.' Under ideal condition with 100% prediction accuracy, the proposed method shows a performance gain of 1500% compared to the conventional one.

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

    OpenAIRE

    Alphy Rose-James; TT, Sreelekha

    2012-01-01

    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...

  7. A genome-wide systematic analysis reveals different and predictive proliferation expression signatures of cancerous vs. non-cancerous cells.

    Directory of Open Access Journals (Sweden)

    Yedael Y Waldman

    Full Text Available Understanding cell proliferation mechanisms has been a long-lasting goal of the scientific community and specifically of cancer researchers. Previous genome-scale studies of cancer proliferation determinants have mainly relied on knockdown screens aimed to gauge their effects on cancer growth. This powerful approach has several limitations such as off-target effects, partial knockdown, and masking effects due to functional backups. Here we employ a complementary approach and assign each gene a cancer Proliferation Index (cPI that quantifies the association between its expression levels and growth rate measurements across 60 cancer cell lines. Reassuringly, genes found essential in cancer gene knockdown screens exhibit significant positive cPI values, while tumor suppressors exhibit significant negative cPI values. Cell cycle, DNA replication, splicing and protein production related processes are positively associated with cancer proliferation, while cellular migration is negatively associated with it - in accordance with the well known "go or grow" dichotomy. A parallel analysis of genes' non-cancerous proliferation indices (nPI across 224 lymphoblastoid cell lines reveals surprisingly marked differences between cancerous and non-cancerous proliferation. These differences highlight genes in the translation and spliceosome machineries as selective cancer proliferation-associated proteins. A cross species comparison reveals that cancer proliferation resembles that of microorganisms while non-cancerous proliferation does not. Furthermore, combining cancerous and non-cancerous proliferation signatures leads to enhanced prediction of patient outcome and gene essentiality in cancer. Overall, these results point to an inherent difference between cancerous and non-cancerous proliferation determinants, whose understanding may contribute to the future development of novel cancer-specific anti-proliferative drugs.

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. Applications of Machine learning in Prediction of Breast Cancer Incidence and Mortality

    International Nuclear Information System (INIS)

    Breast cancer is one of the leading causes of cancer deaths for the female population in both developed and developing countries. In this work we have used the baseline descriptive data about the incidence (new cancer cases) of in situ breast cancer among Wisconsin females. The documented data were from the most recent 12-years period for which data are available. Wiscons in cancer incidence and mortality (deaths due to cancer) that occurred were also considered in this work. Artificial Neural network (ANN) have been successfully applied to problems in the prediction of the number of new cancer cases and mortality. Using artificial intelligence (AI) in this study, the numbers of new cancer cases and mortality that may occur are predicted.

  13. GenoMatrix: A Software Package for Pedigree-Based and Genomic Prediction Analyses on Complex Traits.

    Science.gov (United States)

    Nazarian, Alireza; Gezan, Salvador Alejandro

    2016-07-01

    Genomic and pedigree-based best linear unbiased prediction methodologies (G-BLUP and P-BLUP) have proven themselves efficient for partitioning the phenotypic variance of complex traits into its components, estimating the individuals' genetic merits, and predicting unobserved (or yet-to-be observed) phenotypes in many species and fields of study. The GenoMatrix software, presented here, is a user-friendly package to facilitate the process of using genome-wide marker data and parentage information for G-BLUP and P-BLUP analyses on complex traits. It provides users with a collection of applications which help them on a set of tasks from performing quality control on data to constructing and manipulating the genomic and pedigree-based relationship matrices and obtaining their inverses. Such matrices will be then used in downstream analyses by other statistical packages. The package also enables users to obtain predicted values for unobserved individuals based on the genetic values of observed related individuals. GenoMatrix is available to the research community as a Windows 64bit executable and can be downloaded free of charge at: http://compbio.ufl.edu/software/genomatrix/. PMID:27025440

  14. 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.

  15. Identification of Prognostic Genes for Recurrent Risk Prediction in Triple Negative Breast Cancer Patients in Taiwan

    OpenAIRE

    Chen, Lee H.; Kuo, Wen-Hung; Tsai, Mong-Hsun; Chen, Pei-Chun; Hsiao, Chuhsing K.; Chuang, Eric Y.; Chang, Li-Yun; Hsieh, Fon-Jou; Lai, Liang-Chuan; Chang, King-Jen

    2011-01-01

    Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients. Whole genome expression profiling of breast cancers from 185 patients in Taiwan from 1995 to 2008 was performed, and the results were co...

  16. 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...

  17. 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...

  18. 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.

  19. Influence of Memory Hierarchies on Predictability for Time Constrained Embedded Software

    CERN Document Server

    Wehmeyer, Lars

    2011-01-01

    Safety-critical embedded systems having to meet real-time constraints are expected to be highly predictable in order to guarantee at design time that certain timing deadlines will always be met. This requirement usually prevents designers from utilizing caches due to their highly dynamic, thus hardly predictable behavior. The integration of scratchpad memories represents an alternative approach which allows the system to benefit from a performance gain comparable to that of caches while at the same time maintaining predictability. In this work, we compare the impact of scratchpad memories and caches on worst case execution time (WCET) analysis results. We show that caches, despite requiring complex techniques, can have a negative impact on the predicted WCET, while the estimated WCET for scratchpad memories scales with the achieved Performance gain at no extra analysis cost.

  20. 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)...

  1. 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.

  2. A Boolean-based systems biology approach to predict novel genes associated with cancer: Application to colorectal cancer

    Directory of Open Access Journals (Sweden)

    Reverter Antonio

    2011-02-01

    Full Text Available Abstract Background Cancer has remarkable complexity at the molecular level, with multiple genes, proteins, pathways and regulatory interconnections being affected. We introduce a systems biology approach to study cancer that formally integrates the available genetic, transcriptomic, epigenetic and molecular knowledge on cancer biology and, as a proof of concept, we apply it to colorectal cancer. Results We first classified all the genes in the human genome into cancer-associated and non-cancer-associated genes based on extensive literature mining. We then selected a set of functional attributes proven to be highly relevant to cancer biology that includes protein kinases, secreted proteins, transcription factors, post-translational modifications of proteins, DNA methylation and tissue specificity. These cancer-associated genes were used to extract 'common cancer fingerprints' through these molecular attributes, and a Boolean logic was implemented in such a way that both the expression data and functional attributes could be rationally integrated, allowing for the generation of a guilt-by-association algorithm to identify novel cancer-associated genes. Finally, these candidate genes are interlaced with the known cancer-related genes in a network analysis aimed at identifying highly conserved gene interactions that impact cancer outcome. We demonstrate the effectiveness of this approach using colorectal cancer as a test case and identify several novel candidate genes that are classified according to their functional attributes. These genes include the following: 1 secreted proteins as potential biomarkers for the early detection of colorectal cancer (FXYD1, GUCA2B, REG3A; 2 kinases as potential drug candidates to prevent tumor growth (CDC42BPB, EPHB3, TRPM6; and 3 potential oncogenic transcription factors (CDK8, MEF2C, ZIC2. Conclusion We argue that this is a holistic approach that faithfully mimics cancer characteristics, efficiently predicts

  3. Predicting response to chemotherapy with early-stage lung cancer.

    Science.gov (United States)

    Rosell, Rafael; Taron, Miquel; Massuti, Bartomeu; Mederos, Nuria; Magri, Ignacio; Santarpia, Mariacarmela; Sanchez, Jose Miguel

    2011-01-01

    A recent meta-analysis of 11,107 patients with non-small cell lung cancer who had undergone surgical resection showed that the 5-year survival benefit of adjuvant chemotherapy was 4%, and that of adjuvant chemoradiotherapy was 5%. Two trials have shown a trend toward improved survival with adjuvant paclitaxel plus carboplatin. However, the benefit of adjuvant treatment remains suboptimal. We must distinguish between patients who will not relapse-and who can thus be spared adjuvant treatment-and those who will-for whom adjuvant treatment must be personalized. Several gene expression signatures, generally containing nonoverlapping genes, provide similar predictive information on clinical outcome, and a model combining several signatures did not perform better than did each of the signatures separately. The invasiveness gene signature, containing 186 genes, includes genes involved in the nuclear factor κB pathway, the RAS-mitogen-activated protein kinase pathway, and epigenetic control of gene expression. A 15-gene signature has identified JBR.10 patients who are more sensitive to adjuvant chemotherapy. PMID:21263267

  4. 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...

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Predictive values of symptoms in relation to cancer diagnosis

    DEFF Research Database (Denmark)

    Krasnik, Ivan; Andersen, John Sahl

    Background/significance: Poorer prognosis for cancer patients in Denmark than in comparable countries has been shown and contributed to the introduction of accelerated diagnostic trajectories for patients suspicious for cancer in 2008. For all types of cancers the National Board of Health developed...... cancer and lung cancer in a general practice setting. Methods: The literature search was done in PubMed. The quality of each paper was assessed using Newcastle-Ottawa Quality Assessment Scale. Results: 14 original studies were identified. Colon cancer: Concerning ”Rectal bleeding” the PPV is high for...... literature. Lung cancer: For “Haemoptysis” a high PPV for elderly patients was found (8,4%-20,4%). PPV of “Cough”, ”Pain in the thorax”, ”Dyspnoea” and ”General symptoms” are small (0,4-1,1%).. Conclusion: A few of the “alarm symptoms” show high PPVs. For many symptoms the PPV is not known. To improve...

  10. 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

  11. Expression profiling to predict outcome in breast cancer: the influence of sample selection

    International Nuclear Information System (INIS)

    Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-α status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-α-positive and estrogen receptor-α-negative tumors

  12. A germline predictive signature of response to platinum chemotherapy in esophageal cancer.

    Science.gov (United States)

    Rumiato, Enrica; Boldrin, Elisa; Malacrida, Sandro; Battaglia, Giorgio; Bocus, Paolo; Castoro, Carlo; Cagol, Matteo; Chiarion-Sileni, Vanna; Ruol, Alberto; Amadori, Alberto; Saggioro, Daniela

    2016-05-01

    Platinum-based neoadjuvant therapy is the standard treatment for esophageal cancer (EC). At present, no reliable response markers exist, and patient therapeutic outcome is variable and very often unpredictable. The aim of this study was to understand the contribution of host constitutive DNA polymorphisms in discriminating between responder and nonresponder patients. DNA collected from 120 EC patients treated with platinum-based neoadjuvant chemotherapy was analyzed using drug metabolism enzymes and transporters (DMET) array platform that interrogates polymorphisms in 225 genes of drug metabolism and disposition. Four gene variants of DNA repair machinery, 2 in ERCC1 (rs11615; rs3212986), and 2 in XPD (rs1799793; rs13181) were also studied. Association analysis was performed with pTest software and corrected by permutation test. Predictive models of response were created using the receiver-operating characteristics curve approach and adjusted by the bootstrap procedure. Sixteen single nucleotide polymorphisms (SNPs) of the DMET array resulted significantly associated with either good or poor response; no association was found for the 4 variants mapping in DNA repair genes. The predictive power of 5 DMET SNPs mapping in ABCC2, ABCC3, CYP2A6, PPARG, and SLC7A8 genes was greater than that of clinical factors alone (area under the curve [AUC] = 0.74 vs 0.62). Interestingly, their combination with the clinical variables significantly increased the predictivity of the model (AUC = 0.78 vs 0.62, P = 0.0016). In conclusion, we identified a genetic signature of response to platinum-based neoadjuvant chemotherapy in EC patients. Our results also disclose the potential benefit of combining genetic and clinical variables for personalized EC management. PMID:26772957

  13. Matched-pair analysis and dosimetric variations of two types of software for interstitial permanent brachytherapy for prostate cancer

    International Nuclear Information System (INIS)

    The purpose of this study was to determine whether identical dosimetric results could be achieved using different planning software for permanent interstitial brachytherapy for prostate cancer. Data from 492 patients treated with brachytherapy were used for matched-pair analysis. Interplant and Variseed were used as software for ultrasound-based treatment planning. Institution, neoadjuvant hormonal therapy, prostate volume, and source strength were used for factors to match the 2 groups. The study population comprised of 126 patients with treatment planning using Interplant software and 127 matched patients using Variseed software. Dosimetric results were compared between the 2 groups. The Variseed group showed significantly higher values for dose covering 90% of prostate volume (pD90), prostate volume covered by 150% of prescription dose (pV150), and dose covering 30% of the urethra (uD30) compared with the Interplant group. Our results showed that use of different software could lead to different dosimetric results, which might affect the clinical outcomes.

  14. Novel Management of Oral Cancer: A Paradigm of Predictive Oncology

    OpenAIRE

    Sudbø, Jon

    2004-01-01

    The rationale for molecular-targeted prevention of oral cancer is strong. Oral cancer is a major global threat to public health with 300,000 new cases diagnosed worldwide on an annual basis. Notably, the great morbidity and mortality rates of this devastating disease have not improved in decades. Oral cancer development is a tobacco-related multistep and multifocal process involving field carcinogenesis and intraepithelial clonal spread. Biomarkers of genomic instability, such as aneuploidy a...

  15. 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

  16. 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...

  17. 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...

  18. 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...

  19. Development and Validation of a Prediction Model to Estimate Individual Risk of Pancreatic Cancer.

    Directory of Open Access Journals (Sweden)

    Ami Yu

    Full Text Available There is no reliable screening tool to identify people with high risk of developing pancreatic cancer even though pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea. The goal of this study was to develop an individualized risk prediction model that can be used to screen for asymptomatic pancreatic cancer in Korean men and women.Gender-specific risk prediction models for pancreatic cancer were developed using the Cox proportional hazards model based on an 8-year follow-up of a cohort study of 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996-1997. The performance of the models was evaluated with respect to their discrimination and calibration ability based on the C-statistic and Hosmer-Lemeshow type χ2 statistic.A total of 1,634 (0.13% men and 561 (0.10% women were newly diagnosed with pancreatic cancer. Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the risk prediction model for men. Height, BMI, fasting glucose, urine glucose, smoking, and drinking habit were included in the risk prediction model for women. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. The risk prediction model exhibited good discrimination and calibration ability, and in external validation it had excellent prediction ability.Gender-specific risk prediction models for pancreatic cancer were developed and validated for the first time. The prediction models will be a useful tool for detecting high-risk individuals who may benefit from increased surveillance for pancreatic cancer.

  20. Preoperative distress predicts persistent pain after breast cancer treatment

    DEFF Research Database (Denmark)

    Mejdahl, Mathias Kvist; Mertz, Birgitte Goldschmidt; Bidstrup, Pernille Envold Hansen;

    2015-01-01

    PURPOSE: Persistent pain after breast cancer treatment (PPBCT) affects 25% to 60% of breast cancer survivors and is recognized as a clinical problem, with 10% to 15% reporting moderate to severe pain several years after treatment. Psychological comorbidity is known to influence pain perception, and...

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

    DEFF Research Database (Denmark)

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

    2008-01-01

    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...

  2. 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.

  3. Prediction uncertainty reflects both data input quality and model software sophistication

    Science.gov (United States)

    Syvitski, J.

    2011-12-01

    Recently Syvitski et al. (2011, The Sedimentary Record, v. 9) put forth three concepts related to earth surface modeling: 1) Prediction, as opposed to cataloging, is a major step in the evolution of geoscience; 2) Quantitative modeling provides a framework in which researchers express their predictive ideas in a precise, consistent format; and 3) Models are an encyclopedia of what we know, and often reveal what we cannot yet quantify. This burgeoning field of earth surface science has proportioned itself into three loose fields of endeavor: 1) those that provide data for model initialization and model boundary conditions; 2) those that develop the algorithms, the numerical models and even the middle ware that links models and input data; and 3) the observational specialists that provide test case data that can be used to judge the skill of a model or parts of a model. This 'modern' way of conducting geoscience requires a team approach offering diverse expertise. Uncertainties associated with this workflow are not always understood or appreciated or transparent - leading to poor or over interpretation of model results. To avoid this perception, uncertainties associated with input data must be involved in the model runs, model-run uncertainties must also be expressed independent of the input data uncertainties, and finally model skill testing must be appreciated with full knowledge of uncertainties associated with test case data. While methods have been developed to cope with geo-model skill (ensemble model run averaging or intercomparison; data ingestion schemes to deal with model drift), workflow uncertainty studies are seldom carried out (i.e. is the expense worth the effort?). Prediction uncertainty examples will be presented based on experience from the Community Surface Dynamics Modeling System 'CSDMS' community.

  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. 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

  7. Calibration system of the software for treatment in conformational radiotherapy with a phantom for cases of cervical cancer prostate

    International Nuclear Information System (INIS)

    Full text: The main objective of this work is to design a calibration method for two planning software systems for treatment with conformational radiotherapy to be used for prostate and cervix cancer. For this purpose, a phantom is designed to simulate the prostate and cervix anatomical regions. The phantom is made of acrylic and nylon. These materials have densities similar to soft tissue and bone and they are readily available in Peru at a low cost. The phantom is imaged using a calibrated CT scanner (Siemens - Somatom). The CT images are used for the calculation of the absorbed dose using two software planning systems (WINPLT-3D and KENOS-2D) at the isocenter and at critical points during the process of simulation of the treatment. This calculation is compared to the experimentally measured data in the phantom. Radiation is applied by means of the linear accelerator clinical Varian 2100 C/D, and dosimetry measured using an ionization chamber and thermoluminescent dosimeters (TLD). Preliminary results show that the planned dose and the measured dose differ in less than ± 5.6% with WINPLT-3D and ± 3.3% with KENOS-2D. The measured relative doses at the critical organs to protect originally measured with TLDs at the isocenter point, having results from ± 2.5% to ± 3.5%. These results indicate that the planning software systems are calibrated within the range required by international standards for patients with cancer (ICRU - Report 50). (author)

  8. 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. PMID:25669327

  9. 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...

  10. Higher Levels of GATA3 Predict Better Survival in Women with Breast Cancer

    OpenAIRE

    Yoon, Nam K.; Maresh, Erin L.; Shen, Dejun; Elshimali, Yahya; Apple, Sophia; Horvath, Steve; Mah, Vei; Bose, Shikha; Chia, David; Chang, Helena R.; Goodglick, Lee

    2010-01-01

    The GATA family members are zinc finger transcription factors involved in cell differentiation and proliferation. GATA3 in particular is necessary for mammary gland maturation, and its loss has been implicated in breast cancer development. Our goal was to validate the ability of GATA3 expression to predict survival in breast cancer patients. Protein expression of GATA3 was analyzed on a high density tissue microarray consisting of 242 cases of breast cancer. We associated GATA3 expression wit...

  11. 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.

  12. 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

  13. 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.

  14. 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 ...

  15. 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 ...

  16. 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...

  17. 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...

  18. 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....

  19. 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)

  20. Validation of fluent software for prediction of a flow distribution and pressure gradients in a multi-branch flow header

    International Nuclear Information System (INIS)

    Flow headers are commonly used in nuclear reactors, boilers and heat exchangers to distribute fluid to small branches along the body of the header or to combine flow from the branches along the header. Historically, nuclear safety analysis has been performed using one-dimensional averaged system codes, and as such the distribution headers are cross-sectionally averaged. In this paper, flow distribution and pressure gradients along a multi-branch header have been predicted using the three dimensional computational fluid dynamics software FLUENT and were compared to results obtained from experimental data obtained from literature for single phase conditions. Water inlet flow rate through the header was varied and flow rates in the header branches were measured. The inlet flow rate was found to affect the flow distribution especially at low flow rates and the header pressure gradient especially at high flow rates. The aim of this work is to validate FLUENT software in predicting flow distribution and pressure gradients in single phase flow in such a multi-branch geometry. The effects of flow model, grid density, convergence criteria, flow inlet velocity and header size on the computational results were studied. For the impact of grid density, coarse, fine and very fine meshes were used and the mesh size beyond which no change in solution occurred was adopted. The impact of convergence criteria was studied by tightening the pressure and momentum relaxation factors as well as by decreasing the tolerance. The laminar model provided the best data fit in comparison with the standard and the RNG k-ε models. Vortex formation and flow separation were also studied and compared to the experimentally observed flow behaviour. Agreed with the experiment, largest vortices occur around the first branch pipe of the header. (author)

  1. 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.

  2. 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...

  3. Functional MR imaging for response prediction in rectal cancer treatment

    OpenAIRE

    Intven, M.P.W.

    2015-01-01

    The standard of care treatment for locally advanced rectal cancer is neoadjuvant chemoradiation followed by total mesorectal excision. In recent years, organ-sparing treatments, instead of standard total mesorectal excision, are gradually introduced in the treatment of rectal cancer for patients with good response after neoadjuvant therapy. However, patient selection for organ-sparing treatments is still challenging as no optimal restaging modality is available after neoadjuvant chemoradiatio...

  4. An Iron Regulatory Gene Signature Predicts Outcome in Breast Cancer

    OpenAIRE

    Miller, Lance D.; Coffman, Lan G.; Chou, Jeff W.; Black, Michael A.; Bergh, Jonas; D’Agostino, Ralph; Torti, Suzy V.; Torti, Frank M.

    2011-01-01

    Changes in iron regulation characterize the malignant state. However, the pathways that effect these changes and their specific impact on prognosis remain poorly understood. We capitalized on publicly available microarray datasets comprising 674 breast cancer cases to systematically investigate how expression of genes related to iron metabolism is linked to breast cancer prognosis. Of 61 genes involved in iron regulation, 49% were statistically significantly associated with distant metastasis...

  5. 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)...

  6. 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.

  7. 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.

  8. 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 (pvalue (PPV) and negative predictive value (NPV) also increased from a PPV=0.61 to 0.78 and an NPV=0.65 to 0.75, respectively. This study demonstrates that a multi-feature based BBN can more accurately predict the near-term breast cancer risk than with a single feature.

  9. 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...

  10. 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

  11. 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.

  12. 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.

  13. Cancer predictive value of cytogenetic markers used in occupational health surveillance programs

    DEFF Research Database (Denmark)

    Hagmar, L; Bonassi, S; Strömberg, U; Mikoczy, Z; Lando, C; Hansteen, I L; Montagud, A H; Knudsen, Lisbeth E.; Norppa, H; Reuterwall, C; Tinnerberg, H; Brøgger, A; Forni, A; Högstedt, B; Lambert, B; Mitelman, F; Nordenson, I; Salomaa, S; Skerfving, S

    1998-01-01

    It has not previously been clear whether cytogenetic biomarkers in healthy subjects will predict cancer. Earlier analyses of a Nordic and an Italian cohort indicated predictivity for chromosomal aberrations (CAS) but not for sister chromatid exchanges (SCES). A pooled analysis of the updated...

  14. 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.

  15. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    International Nuclear Information System (INIS)

    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. 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. 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. 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

  16. 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.

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

    OpenAIRE

    Teixidó, Cristina; Karachaliou, Niki; González-Cao, Maria; Morales-Espinosa, Daniela; Rosell, Rafael

    2015-01-01

    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. The interaction blockade of PD-1 and PD-L1 demonstrated promising activity and antitumor efficacy 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 ...

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

    DEFF Research Database (Denmark)

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

    2015-01-01

    biomarker in isolated metastases, in patients with liver metastasis of colorectal cancer (mCRC). Clinicopathological data of 166 patients with mCRC who had surgical resection between 2001 and 2011 were collected retrospectively. All primary tumors have been already resected. The platelet count was evaluated......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...

  19. SOX9 Expression Predicts Relapse of Stage II Colon Cancer Patients

    DEFF Research Database (Denmark)

    Espersen, Maiken Lise Marcker; Linnemann, Dorte; Alamili, Mahdi; Christensen, Ib Jarle; T. Troelsen, Jesper; Høgdall, Estrid

    2016-01-01

    Theaim of this study was to investigate if the protein expression of Sex-determining region y-box 9 (SOX9) in primary tumors could predict relapse of stage II colon cancer patients.144 patients with stage II primary colon cancer were retrospectively enrolledin the study. SOX9 expression was...... high levels of SOX9 of primary stage II colon tumors predict low riskof relapse whereas low levels of SOX9 predict high risk of relapse. SOX9 may have an important value as a biomarker when evaluating risk of relapse for personalized treatment....

  20. Predicting survival after radical cystectomy for bladder cancer.

    Science.gov (United States)

    Margulis, Vitaly; Lotan, Yair; Montorsi, Francesco; Shariat, Shahrokh F

    2008-07-01

    Accurate prediction is essential for patient counselling, appropriate selection of treatments and determination of eligibility for clinical trials. In this review we assess the available determinants of oncological outcome after radical cystectomy (RC) for transitional cell carcinoma of the urinary bladder. We reviewed previous publications to provide guidelines in terms of criteria, limitations and clinical value of available tools for predicting patient outcome after RC. Our findings suggest that while individual surgical, patient and pathological features provide useful estimates of survival outcome, the inherent heterogeneity of tumour biology and patient characteristics leads to significant variation in outcome. By incorporating all relevant continuous predictive factors for individual patients, integrative predictive models, such as nomograms or artificial neural networks, provide more accurate predictions and generally surpass clinical experts at predicting outcomes. Nonetheless, there is a clear need for the development and validation of molecular biomarkers and their incorporation into multivariable predictive tools. Significant progress has been made in identifying important molecular markers of disease and the development of multifactorial tools for predicting the outcome after RC. PMID:18325050

  1. 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)...

  2. 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...

  3. 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...

  4. 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

  5. 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.

  6. 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.

  7. 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.

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

    International Nuclear Information System (INIS)

    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

  9. 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.

  10. Does pain acceptance predict physical and psychological outcomes in cancer outpatients with pain?

    Directory of Open Access Journals (Sweden)

    Evangelia Protopapa

    2013-09-01

    Full Text Available Background: Pain and psychological distress frequently co-exist in cancer patients. Pain acceptance has been associated with improved physical and psychosocial well-being in chronic non-malignant pain patients, however its effects are unclear in cancer outpatients with pain. Methods: The sample consisted of 116 outpatients recruited from a tertiary oncology centre, with various types of cancer and pain levels. To assess patients, we used the Brief Pain Inventory, the Hospital Anxiety and Depression Scale and the Chronic Pain Acceptance Questionnaire comprising of: Activity engagement and Pain willingness. The extent to which acceptance predicts physical and psychological outcomes was investigated using multiple regression analyses. Results: Adjusting for patient characteristics and outcomes, activity engagement and pain willingness significantly predicted pain interference with function (p=.033 and p=.041 respectively. However, only the activity engagement predicted anxiety (p=.001 and depression (p<.001. Conclusion: Components of pain acceptance predicted patient functional outcomes. Activity engagement in particular, shows promise in predicting psychological well-being. Further studies could confirm its role in reducing anxiety and depression in cancer patients with pain, and whether it should be included in cancer pain management interventions.

  11. Expression of RET finger protein predicts chemoresistance in epithelial ovarian cancer

    International Nuclear Information System (INIS)

    Resistance to platinum- and taxane-based chemotherapy is a major cause of treatment failure in ovarian cancer. Thus, it is necessary to develop a predictive marker and molecular target for overcoming drug resistance in ovarian cancer treatment. In a previous report, using an in vitro model, we found that the RET finger protein (RFP) (also known as tripartite motif-containing protein 27, TRIM27) confers cancer cell resistance to anticancer drugs. However, the significance of RFP expression in cancer patients remains elusive. In this study, we showed that RFP was expressed in 62% of ovarian cancer patients and its positivity significantly correlated with drug resistance. Consistent with clinical data, depletion of RFP by RNA interference (RNAi) in ovarian cancer cell lines, SKOV3 and HEY, significantly increased carboplatin- or paclitaxel-induced apoptosis and resulted in reduced anticancer drug resistance. In a nude mouse tumor xenograft model, inoculated RFP-knockdown ovarian cancer cells exhibited lower carboplatin resistance than control cells. These findings suggest that RFP could be a predictive marker for chemoresistance in ovarian cancer patients and also a candidate for a molecular-targeted agent

  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. Recent advancements in toxicity prediction following prostate cancer radiotherapy.

    Science.gov (United States)

    Ospina, J D; Fargeas, A; Dréan, G; Simon, A; Acosta, O; de Crevoisier, R

    2015-01-01

    In external beam radiotherapy for prostate cancer limiting toxicities for dose escalation are bladder and rectum toxicities. Normal tissue complication probability models aim at quantifying the risk of developping adverse events following radiotherapy. These models, originally proposed in the context of uniform irradiation, have evolved to implementations based on the state-of-the-art classification methods which are trained using empirical data. Recently, the use of image processing techniques combined with population analysis methods has led to a new generation of models to understand the risk of normal tissue complications following radiotherapy. This paper overviews those methods in the case of prostate cancer radiation therapy and propose some lines of future research. PMID:26737471

  15. Application of Artificial Neural Network in Predicting the Survival Rate of Gastric Cancer Patients

    Directory of Open Access Journals (Sweden)

    A Biglarian

    2011-06-01

    Full Text Available "nBackground: The aim of this study was to predict the survival rate of Iranian gastric cancer patients using the Cox proportional hazard and artificial neural network models as well as comparing the ability of these approaches in predicting the survival of these patients."nMethods: In this historical cohort study, the data gathered from 436 registered gastric cancer patients who have had surgery between 2002 and 2007 at the Taleghani Hospital (a referral center for gastrointestinal cancers, Tehran, Iran, to predict the survival time using Cox proportional hazard and artificial neural network techniques. "nResults: The estimated one-year, two-year, three-year, four-year and five-year survival rates of the patients were 77.9%, 53.1%, 40.8%, 32.0%, and 17.4%, respectively. The Cox regression analysis revealed that the age at diag-nosis, high-risk behaviors, extent of wall penetration, distant metastasis and tumor stage were significantly associated with the survival rate of the patients. The true prediction of neural network was 83.1%, and for Cox regression model, 75.0%."nConclusion: The present study shows that neural network model is a more powerful statistical tool in predicting the survival rate of the gastric cancer patients compared to Cox proportional hazard regression model. Therefore, this model recommended for the predicting the survival rate of these patients.

  16. 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.

  17. 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.

  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. 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...

  20. 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...

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  5. 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...

  6. 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.)

  7. 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.)

  8. 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

  9. "THE EFFICACY OF VIDEO LAPARASCOPY FOR RESECTABILITY PREDICTION OF GASTRIC CANCER "

    Directory of Open Access Journals (Sweden)

    F. Safarpour D. Safarpour

    2003-08-01

    Full Text Available Cancer of the stomach carries poor prognosis. Surgery is the best treatment for gastric cancer. Prediction of survival depends on the stage at the time of presentation. Fluoroscopy, sonography, and computerized tomography are used for advanced gastric cancers staging, but they are not accurate enough to grade advanced gastric cancers. Laparoscopic findings of lesions under direct vision, are magnified 15 times and have been used for gastric cancer staging and more specific prediction. To demonstrate the importance of laparascopy, we carried out this study on 84 confirmed cases of gastric cancer prior to laparatomy. Results of computerized tomography were compared with the findings of laparascopies, and laparotomy was the gold standard in this study. Abdominal CT of gastric lesions had recommended resectability in 84 cases. Resectability was believed possible by laparoscopy only in 65 patients (19 false positives. Sixty-five patients were considered to have true positive diagnosis of resectability and one false negative and two false positive cases had false laparoscopic finding, which were confirmed by laparatomy and all of them were resectable. Eleven patients were diagnosed as stage IV because distant metastases were found during laparoscopy. This study showed that there are 42% differences between CT and laparoscopic findings. In this study the sensitivity and specifity of CT for stage II are respectively 77.7% and 82% but sensitivity and specifity of laparoscopy for stage III are respectively 78% and 55% and sensitivity and specifity of laparoscopy for stage III gastric cancer are respectively 94.5% and 100%. Laparoscopic examination is a valuable tool for diagnosing metastases and should be used for the management of advanced gastric cancers. We observed that 22% of patient had no need to undergo surgical operation, if they had pre-operative laparoscopic examination. This study suggests that terminally ill patients, and in advanced gastric

  10. Upregulation of KPNβ1 in gastric cancer cell promotes tumor cell proliferation and predicts poor prognosis.

    Science.gov (United States)

    Zhu, Jia; Wang, Yingying; Huang, Hua; Yang, Qichang; Cai, Jing; Wang, Qiuhong; Gu, Xiaoling; Xu, Pan; Zhang, Shusen; Li, Manhua; Ding, Haifang; Yang, Lei

    2016-01-01

    KPNβ1, also known as importin β, P97, is reported as one of soluble transport factors that mediates transportion of proteins and RNAs between the nucleus and cytoplasm in cellular process. Recent studies show that KPNβ1 is a tumor gene which is highly expressed in several malignant tumors such as ovarian cancer, cervical tumor, neck cancer, and lung cancer via promoting cell proliferation or inhibiting cell apoptotic pathways. However, the the role of KPNβ1 in gastric cancer remains unclear. In this study, Western blot and immunohistochemistrical analyses showed that KPNβ1 was significantly upregulated in clinical gastric cancer specimens compared with adjacent noncancerous tissues. KPNβ1 was positively correlated with tumor grade, Ki-67, and predicted poor prognosis of gastric cancer. More importantly, through starvation-refeeding model, CCK8 assay, flow cytometry, colony formation assays, the vitro studies demonstrated that KPNβ1 promoted proliferation of gastric cancer cells, while KPNβ1 knockdown led to decreased cell proliferation and arrested cell cycle at G1 phase. Furthermore, our results also indicated that KPNβ1 expression could result in docetaxel resistance. And, KPNβ1 could interact with Stat1, contributed to its nucleus import in gastric cancer cells. These findings provided a novel promising therapeutic targets for clinical treatment against human gastric cancer. PMID:26242264

  11. 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...

  12. 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...

  13. Early diagnosis of breast cancer dissemination by tumor markers follow-up and method of prediction

    International Nuclear Information System (INIS)

    A mathematical model of prediction of progression was tested in patients with breast cancer employing long-term monitoring of tumor markers CEA, CA 15/3, MSA and TPA, erythrocyte sedimentation rate (FW), and the enzymes gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP) and lactate dehydrogenase (LD) in serum. At the same time, specificity, sensitivity, lead time and positive predictive value were evaluated along with false positivity for the all these parameters and their combinations. A model was proposed for the follow up of patients with breast cancer after the completion of basic therapy. (author)

  14. 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 ...

  15. 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. 

  16. 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.

  17. 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

  18. 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

  19. Predicting cancer drug mechanisms of action using molecular network signatures.

    Science.gov (United States)

    Pritchard, Justin R; Bruno, Peter M; Hemann, Michael T; Lauffenburger, Douglas A

    2013-07-01

    Molecular signatures are a powerful approach to characterize novel small molecules and derivatized small molecule libraries. While new experimental techniques are being developed in diverse model systems, informatics approaches lag behind these exciting advances. We propose an analysis pipeline for signature based drug annotation. We develop an integrated strategy, utilizing supervised and unsupervised learning methodologies that are bridged by network based statistics. Using this approach we can: 1, predict new examples of drug mechanisms that we trained our model upon; 2, identify "New" mechanisms of action that do not belong to drug categories that our model was trained upon; and 3, update our training sets with these "New" mechanisms and accurately predict entirely distinct examples from these new categories. Thus, not only does our strategy provide statistical generalization but it also offers biological generalization. Additionally, we show that our approach is applicable to diverse types of data, and that distinct biological mechanisms characterize its resolution of categories across different data types. As particular examples, we find that our predictive resolution of drug mechanisms from mRNA expression studies relies upon the analog measurement of a cell stress-related transcriptional rheostat along with a transcriptional representation of cell cycle state; whereas, in contrast, drug mechanism resolution from functional RNAi studies rely upon more dichotomous (e.g., either enhances or inhibits) association with cell death states. We believe that our approach can facilitate molecular signature-based drug mechanism understanding from different technology platforms and across diverse biological phenomena. PMID:23287973

  20. 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.

  1. 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…

  2. Personalized Circulating Tumor DNA Biomarkers Dynamically Predict Treatment Response and Survival In Gynecologic Cancers.

    Directory of Open Access Journals (Sweden)

    Elena Pereira

    Full Text Available High-grade serous ovarian and endometrial cancers are the most lethal female reproductive tract malignancies worldwide. In part, failure to treat these two aggressive cancers successfully centers on the fact that while the majority of patients are diagnosed based on current surveillance strategies as having a complete clinical response to their primary therapy, nearly half will develop disease recurrence within 18 months and the majority will die from disease recurrence within 5 years. Moreover, no currently used biomarkers or imaging studies can predict outcome following initial treatment. Circulating tumor DNA (ctDNA represents a theoretically powerful biomarker for detecting otherwise occult disease. We therefore explored the use of personalized ctDNA markers as both a surveillance and prognostic biomarker in gynecologic cancers and compared this to current FDA-approved surveillance tools.Tumor and serum samples were collected at time of surgery and then throughout treatment course for 44 patients with gynecologic cancers, representing 22 ovarian cancer cases, 17 uterine cancer cases, one peritoneal, three fallopian tube, and one patient with synchronous fallopian tube and uterine cancer. Patient/tumor-specific mutations were identified using whole-exome and targeted gene sequencing and ctDNA levels quantified using droplet digital PCR. CtDNA was detected in 93.8% of patients for whom probes were designed and levels were highly correlated with CA-125 serum and computed tomography (CT scanning results. In six patients, ctDNA detected the presence of cancer even when CT scanning was negative and, on average, had a predictive lead time of seven months over CT imaging. Most notably, undetectable levels of ctDNA at six months following initial treatment was associated with markedly improved progression free and overall survival.Detection of residual disease in gynecologic, and indeed all cancers, represents a diagnostic dilemma and a potential

  3. Personalized Circulating Tumor DNA Biomarkers Dynamically Predict Treatment Response and Survival In Gynecologic Cancers

    Science.gov (United States)

    Anand, Sanya; Sebra, Robert; Catalina Camacho, Sandra; Garnar-Wortzel, Leopold; Nair, Navya; Moshier, Erin; Wooten, Melissa; Uzilov, Andrew; Chen, Rong; Prasad-Hayes, Monica; Zakashansky, Konstantin; Beddoe, Ann Marie; Schadt, Eric; Dottino, Peter; Martignetti, John A.

    2015-01-01

    Background High-grade serous ovarian and endometrial cancers are the most lethal female reproductive tract malignancies worldwide. In part, failure to treat these two aggressive cancers successfully centers on the fact that while the majority of patients are diagnosed based on current surveillance strategies as having a complete clinical response to their primary therapy, nearly half will develop disease recurrence within 18 months and the majority will die from disease recurrence within 5 years. Moreover, no currently used biomarkers or imaging studies can predict outcome following initial treatment. Circulating tumor DNA (ctDNA) represents a theoretically powerful biomarker for detecting otherwise occult disease. We therefore explored the use of personalized ctDNA markers as both a surveillance and prognostic biomarker in gynecologic cancers and compared this to current FDA-approved surveillance tools. Methods and Findings Tumor and serum samples were collected at time of surgery and then throughout treatment course for 44 patients with gynecologic cancers, representing 22 ovarian cancer cases, 17 uterine cancer cases, one peritoneal, three fallopian tube, and one patient with synchronous fallopian tube and uterine cancer. Patient/tumor-specific mutations were identified using whole-exome and targeted gene sequencing and ctDNA levels quantified using droplet digital PCR. CtDNA was detected in 93.8% of patients for whom probes were designed and levels were highly correlated with CA-125 serum and computed tomography (CT) scanning results. In six patients, ctDNA detected the presence of cancer even when CT scanning was negative and, on average, had a predictive lead time of seven months over CT imaging. Most notably, undetectable levels of ctDNA at six months following initial treatment was associated with markedly improved progression free and overall survival. Conclusions Detection of residual disease in gynecologic, and indeed all cancers, represents a diagnostic

  4. Nomogram of Naive Bayesian Model for Recurrence Prediction of Breast Cancer

    Science.gov (United States)

    Kim, Woojae; Kim, Ku Sang

    2016-01-01

    Objectives Breast cancer has a high rate of recurrence, resulting in the need for aggressive treatment and close follow-up. However, previously established classification guidelines, based on expert panels or regression models, are controversial. Prediction models based on machine learning show excellent performance, but they are not widely used because they cannot explain their decisions and cannot be presented on paper in the way that knowledge is customarily represented in the clinical world. The principal objective of this study was to develop a nomogram based on a naïve Bayesian model for the prediction of breast cancer recurrence within 5 years after breast cancer surgery. Methods The nomogram can provide a visual explanation of the predicted probabilities on a sheet of paper. We used a data set from a Korean tertiary teaching hospital of 679 patients who had undergone breast cancer surgery between 1994 and 2002. Seven prognostic factors were selected as independent variables for the model. Results The accuracy was 80%, and the area under the receiver operating characteristics curve (AUC) of the model was 0.81. Conclusions The nomogram can be easily used in daily practice to aid physicians and patients in making appropriate treatment decisions after breast cancer surgery. PMID:27200218

  5. Predicting cetuximab efficacy in patients with advanced colorectal cancer

    Directory of Open Access Journals (Sweden)

    Sahin IH

    2014-06-01

    Full Text Available Ibrahim H Sahin, Christopher R Garrett Department of Gastrointestinal Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA Abstract: Cetuximab has demonstrated activity, both as monotherapy, and in combination with cytotoxic chemotherapy, albeit modest. Efforts over the last decade have focused on determining which patient populations are most likely to benefit from this chimeric monoclonal antibody therapy. As the antibody targets the epidermal growth factor receptor (EGFR, cell surface expression by immunohistochemistry was hypothesized to be a biomarker of clinical efficacy; subsequent clinical trials have shown that this was not the case. Tumor KRAS mutation (the most frequently observed site is at codon 12 has been shown to be a negative biomarker (ie, a marker of cetuximab resistance; since 2008, treatment of patients with cetuximab has been restricted to those whose tumors do not harbor a KRAS mutation. There is considerable heterogeneity of KRAS mutations, and studies are ongoing to determine whether cetuximab resistance extends to those patients whose tumors have a KRAS codon 13, 61, and 164, mutation. EGFR gene copy, or more precisely a lack of increase in EGFR gene copy number, has been demonstrated to be a negative biomarker of EGFR efficacy; currently, it is not in routine use as a clinical standard of care. Tumor BRAF status, NRAS status, and PIK3CA mutation status are being evaluated as additional potential negative biomarkers of treatment efficacy. High expression of the receptor ligands epiregulin and amphiregulin has been shown to be a positive biomarker for treatment efficacy and is continuing to be studied clinically. After almost 10 years following the widespread introduction of cetuximab into the clinic as a treatment for metastatic colorectal cancer, the story of identifying suitable biomarkers of efficacy is still evolving. The tremendous tumor heterogeneity at the molecular level and the cell

  6. 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.

  7. 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.

  8. 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

  9. Prognostic and predictive value of cathepsin X in serum from colorectal cancer patients

    DEFF Research Database (Denmark)

    Vižin, Tjaša; Christensen, Ib Jarle; Wilhelmsen, Michael; Nielsen, Hans Jørgen; Kos, Janko

    2014-01-01

    BACKGROUND: Cathepsin X is a cysteine protease involved in mechanisms of malignant progression. It is secreted from tumour cells as a proenzyme and may serve to predict the disease status and risk of death for cancer patients. In a previous, pilot, study on 77 colorectal patients we demonstrated...

  10. 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.

  11. 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

  12. 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

    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 was...

  13. Expression profiling in head and neck cancer: Predicting response to chemoradiation

    OpenAIRE

    Pramana, J.

    2014-01-01

    The goal of this thesis was to find biomarkers through gene expression profiling, using microarray techniques, which can predict outcome after concurrent chemoradiation in head and neck cancer. The main endpoints for outcome were local control, locoregional control and disease free survival.

  14. 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

  15. 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...

  16. 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

    (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......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...... 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...

  17. 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.

  18. 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)

  19. The value of surrogate endpoints for predicting real-world survival across five cancer types.

    Science.gov (United States)

    Shafrin, Jason; Brookmeyer, Ron; Peneva, Desi; Park, Jinhee; Zhang, Jie; Figlin, Robert A; Lakdawalla, Darius N

    2016-04-01

    Objective It is unclear how well different outcome measures in randomized controlled trials (RCTs) perform in predicting real-world cancer survival. We assess the ability of RCT overall survival (OS) and surrogate endpoints - progression-free survival (PFS) and time to progression (TTP) - to predict real-world OS across five cancers. Methods We identified 20 treatments and 31 indications for breast, colorectal, lung, ovarian, and pancreatic cancer that had a phase III RCT reporting median OS and median PFS or TTP. Median real-world OS was determined using a Kaplan-Meier estimator applied to patients in the Surveillance and Epidemiology End Results (SEER)-Medicare database (1991-2010). Performance of RCT OS and PFS/TTP in predicting real-world OS was measured using t-tests, median absolute prediction error, and R(2) from linear regressions. Results Among 72,600 SEER-Medicare patients similar to RCT participants, median survival was 5.9 months for trial surrogates, 14.1 months for trial OS, and 13.4 months for real-world OS. For this sample, regression models using clinical trial OS and trial surrogates as independent variables predicted real-world OS significantly better than models using surrogates alone (P = 0.026). Among all real-world patients using sample treatments (N = 309,182), however, adding trial OS did not improve predictive power over predictions based on surrogates alone (P = 0.194). Results were qualitatively similar using median absolute prediction error and R(2) metrics. Conclusions Among the five tumor types investigated, trial OS and surrogates were each independently valuable in predicting real-world OS outcomes for patients similar to trial participants. In broader real-world populations, however, trial OS added little incremental value over surrogates alone. PMID:26743800

  20. 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

  1. 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

  2. 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

    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...... in subcohorts defined by patients' sex, age, cancer type, and time period of diagnosis, which had nearly complete follow-up. Based on theoretical considerations, we fitted a linear relationship between observed means and medians on the log scale. For validation, we estimated mean survival from medians...

  3. 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.

  4. 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.

  5. 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

  6. Cross Validation Evaluation for Breast Cancer Prediction Using Multilayer Perceptron Neural Networks

    Directory of Open Access Journals (Sweden)

    Shirin A. Mojarad

    2011-01-01

    Full Text Available Problem statement: The presence of metastasis in the regional lymph nodes is the most important factor in predicting prognosis in breast cancer. Many biomarkers have been identified that appear to relate to the aggressive behaviour of cancer. However, the nonlinear relation of these markers to nodal status and also the existence of complex interaction between markers have prohibited an accurate prognosis. Approach: The aim of this study is to investigate the effectiveness of a Multilayer Perceptron (MLP for predicting breast cancer progression using a set of four biomarkers of breast tumors. The biomarkers include DNA ploidy, cell cycle distribution (G0G1/G2M, steroid receptors (ER/PR and S-Phase Fraction (SPF. A further objective of the study is to explore the predictive potential of these markers in defining the state of nodal involvement in breast cancer. Two methods of outcome evaluation viz. stratified and simple k-fold Cross Validation (CV are studied in order to assess their accuracy and reliability for neural network validation. Criteria such as output accuracy, sensitivity and specificity are used for selecting the best validation technique besides evaluating the network outcome for different combinations of markers. Results: The results show that stratified 2-fold CV is more accurate and reliable compared to simple k-fold CV as it obtains a higher accuracy and specificity and also provides a more stable network validation in terms of sensitivity. Best prediction results are obtained by using an individual marker-SPF which obtains an accuracy of 65%. Conclusion/Recommendations: Our findings suggest that MLP-based analysis provides an accurate and reliable platform for breast cancer prediction given that an appropriate design and validation method is employed.

  7. Clinical utility of polymorphisms in one-carbon metabolism for breast cancer risk prediction

    Directory of Open Access Journals (Sweden)

    Shaik Mohammad Naushad

    2011-01-01

    Full Text Available This study addresses the issues in translating the laboratory derived data obtained during discovery phase of research to a clinical setting using a breast cancer model. Laboratory-based risk assessment indi-cated that a family history of breast cancer, reduced folate carrier 1 (RFC1 G80A, thymidylate synthase (TYMS 5’-UTR 28bp tandem repeat, methylene tetrahydrofolate reductase (MTHFR C677T and catecholamine-O-methyl transferase (COMT genetic polymorphisms in one-carbon metabolic pathway increase the risk for breast cancer. Glutamate carboxypeptidase II (GCPII C1561T and cytosolic serine hydroxymethyl transferase (cSHMT C1420T polymorphisms were found to decrease breast cancer risk. In order to test the clinical validity of this information in the risk prediction of breast cancer, data was stratified based on number of protective alleles into four categories and in each category sensitivity and 1-specificity values were obtained based on the distribution of number of risk alleles in cases and controls. Receiver operating characteristic (ROC curves were plotted and the area under ROC curve (C was used as a measure of discriminatory ability between cases and controls. In subjects without any protective allele, aberrations in one-carbon metabolism showed perfect prediction (C=0.93 while the predictability was lost in subjects with one protective allele (C=0.60. However, predictability increased steadily with increasing number of protective alleles (C=0.63 for 2 protective alleles and C=0.71 for 3 protective alleles. The cut-off point for discrimination was >4 alleles in all predictable combinations. Models of this kind can serve as valuable tools in translational re-search, especially in identifying high-risk individuals and reducing the disease risk either by life style modification or by medical intervention.

  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. 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.

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

    International Nuclear Information System (INIS)

    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. 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

  12. 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 search...... “haemospermia” were not found. Lung cancer: For “haemoptysis” the PPV increased from 8.4 in patients aged 55 years to 20.4 at the age of > 85 years. PPV for “cough”, “pain in the thorax”, “dyspnoea” and “general symptoms” were low (0.4-1.1%). Using a new algorithm that estimates the PPV of combinations of....... Colorectal cancer: The PPV of “rectal bleeding” was high for patients > 60 years (6.6-21.2%), but much lower in younger age groups. For “change in bowel habits” and “significant general symptoms”, the PPV was 3.5-8.5%. Breast cancer: “Palpable suspected tumour” was well supported (8.1-24%). No studies on the...

  13. 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.

  14. 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

  15. 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

  16. 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...

  17. Elevated HMGA2 expression is associated with cancer aggressiveness and predicts poor outcome in breast cancer.

    Science.gov (United States)

    Wu, Jingjing; Zhang, Shizhen; Shan, Jinlan; Hu, Zujian; Liu, Xiyong; Chen, Lirong; Ren, Xingchang; Yao, Lifang; Sheng, Hongqiang; Li, Ling; Ann, David; Yen, Yun; Wang, Jian; Wang, Xiaochen

    2016-07-01

    High mobility group AT-hook 2 (HMGA2) is involved in a wide spectrum of biological processes and is upregulated in several tumors. Here, we collected 273 breast cancer (BC) specimens as a training set and 310 specimens as a validation set to examine the expression of HMGA2 by immunohistochemical staining. It was found that HMGA2 expression was significantly positively correlated with advanced tumor grade and poor survival. Subgroup analysis indicated that high level of HMGA2 was significantly correlated with poor prognosis, especially in the subgroups of stage II-III, low pathological grade and non-triple negative breast cancer cases. Gene set enrichment analysis (GSEA) demonstrated a significant positive correlation between HMGA2 level and the gene expression signature of metaplastic and mesenchymal phenotype. Importantly, we also observed that ectopic expression of HMGA2 promoted the migration and invasion of breast cancer cells, and protected cancer cells against genotoxic stress from agents stimulating P53 (Ser15) phosphorylation. As a conclusion, expression of HMGA2 might indicate more advanced malignancy of breast cancer. Thus we believe HMGA2 could serve as a biomarker of poor prognosis and a novel target in treating BC tumors. PMID:27063096

  18. 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...

  19. Displacements Prediction in Double-Arch Dam Rock Abutment Using SPSS Software Based on Extensometer Readings Case study: Karun 4 Concrete Dam, Iran

    OpenAIRE

    Hadi kamali Bandpey; Kaveh Ahangari; Mirsaeid Hosseini Shirvani

    2012-01-01

    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 extens...

  20. 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

  1. Methodology to predict long-term cancer survival from short-term data using Tobacco Cancer Risk and Absolute Cancer Cure models

    International Nuclear Information System (INIS)

    Three parametric statistical models have been fully validated for cancer of the larynx for the prediction of long-term 15, 20 and 25 year cancer-specific survival fractions when short-term follow-up data was available for just 1-2 years after the end of treatment of the last patient. In all groups of cases the treatment period was only 5 years. Three disease stage groups were studied, T1N0, T2N0 and T3N0. The models are the Standard Lognormal (SLN) first proposed by Boag (1949 J. R. Stat. Soc. Series B 11 15-53) but only ever fully validated for cancer of the cervix, Mould and Boag (1975 Br. J. Cancer 32 529-50), and two new models which have been termed Tobacco Cancer Risk (TCR) and Absolute Cancer Cure (ACC). In each, the frequency distribution of survival times of defined groups of cancer deaths is lognormally distributed: larynx only (SLN), larynx and lung (TCR) and all cancers (ACC). All models each have three unknown parameters but it was possible to estimate a value for the lognormal parameter S a priori. By reduction to two unknown parameters the model stability has been improved. The material used to validate the methodology consisted of case histories of 965 patients, all treated during the period 1944-1968 by Dr Manuel Lederman of the Royal Marsden Hospital, London, with follow-up to 1988. This provided a follow-up range of 20- 44 years and enabled predicted long-term survival fractions to be compared with the actual survival fractions, calculated by the Kaplan and Meier (1958 J. Am. Stat. Assoc. 53 457-82) method. The TCR and ACC models are better than the SLN model and for a maximum short-term follow-up of 6 years, the 20 and 25 year survival fractions could be predicted. Therefore the numbers of follow-up years saved are respectively 14 years and 19 years. Clinical trial results using the TCR and ACC models can thus be analysed much earlier than currently possible. Absolute cure from cancer was also studied, using not only the prediction models which

  2. 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...

  3. Circulating HER2 DNA after trastuzumab treatment predicts survival and response in breast cancer

    DEFF Research Database (Denmark)

    Sorensen, Boe S; Mortensen, Lise S; Andersen, Jørn;

    2010-01-01

    BACKGROUND: Only a subset of breast cancer patients responds to the HER2 inhibitor trastuzumab, and methods to identify responders are needed. PATIENTS AND METHODS: We studied 28 patients with metastatic breast cancer that had amplified human epidermal growth factor receptor 2 (HER2) genes in their...... response (p=0.02), and overall survival (p=0.05). HER2 ECD kinetics did not correlate to clinical data. CONCLUSION: We suggest that a decrease in HER2 gene amplification in the plasma predicts a more favourable response to trastuzumab....

  4. 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 in the....... The univariate analysis of prognostic factors for brain metastases showed a significant correlation with systemic disease (P30 ng/ml (P=0.002). Only systemic disease (P30 ng/ml were identified to correlate with increased risk of brain metastases, which calls for further investigation....

  5. 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.

  6. 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

  7. 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.

  8. Evaluation of the use of decision-support software in carcino-embryonic antigen (CEA-based follow-up of patients with colorectal cancer

    Directory of Open Access Journals (Sweden)

    Verberne Charlotte J

    2012-03-01

    Full Text Available Abstract Background The present paper is a first evaluation of the use of "CEAwatch", a clinical support software system for surgeons for the follow-up of colorectal cancer (CRC patients. This system gathers Carcino-Embryonic Antigen (CEA values and automatically returns a recommendation based on the latest values. Methods Consecutive patients receiving follow-up care for CRC fulfilling our in- and exclusion criteria were identified to participate in this study. From August 2008, when the software was introduced, patients were asked to undergo the software-supported follow-up. Safety of the follow-up, experiences of working with the software, and technical issues were analyzed. Results 245 patients were identified. The software-supported group contained 184 patients; the control group contained 61 patients. The software was safe in finding the same amount of recurrent disease with fewer outpatient visits, and revealed few technical problems. Clinicians experienced a decrease in follow-up workload of up to 50% with high adherence to the follow-up scheme. Conclusion CEAwatch is an efficient software tool helping clinicians working with large numbers of follow-up patients. The number of outpatient visits can safely be reduced, thus significantly decreasing workload for clinicians.

  9. 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

  10. 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.

  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. 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.

  13. 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.

  14. 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...... 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......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...

  15. Prostate cancer risk prediction based on complete prostate cancer family history

    OpenAIRE

    Albright, Frederick; Stephenson, Robert A; Agarwal, Neeraj; Teerlink, Craig C.; Lowrance, William T.; Farnham, James M.; Albright, Lisa A Cannon

    2014-01-01

    Background Prostate cancer (PC) relative risks (RRs) are typically estimated based on status of close relatives or presence of any affected relatives. This study provides RR estimates using extensive and specific PC family history. Methods A retrospective population-based study was undertaken to estimate RRs for PC based on complete family history of PC. A total of 635,443 males, all with ancestral genealogy data, were analyzed. RRs for PC were determined based upon PC rates estimated from ma...

  16. 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.

  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. Predictive factors for the development of persistent pain after breast cancer surgery

    DEFF Research Database (Denmark)

    Andersen, Kenneth Geving; Duriaud, Helle Molter; Jensen, Helle Elisabeth; Kroman, Niels; Kehlet, Henrik

    2015-01-01

    , and 1 year after surgery. A comprehensive validated questionnaire was used. Handling of the intercostobrachial nerve was registered by the surgeon. Factors known by the first 3 weeks after surgery were modeled in ordinal logistic regression analyses. Five hundred thirty-seven patients with baseline......Previous studies have reported that 15% to 25% of patients treated for breast cancer experience long-term moderate-to-severe pain in the area of surgery, potentially lasting for several years. Few prospective studies have included all potential risk factors for the development of persistent pain...... after breast cancer surgery (PPBCS). The aim of this prospective cohort study was to comprehensively identify factors predicting PPBCS. Patients scheduled for primary breast cancer surgery were recruited. Assessments were conducted preoperatively, the first 3 days postoperatively, and 1 week, 6 months...

  20. Combining Dissimilarities in a Hyper Reproducing Kernel Hilbert Space for Complex Human Cancer Prediction

    Directory of Open Access Journals (Sweden)

    Ángela Blanco

    2009-01-01

    Full Text Available DNA microarrays provide rich profiles that are used in cancer prediction considering the gene expression levels across a collection of related samples. Support Vector Machines (SVM have been applied to the classification of cancer samples with encouraging results. However, they rely on Euclidean distances that fail to reflect accurately the proximities among sample profiles. Then, non-Euclidean dissimilarities provide additional information that should be considered to reduce the misclassification errors. In this paper, we incorporate in the ν-SVM algorithm a linear combination of non-Euclidean dissimilarities. The weights of the combination are learnt in a (Hyper Reproducing Kernel Hilbert Space HRKHS using a Semidefinite Programming algorithm. This approach allows us to incorporate a smoothing term that penalizes the complexity of the family of distances and avoids overfitting. The experimental results suggest that the method proposed helps to reduce the misclassification errors in several human cancer problems.

  1. Caveolin-1 Expression Level in Cancer Associated Fibroblasts Predicts Outcome in Gastric Cancer

    Science.gov (United States)

    Gao, Jun; Fan, Lifang; Li, Zonghuan; Yang, Guifang; Chen, Honglei

    2013-01-01

    Aims Altered expression of epithelial or stromal caveolin-1 (Cav-1) is observed in various types of human cancers. However, the clinical significance of Cav-1 expression in gastric cancer (GC) remains largely unknown. The present study aims to explore the clinicopathological significance and prognostic value of both tumor cells and cancer associated fibroblasts (CAFs) Cav-1 in GC. Methods and Results Quantum dots immunofluorescence histochemistry was performed to examine the expression of Cav-1 in 20 cases of gastritis without intestinal metaplasia (IM), 20 cases of gastritis with IM and 286 cases of GC. Positive rates of epithelial Cav-1 in gastritis without IM, gastritis with IM and GC showed a decreasing trend (P = 0.012). Low expression of Cav-1 in CAFs but not in tumor cells was an independent predictor of poor prognosis in GC patients (P = 0.034 and 0.005 respectively in disease free survival and overall survival). Cav-1 level in tumor cells and CAFs showed no significant correlation with classic clinicopathological features. Conclusions Loss of epithelial Cav-1 may promote malignant progression and low CAFs Cav-1 level herald worse outcome of GC patient, suggesting CAFs Cav-1 may be a candidate therapeutic target and a useful prognostic marker of GC. PMID:23527097

  2. 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)

  3. 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....

  4. Predictive factor for the response to adjuvant therapy with emphasis in breast cancer

    International Nuclear Information System (INIS)

    One of the major challenges of early-stage breast cancer is to select the adjuvant therapy that ensures the most benefits and the least harm for the patient. The definition of accurate predictive factors is therefore of paramount importance. So far the choice of adjuvant therapy has been based on the number of affected lymph nodes and the hormone receptor status of the patient. This paper evaluates the use of other tumor-related markers as predictive factors for adjuvant therapy. These include HER2, p53 and Bcl-2, cathepsin B, p27, proliferating cell nuclear antigen (PCNA), cyclin D, Ki-67, and vascular endothelial growth factor (VEGF)

  5. 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 ...

  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. 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

  8. Comparison of the predictive qualities of three prognostic models of colorectal cancer.

    Science.gov (United States)

    Anderson, Billie; Hardin, J Michael; Alexander, Dominik D; Grizzle, William E; Meleth, Sreelatha; Manne, Upender

    2010-01-01

    Most discoveries of cancer biomarkers involve construction of a single model to determine predictions of survival.. 'Data-mining' techniques, such as artificial neural networks (ANNs), perform better than traditional methods, such as logistic regression. In this study, the quality of multiple predictive models built on a molecular data set for colorectal cancer (CRC) was evaluated. Predictive models (logistic regressions, ANNs, and decision trees) were compared, and the effect of techniques for variable selection on the predictive quality of these models was investigated. The Kolmogorov-Smirnoff (KS) statistic was used to compare the models. Overall, the logistic regression and ANN methods outperformed use of a decision tree. In some instances (e.g., for a model that included 'all variables without tumor stage' and use of a decision tree for variable selection), the ANN marginally outperformed logistic regression, although the difference between the accuracy of the KS statistic was minimal (0.80 versus 0.82). Regardless of the variable(s) and the methods for variable selection, all three predictive models identified survivors and non-survivors with the same level of statistical accuracy. PMID:20515758

  9. 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.

  10. 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....

  11. 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...

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

    International Nuclear Information System (INIS)

    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

  13. 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 ...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. [Revision of therapeutic index for targeted treatment in kidney cancer: What if toxicity could predict efficacy?].

    Science.gov (United States)

    Grellety, Thomas; Brugères-Chakiba, Camille; Chaminade, Axel; Roubaud, Guilhem; Ravaud, Alain; Gross-Goupil, Marine

    2014-06-01

    Since 2006, new treatments as targeted therapies (anti angiogenic and mTOR inhibitors) are prescribed in renal cell cancer. Toxicity of these treatments is well known by clinicians. Occurrence of these side effects has been associated with anti tumoral efficacy. High blood pressure, hypothyroïdie and hand foot syndrome were reported to be predictive of anti tumoral response. Fatigue and hyponatremia are still largely discussed. Moreover, non infectious pneumonia, which frequently occurs with mTOR inhibitors, is associated with clinical benefit. The main objective of treatment of advanced kidney cancer, specially renal cell cancer, is obtaining clinical benefit (stabilization and response) with a chronic evolution of the disease. This prolong exposure to drugs, according to their toxicity profile, often contributes to dose reduction, moreover interruption of treatment, potentially associated with a loss of control of disease. Thus, the adverse effects, described hereby, may be considered as « positive events », predicting efficacy, and thus looked for… Moreover, the sequential approach, with new drugs, emphasizes the need of defining the optimal sequence. Thus, because of the lack of molecular biomarkers to date, this predictives secondary effects may help for selecting the therapeutic strategy. PMID:24977449

  19. 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)

  20. 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.)

  1. 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.

  2. Low cdc27 and high securin expression predict short survival for breast cancer patients.

    Science.gov (United States)

    Talvinen, Kati; Karra, Henna; Pitkänen, Reino; Ahonen, Ilmari; Nykänen, Marjukka; Lintunen, Minnamaija; Söderström, Mirva; Kuopio, Teijo; Kronqvist, Pauliina

    2013-10-01

    Cell cycle regulators cdc27 and securin participate in control of the mitotic checkpoint and survey the mitotic spindle to maintain chromosomal integrity. This is achieved by their functions in metaphase-anaphase transition, DNA damage repair, enhancement of mitotic arrest and apoptosis. We report on the roles of cdc27 and securin in aneuploidy and prognosis of breast cancer. The study comprises 429 breast cancer patients with up to 22 years of follow-up. DNA content was determined by image cytometry, and immunopositivity for cdc27 and securin was based on tissue microarrays. An inverse association between cdc27 and securin expression was observed in both image cytometric and immunohistochemical analyses. Low cdc27 and high securin expression identified patients with significant difference in disease outcome. Cdc27 and securin immunoexpression identified patients at risk of early cancer death within five years from diagnosis. In multivariate analysis, the combination of cdc27 and securin immunohistochemistry was the strongest predictor of cancer death after lymph node status. We demonstrate, for the first time in human breast cancer, the prognostic value of cdc27 and securin immunohistochemistry. Cdc27 and securin appear promising biomarkers for applications in predicting disease progression, prognostication of individual patients and potential in anti-mitotic drug development. PMID:23755904

  3. 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

  4. 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

  5. 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

  6. 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.

  7. 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

  8. Nuclear factor, erythroid 2-like 2-associated molecular signature predicts lung cancer survival.

    Science.gov (United States)

    Qian, Zhongqing; Zhou, Tong; Gurguis, Christopher I; Xu, Xiaoyan; Wen, Qing; Lv, Jingzhu; Fang, Fang; Hecker, Louise; Cress, Anne E; Natarajan, Viswanathan; Jacobson, Jeffrey R; Zhang, Donna D; Garcia, Joe G N; Wang, Ting

    2015-01-01

    Nuclear factor, erythroid 2-like 2 (NFE2L2), a transcription factor also known as NF-E2-related factor 2 (Nrf2), is a key cytoprotective gene that regulates critical antioxidant and stress-responsive genes. Nrf2 has been demonstrated to be a promising therapeutic target and useful biomarker in malignant disease. We hypothesized that NFE2L2-mediated gene expression would reflect cancer severity and progression. We conducted a meta-analysis of microarray data for 240 NFE2L2-mediated genes that were enriched in tumor tissues. We then developed a risk scoring system based on NFE2L2 gene expression profiling and designated 50 tumor-associated genes as the NFE2L2-associated molecular signature (NAMS). We tested the relationship between this gene expression signature and both recurrence-free survival and overall survival in lung cancer patients. We find that NAMS predicts clinical outcome in the training cohort and in 12 out of 20 validation cohorts. Cox proportional hazard regressions indicate that NAMS is a robust prognostic gene signature, independent of other clinical and pathological factors including patient age, gender, smoking, gene alteration, MYC level, and cancer stage. NAMS is an excellent predictor of recurrence-free survival and overall survival in human lung cancer. This gene signature represents a promising prognostic biomarker in human lung cancer. PMID:26596768

  9. 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.

  10. DNA demethylation in normal colon tissue predicts predisposition to multiple cancers.

    Science.gov (United States)

    Kamiyama, H; Suzuki, K; Maeda, T; Koizumi, K; Miyaki, Y; Okada, S; Kawamura, Y J; Samuelsson, J K; Alonso, S; Konishi, F; Perucho, M

    2012-11-29

    Some colon cancer (CC) patients present synchronous cancers at diagnosis and others develop metachronous neoplasms, but the risk factors are unclear for non-hereditary CC. We showed previously that global DNA demethylation increased with aging and correlated with genomic damage in CC, and we show now that preferentially associates to CCs with wild-type p53. This study aimed to elucidate the extent of DNA hypomethylation in patients with single and multiple CC, its relationship with aging, and its potential as predictive tool. We compared by real-time methylation-specific PCR the relative demethylation level (RDL) of long interspersed nucleotide element-1 (LINE-1) sequences in matched cancer tissues and non-cancerous colonic mucosa (NCM) from patients with single and multiple right-sided CCs. Although no RDL difference was found in NCM from single CC patients and healthy volunteers (P=0.5), there was more demethylation (higher RDL) in NCM from synchronous cancer patients (P=1.1 × 10(-5)) multiple CCs also were more demethylated than single CCs (P=0.0014). High NCM demethylation was predictive for metachronous neoplasms (P=0.003). In multivariate logistic regression analyses RDL was the only independent predictor for metachronous (P=0.02) and multiple (P=4.9 × 10(-5)) tumors. The higher LINE-1 demethylation in NCM from patients with multiple (synchronous and metachronous) tumors (P=9.6 × 10(-7)) was also very significant in patients with tumors without (P=3.8 × 10(-6)), but not with (P=0.16) microsatellite instability. NCM demethylation increased with aging in patients with single tumors, but decreased in those with multiple tumors. Moreover, the demethylation difference between patients with single vs multiple tumors appeared higher in younger (P=3.6 × 10(-4)) than in older (P=0.0016) patients. These results predict that LINE-1 hypomethylation in NCM can be used as an epigenetic predictive biomarker for multiple CC risk. The stronger association of

  11. 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.

  12. 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

  13. Uji sensitivitas dan spesifisitas perangkat lunak “Prediktor Karies Anak” (The sensitivity and specificity test of software for dental caries prediction in children

    Directory of Open Access Journals (Sweden)

    Quroti A’yun

    2014-03-01

    Full Text Available Background: The prevalence of dental caries in children is high, therefore preventive actions is needed. So far the computer preventive actions is needed. So far the computer software that have been used for caries predictor is cariogram, which determine the condition of teeth and oral mouth. Recently which determine the condition of teeth and oral mouth. Recently and oral mouth. Recently mouth. Recently“Prediktor Karies Anak” (pediatric caries predictor software have been developed not only determine the condition of teeth and software have been developed not only determine the condition of teeth and been developed not only determine the condition of teeth and oral mouth but also child’s behavior, maternal behavior, and the environment. behavior, maternal behavior, and the environment. Purpose: The objective of this study was to examine objective of this study was to examinethe sensitivity, specificity, positive predictive value (PPV and negative predictive value (NPV of "Prediktor Karies Anak" a software for dental caries prediction in children. Methods: This study study was an observational study with cross-sectional plan, carried out on 67 primary school children aged 10-12 years. The research instrument was software of “Prediktor Karies Anak” (pediatric caries of “Prediktor Karies Anak” (pediatric caries predictor and cariogram. The data of this research was the percentage of new caries occurrence and caries risk categorized into the percentage of new caries occurrence and caries risk categorized into high and low, and analyzed with a 2 x 2 table. results: The data of 67 children was analyzed using “Prediktor Karies Anak”software and revealed 38 children had low caries risk and 29 children had high caries. The data then re-analyzed using cariogram software had low caries risk and 29 children had high caries. The data then re-analyzed using cariogram software then re-analyzed using cariogram software showed that 37

  14. 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

  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. Determining if pretreatment PSA doubling time predicts PSA trajectories after radiation therapy for localized prostate cancer

    International Nuclear Information System (INIS)

    Introduction: To determine if pretreatment PSA doubling time (PSA-DT) can predict post-radiation therapy (RT) PSA trajectories for localized prostate cancer. Materials and methods: Three hundred and seventy-five prostate cancer patients treated with external beam RT without androgen deprivation therapy (ADT) were identified with an adequate number of PSA values. We utilized a linear mixed model (LMM) analysis to model longitudinal PSA data sets after definitive treatment. Post-treatment PSA trajectories were allowed to depend on the pre-RT PSA-DT, pre-RT PSA (iPSA), Gleason score (GS), and T-stage. Results: Pre-RT PSA-DT had a borderline impact on predicting the rate of PSA rise after nadir (p = 0.08). For a typical low risk patient (T1, GS ≤ 6, iPSA 10), the predicted PSA-DT post-nadir was 21% shorter for pre-RT PSA-DT 24 month (19 month vs. 24 month). Additional significant predictors of post-RT PSA rate of rise included GS (p < 0.0001), iPSA (p < 0.0001), and T-stage (p = 0.02). Conclusions: We observed a trend between rapidly rising pre-RT PSA and the post-RT post-nadir PSA rise. This effect appeared to be independent of iPSA, GS, or T-stage. The results presented suggest that pretreatment PSA-DT may help predict post-RT PSA trajectories

  17. Predicting Resectability of Pancreatic Head Cancer with Multi-Detector CT. Surgical and Pathologic Correlation

    Directory of Open Access Journals (Sweden)

    Damien Olivié

    2007-11-01

    Full Text Available Context Computed tomography is widely used to pre-operatively evaluate patients with ductal carcinoma of the pancreas. Objective To prospectively evaluate the ability of multi-detector computed tomography to predict resectability of pancreatic head cancer. Patients Ninety-one consecutive patients (53 men, 38 women; mean age, 61 years referred to our department with a diagnosis of cancer of the head of the pancreas underwent a preoperative contrast enhanced triphasic 16- slice multi-detector computed tomography. Sixty-three were considered inoperable because of advanced local disease, metastatic disease, or poor surgical risk. Intervention Of the remaining 28 patients, 23 underwent a Whipple procedure, whereas 5 patients underwent a palliative procedure. Main outcome measures Surgical and pathologic reports were reviewed and compared to CT results. Results Of the 91 patients evaluated, 25% had successful resection of pancreatic head carcinoma; while only 5% had a palliative procedure. When compared to surgical outcome, the positive predictive value of multi-detector computed tomography for resectability was 100%. On the basis of pathologic results, the positive predictive value of multi-detector computed tomography for resectability fell to 83%, Four patients deemed resectable following multi-detector computed tomography had positive margins at pathology. Conclusion The positive predictive value of multi-detector computed tomography for resectable disease is lower when pathologic correlation, as opposed to surgical correlation, is used as the gold standard. Compared to previous studies, there was a lower rate of palliative surgery in our cohort.

  18. 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

  19. 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.

  20. Molecular sampling of prostate cancer: a dilemma for predicting disease progression

    Directory of Open Access Journals (Sweden)

    Mucci Lorelei A

    2010-03-01

    Full Text Available Abstract Background Current prostate cancer prognostic models are based on pre-treatment prostate specific antigen (PSA levels, biopsy Gleason score, and clinical staging but in practice are inadequate to accurately predict disease progression. Hence, we sought to develop a molecular panel for prostate cancer progression by reasoning that molecular profiles might further improve current clinical models. Methods We analyzed a Swedish Watchful Waiting cohort with up to 30 years of clinical follow up using a novel method for gene expression profiling. This cDNA-mediated annealing, selection, ligation, and extension (DASL method enabled the use of formalin-fixed paraffin-embedded transurethral resection of prostate (TURP samples taken at the time of the initial diagnosis. We determined the expression profiles of 6100 genes for 281 men divided in two extreme groups: men who died of prostate cancer and men who survived more than 10 years without metastases (lethals and indolents, respectively. Several statistical and machine learning models using clinical and molecular features were evaluated for their ability to distinguish lethal from indolent cases. Results Surprisingly, none of the predictive models using molecular profiles significantly improved over models using clinical variables only. Additional computational analysis confirmed that molecular heterogeneity within both the lethal and indolent classes is widespread in prostate cancer as compared to other types of tumors. Conclusions The determination of the molecularly dominant tumor nodule may be limited by sampling at time of initial diagnosis, may not be present at time of initial diagnosis, or may occur as the disease progresses making the development of molecular biomarkers for prostate cancer progression challenging.

  1. 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.

  2. 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,...

  3. 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...

  4. A new histological therapeutic classification system to predict eradicated and residual lymph nodes in breast cancer after neoadjuvant chemotherapy

    OpenAIRE

    MOROHASHI, SATOKO; YOSHIZAWA, TADASHI; SEINO, HIROKO; HIRAI, HIDEAKI; HAGA, TOSHIHIRO; OTA, RIE; Wu, Yunyan; Yoshida, Eri; HAKAMADA, KENICHI; Kijima, Hiroshi

    2016-01-01

    The indication for neoadjuvant chemotherapy (NAC) has recently broadened to include its use in the treatment of initial stage breast cancer. Axillary lymph node metastasis after NAC in breast cancer is a poor prognostic factor. Thus, the prediction of lymph node metastasis is important to estimate the prognosis of breast cancer patients after NAC. Therefore, we focused on residual carcinoma patterns of primary breast tumors after NAC and examined the correlation between the patterns and lymph...

  5. 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,...

  6. A Lymph Node Ratio of 10% Is Predictive of Survival in Stage III Colon Cancer: A French Regional Study

    OpenAIRE

    Sabbagh, Charles; Mauvais, François; Cosse, Cyril; Rebibo, Lionel; Joly, Jean-Paul; Dromer, Didier; Aubert, Christine; Carton, Sophie; Dron, Bernard; Dadamessi, Innocenti; Maes, Bernard; Perrier, Guillaume; Manaouil, David; Fontaine, Jean-François; Gozy, Michel

    2014-01-01

    Lymph node ratio (LNR) (positive lymph nodes/sampled lymph nodes) is predictive of survival in colon cancer. The aim of the present study was to validate the LNR as a prognostic factor and to determine the optimum LNR cutoff for distinguishing between “good prognosis” and “poor prognosis” colon cancer patients.

  7. Prediction of axillary lymph node metastases in breast cancer patients based on pathologic information of the primary tumor

    OpenAIRE

    Wu, Jia-Long; Tseng, Hsin-Shun; Yang, Li-Heng; Wu, Hwa-Koon; Kuo, Shou-Jen; Chen, Shou-Tung; Chen, Dar-Ren

    2014-01-01

    Background Axillary lymph nodes (ALN) are the most commonly involved site of disease in breast cancer that has spread outside the primary lesion. Although sentinel node biopsy is a reliable way to manage ALN, there are still no good methods of predicting ALN status before surgery. Since morbidity in breast cancer surgery is predominantly related to ALN dissection, predictive models for lymph node involvement may provide a way to alert the surgeon in subgroups of patients. Material/Methods A t...

  8. Circulating soluble urokinase plasminogen activator receptor predicts cancer, cardiovascular disease, diabetes and mortality in the general population

    DEFF Research Database (Denmark)

    Eugen-Olsen, J; Andersen, O; Linneberg, A; Ladelund, S; Hansen T, W; Langkilde, A; Petersen, Janne; Pielak, T; Møller, N. L.; Jeppesen, J; Lyngbæk, S; Fenger, M; Olsen M, H; Borch-Johnsen, K; Jørgensen, Torben; Haugaard S, B; Hildebrandt, P. R.

    2010-01-01

    Low-grade inflammation is thought to contribute to the development of cardiovascular disease (CVD), type-2 diabetes mellitus (T2D), cancer and mortality. Biomarkers of inflammation may aid in risk prediction and enable early intervention and prevention of disease.......Low-grade inflammation is thought to contribute to the development of cardiovascular disease (CVD), type-2 diabetes mellitus (T2D), cancer and mortality. Biomarkers of inflammation may aid in risk prediction and enable early intervention and prevention of disease....

  9. 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...

  10. Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models

    Directory of Open Access Journals (Sweden)

    Hu Yun-tao

    2009-09-01

    Full Text Available Abstract Background In recent years, artificial neural network is advocated in modeling complex multivariable relationships due to its ability of fault tolerance; while decision tree of data mining technique was recommended because of its richness of classification arithmetic rules and appeal of visibility. The aim of our research was to compare the performance of ANN and decision tree models in predicting hospital charges on gastric cancer patients. Methods Data about hospital charges on 1008 gastric cancer patients and related demographic information were collected from the First Affiliated Hospital of Anhui Medical University from 2005 to 2007 and preprocessed firstly to select pertinent input variables. Then artificial neural network (ANN and decision tree models, using same hospital charge output variable and same input variables, were applied to compare the predictive abilities in terms of mean absolute errors and linear correlation coefficients for the training and test datasets. The transfer function in ANN model was sigmoid with 1 hidden layer and three hidden nodes. Results After preprocess of the data, 12 variables were selected and used as input variables in two types of models. For both the training dataset and the test dataset, mean absolute errors of ANN model were lower than those of decision tree model (1819.197 vs. 2782.423, 1162.279 vs. 3424.608 and linear correlation coefficients of the former model were higher than those of the latter (0.955 vs. 0.866, 0.987 vs. 0.806. The predictive ability and adaptive capacity of ANN model were better than those of decision tree model. Conclusion ANN model performed better in predicting hospital charges of gastric cancer patients of China than did decision tree model.

  11. Esophageal Stenosis Associated With Tumor Regression in Radiotherapy for Esophageal Cancer: Frequency and Prediction

    International Nuclear Information System (INIS)

    Purpose: To determine clinical factors for predicting the frequency and severity of esophageal stenosis associated with tumor regression in radiotherapy for esophageal cancer. Methods and Materials: The study group consisted of 109 patients with esophageal cancer of T1–4 and Stage I–III who were treated with definitive radiotherapy and achieved a complete response of their primary lesion at Kyushu University Hospital between January 1998 and December 2007. Esophageal stenosis was evaluated using esophagographic images within 3 months after completion of radiotherapy. We investigated the correlation between esophageal stenosis after radiotherapy and each of the clinical factors with regard to tumors and therapy. For validation of the correlative factors for esophageal stenosis, an artificial neural network was used to predict the esophageal stenotic ratio. Results: Esophageal stenosis tended to be more severe and more frequent in T3–4 cases than in T1–2 cases. Esophageal stenosis in cases with full circumference involvement tended to be more severe and more frequent than that in cases without full circumference involvement. Increases in wall thickness tended to be associated with increases in esophageal stenosis severity and frequency. In the multivariate analysis, T stage, extent of involved circumference, and wall thickness of the tumor region were significantly correlated to esophageal stenosis (p = 0.031, p < 0.0001, and p = 0.0011, respectively). The esophageal stenotic ratio predicted by the artificial neural network, which learned these three factors, was significantly correlated to the actual observed stenotic ratio, with a correlation coefficient of 0.864 (p < 0.001). Conclusion: Our study suggested that T stage, extent of involved circumference, and esophageal wall thickness of the tumor region were useful to predict the frequency and severity of esophageal stenosis associated with tumor regression in radiotherapy for esophageal cancer.

  12. Noncoding RNAs as potential biomarkers to predict the outcome in pancreatic cancer

    Directory of Open Access Journals (Sweden)

    Jin K

    2015-02-01

    Full Text Available Kaizhou Jin,1–3,* Guopei Luo,1–3,* Zhiwen Xiao,1–3 Zuqiang Liu,1–3 Chen Liu,1–3 Shunrong Ji,1–3 Jin Xu,1–3 Liang Liu,1–3 Jiang Long,1–3 Quanxing Ni,1–3 Xianjun Yu1–3 1Department of Pancreatic and Hepatobiliary Surgery, Fudan University Shanghai Cancer Center, 2Department of Oncology, Shanghai Medical College, Fudan University, 3Pancreatic Cancer Institute, Fudan University, Shanghai, People’s Republic of China *These authors contributed equally to this work Abstract: Pancreatic ductal adenocarcinoma (PDAC, a common digestive system cancer, is highly malignant and has a poor disease outcome. Currently, all available examination and detection methods cannot accurately predict the clinical outcome. Therefore, it is extremely important to identify novel molecular biomarkers for personalized medication and to significantly improve the overall outcome. The “noncoding RNAs” (ncRNAs are a group of RNAs that do not code for proteins, and they are categorized as structural RNAs and regulatory RNAs. It has been shown that microRNAs and long ncRNAs function as regulatory RNAs to affect the progression of various diseases. Many studies have confirmed a role for ncRNAs in the progression of PDAC during the last few years. Because of the significant role of ncRNAs in PDAC, ncRNA profiling may be used to predict PDAC outcome with high accuracy. This review comprehensively analyzes the value of ncRNAs as potential biomarkers to predict the outcome in PDAC and the possible mechanisms thereof. Keywords: pancreatic ductal adenocarcinoma, microRNA, long noncoding RNA, outcome prediction

  13. Esophageal Stenosis Associated With Tumor Regression in Radiotherapy for Esophageal Cancer: Frequency and Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Atsumi, Kazushige [Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan); Shioyama, Yoshiyuki, E-mail: shioyama@radiol.med.kyushu-u.ac.jp [Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan); Arimura, Hidetaka [Department of Health Sciences, Kyushu University, Fukuoka (Japan); Terashima, Kotaro [Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan); Matsuki, Takaomi [Department of Health Sciences, Kyushu University, Fukuoka (Japan); Ohga, Saiji; Yoshitake, Tadamasa; Nonoshita, Takeshi; Tsurumaru, Daisuke; Ohnishi, Kayoko; Asai, Kaori; Matsumoto, Keiji [Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan); Nakamura, Katsumasa [Department of Radiology, Kyushu University Hospital at Beppu, Oita (Japan); Honda, Hiroshi [Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan)

    2012-04-01

    Purpose: To determine clinical factors for predicting the frequency and severity of esophageal stenosis associated with tumor regression in radiotherapy for esophageal cancer. Methods and Materials: The study group consisted of 109 patients with esophageal cancer of T1-4 and Stage I-III who were treated with definitive radiotherapy and achieved a complete response of their primary lesion at Kyushu University Hospital between January 1998 and December 2007. Esophageal stenosis was evaluated using esophagographic images within 3 months after completion of radiotherapy. We investigated the correlation between esophageal stenosis after radiotherapy and each of the clinical factors with regard to tumors and therapy. For validation of the correlative factors for esophageal stenosis, an artificial neural network was used to predict the esophageal stenotic ratio. Results: Esophageal stenosis tended to be more severe and more frequent in T3-4 cases than in T1-2 cases. Esophageal stenosis in cases with full circumference involvement tended to be more severe and more frequent than that in cases without full circumference involvement. Increases in wall thickness tended to be associated with increases in esophageal stenosis severity and frequency. In the multivariate analysis, T stage, extent of involved circumference, and wall thickness of the tumor region were significantly correlated to esophageal stenosis (p = 0.031, p < 0.0001, and p = 0.0011, respectively). The esophageal stenotic ratio predicted by the artificial neural network, which learned these three factors, was significantly correlated to the actual observed stenotic ratio, with a correlation coefficient of 0.864 (p < 0.001). Conclusion: Our study suggested that T stage, extent of involved circumference, and esophageal wall thickness of the tumor region were useful to predict the frequency and severity of esophageal stenosis associated with tumor regression in radiotherapy for esophageal cancer.

  14. 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

  15. 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

  16. 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

  17. Circulating cell death products predict clinical outcome of colorectal cancer patients

    International Nuclear Information System (INIS)

    Tumor cell death generates products that can be measured in the circulation of cancer patients. CK18-Asp396 (M30 antigen) is a caspase-degraded product of cytokeratin 18 (CK18), produced by apoptotic epithelial cells, and is elevated in breast and lung cancer patients. We determined the CK18-Asp396 and total CK18 levels in plasma of 49 colorectal cancer patients, before and after surgical resection of the tumor, by ELISA. Correlations with patient and tumor characteristics were determined by Kruskal-Wallis H and Mann-Whitney U tests. Disease-free survival was determined using Kaplan-Meier methodology with Log Rank tests, and univariate and multivariate Cox proportional hazard analysis. Plasma CK18-Asp396 and total CK18 levels in colorectal cancer patients were related to disease stage and tumor diameter, and were predictive of disease-free survival, independent of disease-stage, with hazard ratios (HR) of patients with high levels (> median) compared to those with low levels (≤ median) of 3.58 (95% CI: 1.17–11.02) and 3.58 (95% CI: 0.97–7.71), respectively. The CK18-Asp396/CK18 ratio, which decreased with tumor progression, was also predictive of disease-free survival, with a low ratio (≤ median) associated with worse disease-free survival: HR 2.78 (95% CI: 1.06–7.19). Remarkably, the plasma CK18-Asp396 and total CK18 levels after surgical removal of the tumor were also predictive of disease-free survival, with patients with high levels having a HR of 3.78 (95% CI: 0.77–18.50) and 4.12 (95% CI: 0.84–20.34), respectively, indicating that these parameters can be used also to monitor patients after surgery. CK18-Asp396 and total CK18 levels in the circulation of colorectal cancer patients are predictive of tumor progression and prognosis and might be helpful for treatment selection and monitoring of these patients

  18. Predicting the continuous values of breast cancer relapse time by type-2 fuzzy logic system

    International Nuclear Information System (INIS)

    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.

  19. 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.

  20. Predictive Factors Study Of Rectal Bleeding Radical Radiotherapy In Prostate Cancer

    International Nuclear Information System (INIS)

    Full text: Objective: To determine clinical, para clinical or to predict dosimetric the occurrence of rectal bleeding after radical radiotherapy as primary treatment prostate cancer in patients treated at the National Cancer Institute (Inca) dimensional external beam radiation. Materials and Methods: From July 2008 to July 2011 132 patients were recruited, 86 of which met followed for 12 months. Side effect was recorded gastrointestinal track at different times of the patient with classifications RTOG / EORTC ( Radiation Therapy Oncology Group / European Organization for Research and Treatment of Cancer) and SOMA / LENT also used a questionnaire specifically built and tested by the Italian cooperative group. results were correlated with clinical parameters (PSA, Gleason score, clinical T, class risk, hypertension, and diabetes) and dosimetric ( treatment volume, volume rectum, total dose, maximum dose to the rectum mean dose to the rectum) to assess the correlation thereof with the occurrence of rectal bleeding. Results: In a cut to 12 months follow-up, there is a relationship between the appearance bleeding with time. The mean dose to the rectum and the initial PSA showed significance in correlation with the occurrence of rectal bleeding, with a p of 0.01 and 0.54 respectively. Conclusions: The rectal side effect is one of the major side effects both acute and chronic prostate radiotherapy in the present study has failed to demonstrate a correlation between two factors predictive identified as potentially priori significant the occurrence of rectal bleeding, as the initial PSA and the mean dose to the rectum

  1. Predictive Factors of Biliary Tract Cancer in Anomalous Union of the Pancreaticobiliary Duct.

    Science.gov (United States)

    Park, Jin-Seok; Song, Tae Jun; Park, Tae Young; Oh, Dongwook; Lee, Hyun Kyo; Park, Do Hyun; Lee, Sang Soo; Seo, Dong Wan; Lee, Sung Koo; Kim, Myung-Hwan

    2016-05-01

    The assessment of malignancies associated with anomalous union of the pancreaticobiliary duct (AUPBD) is essential for the design of appropriate treatment strategies. The aim of the present study is to measure the incidence of AUPBD-related pancreaticobiliary malignancy and to identify predictive factors. This retrospective cohort study included cases of 229 patients with AUPBD between January 1999 and December 2013. The impact of bile duct dilatation on the incidence of AUPBD-related pancreaticobiliary disease was measured, and predictive factors were evaluated.Among 229 patients with AUPBD, 152 had common bile duct dilatation (≥10 mm) (dilated group) and 77 did not (pancreatic enzymes refluxed in the bile duct were associated with occurrence of biliary tract cancers. In multivariate analysis, age ≥45 years (odds ratio [OR] 1.042, 95% confidence interval [CI] 1.011-1.073, P < 0.05), P-C type (OR 3.327, 95% CI 1.031-10.740, P < 0.05), and a high level of biliary lipase (OR 4.132, 95% CI 1.420-12.021, P < 0.05) showed a significant association with AUPBD-related biliary tract cancer.Intrahepatic cholangiocarcinoma may occur more frequently in AUPBD patients without bile duct dilatation. Age ≥45 years, P-C type, and biliary lipase level ≥45,000 IU/L are significantly associated with AUPBD-related biliary tract cancer. PMID:27196455

  2. 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.)

  3. 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.)

  4. Prediction of lung cancer based on serum biomarkers by gene expression programming methods.

    Science.gov (United States)

    Yu, Zhuang; Chen, Xiao-Zheng; Cui, Lian-Hua; Si, Hong-Zong; Lu, Hai-Jiao; Liu, Shi-Hai

    2014-01-01

    In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool - gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are frequently- used lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer. PMID:25422226

  5. Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction

    Science.gov (United States)

    Yip, Stephen S. F.; Coroller, Thibaud P.; Sanford, Nina N.; Huynh, Elizabeth; Mamon, Harvey; Aerts, Hugo J. W. L.; Berbeco, Ross I.

    2016-01-01

    Change in PET-based textural features has shown promise in predicting cancer response to treatment. However, contouring tumour volumes on longitudinal scans is time-consuming. This study investigated the usefulness of contour propagation in texture analysis for the purpose of pathologic response prediction in esophageal cancer. Forty-five esophageal cancer patients underwent PET/CT scans before and after chemo-radiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumour ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. PET images were converted into 256 discrete values. Co-occurrence, run-length, and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs from different algorithms were compared using Dice similarity index (DSI). Contours propagated by the fast-demons, fast-free-form and rigid algorithms did not fully capture the high FDG uptake regions of tumours. Fast-demons propagated ROIs had the least agreement with other contours (DSI  =  58%). Moderate to substantial overlap were found in the ROIs propagated by all other algorithms (DSI  =  69%-79%). Rigidly propagated ROIs with co-occurrence texture failed to significantly differentiate between responders and non-responders (AUC  =  0.58, q-value  =  0.33), while the differentiation was significant with other textures (AUC  =  0.71‒0.73, p  <  0.009). Among the deformable algorithms, fast-demons (AUC  =  0.68‒0.70, q-value  <  0.03) and fast-free-form (AUC  =  0.69‒0.74, q-value  <  0.04) were the least predictive. ROIs propagated by all other

  6. Protein structure prediction software generate two different sets of conformations. Or the study of unfolded self-avoiding walks

    OpenAIRE

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

    2013-01-01

    Self-avoiding walks (SAW) are the source of very difficult problems in probabilities and enumerative combinatorics. They are also of great interest as they are, for instance, the basis of protein structure prediction in bioinformatics. Authors of this article have previously shown that, depending on the prediction algorithm, the sets of obtained conformations differ: all the self-avoiding walks can be reached using stretching-based algorithms whereas only the folded SAWs can be attained with ...

  7. 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

  8. Elevated MED28 expression predicts poor outcome in women with breast cancer

    Directory of Open Access Journals (Sweden)

    Horvath Steve

    2010-06-01

    Full Text Available Abstract Background 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. Methods 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. Results 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. Conclusions 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.

  9. Upregulation of CDK7 in gastric cancer cell promotes tumor cell proliferation and predicts poor prognosis.

    Science.gov (United States)

    Wang, Qiuhong; Li, Manhua; Zhang, Xunlei; Huang, Hua; Huang, Jianfei; Ke, Jing; Ding, Haifang; Xiao, Jinzhang; Shan, Xiaohang; Liu, Qingqing; Bao, Bojun; Yang, Lei

    2016-06-01

    CDK7 has been known as a component of CDK activating kinase (CAK) complex, the complex was composed of CDK7, Cyclin H and RING finger protein Mat1 that contribute to cell cycle progression by phosphorylating other CDKs. In addition, the complex is also an essential component of general transcription factor TFIIH which controls transcription via activating RNA polymerase II by serines 5 and 7 phosphorylation of the carboxyl-terminal domain (CTD) of its largest subunit. However, the role of CDK7 in the pathogenesis of gastric cancer has not been identified. Our study showed that CDK7 was significantly upregulated and positively correlated with tumor grade, infiltration depth, lymph node, Ki-67, and predicted poor prognosis in 173 gastric cancer specimens by immunohistochemistrical analyses. Furthermore, in vitro results indicated that CDK7 promoted proliferation of gastric cancer cells by CCK8, clone formation analyses and flow cytometric analyses, while CDK7 knockdown led to decreased cell proliferation. Our study will provide a theoretical basis for the study of CDK7 in gastric cancer. PMID:27155449

  10. 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

  11. Prognostic and predictive values of long non-coding RNA LINC00472 in breast cancer.

    Science.gov (United States)

    Shen, Yi; Katsaros, Dionyssios; Loo, Lenora W M; Hernandez, Brenda Y; Chong, Clayton; Canuto, Emilie Marion; Biglia, Nicoletta; Lu, Lingeng; Risch, Harvey; Chu, Wen-Ming; Yu, Herbert

    2015-04-20

    LINC00472 is a novel long intergenic non-coding RNA. We evaluated LINC00472 expression in breast tumor samples using RT-qPCR, performed a meta-analysis of over 20 microarray datasets from the Gene Expression Omnibus (GEO) database, and investigated the effect of LINC00472 expression on cell proliferation and migration in breast cancer cells transfected with a LINC00472-expressing vector. Our qPCR results showed that high LINC00472 expression was associated with less aggressive breast tumors and more favorable disease outcomes. Patients with high expression of LINC00472 had significantly reduced risk of relapse and death compared to those with low expression. Patients with high LINC00472 expression also had better responses to adjuvant chemo- or hormonal therapy than did patients with low expression. Results of meta-analysis on multiple studies from the GEO database were in agreement with the findings of our study. High LINC00472 was also associated with favorable molecular subtypes, Luminal A or normal-like tumors. Cell culture experiments showed that up-regulation of LINC00472 expression could suppress breast cancer cell proliferation and migration. Collectively, our clinical and in vitro studies suggest that LINC00472 is a tumor suppressor in breast cancer. Evaluating this long non-coding RNA in breast tumors may have prognostic and predictive value in the clinical management of breast cancer. PMID:25865225

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. Survival of patients after resection for lung cancer. Predictive value of staging by CT vs thoracotomy

    International Nuclear Information System (INIS)

    The predictive value of staging of primary lung cancer by CT and thoracotomy with respect to survival was assessed in a series of 151 consecutive patients. The new international staging system for lung cancer was used, with an additional indeterminate stage employed for cases in which a definite classification was impossible by CT. The survival rate curves for the stage groups assessed at CT and thoracotomy showed moderate to good parallelism. The patients with tumor stage I at thoracotomy but indeterminate or IIIa at CT had a significantly lower survival rate than those scored stage I at both. It was concluded that a sign of tumor spread obtained at any of the investigations should lead to an active approach, increasing the radicality of surgery or omitting noncurative operations. (orig.)

  18. Survival of patients after resection for lung cancer. Predictive value of staging by CT vs thoracotomy

    Energy Technology Data Exchange (ETDEWEB)

    Laehde, S. [Univ. Central Hospital, Oulu (Finland). Dept. of Diagnostic Radiology; Rainio, P. [Univ. Central Hospital, Oulu (Finland). Dept. of Surgery; Bloigu, R. [Oulu Univ. (Finland). Dept. of Public Science and General Practice

    1995-09-01

    The predictive value of staging of primary lung cancer by CT and thoracotomy with respect to survival was assessed in a series of 151 consecutive patients. The new international staging system for lung cancer was used, with an additional indeterminate stage employed for cases in which a definite classification was impossible by CT. The survival rate curves for the stage groups assessed at CT and thoracotomy showed moderate to good parallelism. The patients with tumor stage I at thoracotomy but indeterminate or IIIa at CT had a significantly lower survival rate than those scored stage I at both. It was concluded that a sign of tumor spread obtained at any of the investigations should lead to an active approach, increasing the radicality of surgery or omitting noncurative operations. (orig.).

  19. Metabolic response at repeat PET/CT predicts pathological response to neoadjuvant chemotherapy in oesophageal cancer

    International Nuclear Information System (INIS)

    Reports have suggested that a reduction in tumour 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography (PET) examination during or after neoadjuvant chemotherapy may predict pathological response in oesophageal cancer. Our aim was to determine whether metabolic response predicts pathological response to a standardised neoadjuvant chemotherapy regimen within a prospective clinical trial. Consecutive patients staged with potentially curable oesophageal cancer who underwent treatment within a non-randomised clinical trial were included. A standardised chemotherapy regimen (two cycles of oxaliplatin and 5-fluorouracil) was used. PET/CT was performed before chemotherapy and repeated 24-28 days after the start of cycle 2. Forty-eight subjects were included: mean age 65 years; 37 male. Using the median percentage reduction in SUVmax (42%) to define metabolic response, pathological response was seen in 71% of metabolic responders (17/24) compared with 33% of non-responders (8/24; P = 0.009, sensitivity 68%, specificity 70%). Pathological response was seen in 81% of subjects with a complete metabolic response (13/16) compared with 38% of those with a less than complete response (12/32; P = 0.0042, sensitivity 52%, specificity 87%). There was no significant histology-based effect. There was a significant association between metabolic response and pathological response; however, accuracy in predicting pathological response was relatively low. (orig.)

  20. 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

  1. 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

  2. Prediction of pathological stage in prostate cancer through the percentage of involved fragments upon biopsy

    Directory of Open Access Journals (Sweden)

    Marcos F. Dall'oglio

    2005-10-01

    Full Text Available INTRODUCTION: The need for defining the extension of disease in patients undergoing radical prostatectomy due to prostate adenocarcinoma is a relevant factor cure in such individuals. In order to identify a new independent preoperative factor for predicting the extension of prostate cancer, we assessed the role of the percentage of positive fragments upon biopsy. MATERIALS AND METHODS: A retrospective study compared the percentage of positive fragments on biopsy with the extension of disease as defined by the pathological examination of the surgical specimen from 898 patients undergoing radical prostatectomy due to clinically localized prostate cancer. RESULTS: On the univariate analysis, the percentage of positive fragments on biopsy showed a statistical significance for predicting confined disease (p < 0.001, which was found in 66.7% of the cases under study. Additionally, we observed that the total number of removed fragments exerts no influence on the extension of the disease (p = 0.567. CONCLUSION: the percentage of positive fragments is an independent factor for predicting the pathological stage of prostate adenocarcinoma, and the number of removed fragments is not related to the extension of the disease.

  3. 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.

  4. 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...

  5. 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.

  6. Polymorphism at 19q13.41 predicts breast cancer survival specifically after endocrine therapy

    Science.gov (United States)

    Khan, Sofia; Fagerholm, Rainer; Rafiq, Sajjad; Tapper, William; Aittomäki, Kristiina; Liu, Jianjun

    2015-01-01

    Purpose Although most estrogen receptor (ER)-positive breast cancer patients benefit from endocrine therapies, a significant proportion do not. Our aim was to identify inherited genetic variations that might predict survival among patients receiving adjuvant endocrine therapies. Experimental Design We performed a meta-analysis of two genome-wide studies; Helsinki Breast Cancer Study, 805 patients, with 240 receiving endocrine therapy and Prospective study of Outcomes in Sporadic versus Hereditary breast cancer, 536 patients, with 155 endocrine therapy-patients, evaluating 486,478 single nucleotide polymorphisms (SNPs). The top four associations from the endocrine treatment subgroup were further investigated in two independent datasets totalling 5011 patients, with 3485 receiving endocrine therapy. Results A meta-analysis identified a common SNP rs8113308, mapped to 19q13.41, associating with reduced survival among endocrine treated patients (hazard ratio (HR) 1.69, 95% confidence interval (CI) 1.37-2.07, P = 6.34 ×10−7) and improved survival among ER-negative patients, with a similar trend in ER-positive cases not receiving endocrine therapy. In a multivariate analysis adjusted for conventional prognostic factors, we found a significant interaction between the rs8113308 and endocrine treatment indicating a predictive, treatment-specific effect of the SNP rs8113308 on breast cancer survival, with the per-allele HR for interaction 2.16 (95% CI 1.30 – 3.60, Pinteraction = 0.003) and HR=7.77 (95% CI 0.93 – 64.71) for the homozygous genotype carriers. A biological rationale is suggested by in silico functional analyses. Conclusions Our findings suggest carrying the rs8113308 rare allele may identify patients who will not benefit from adjuvant endocrine treatment. PMID:25964295

  7. 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.

  8. 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.

  9. SNPs in transporter and metabolizing genes as predictive markers for oxaliplatin treatment in colorectal cancer patients.

    Science.gov (United States)

    Kap, Elisabeth J; Seibold, Petra; Scherer, Dominique; Habermann, Nina; Balavarca, Yesilda; Jansen, Lina; Zucknick, Manuela; Becker, Natalia; Hoffmeister, Michael; Ulrich, Alexis; Benner, Axel; Ulrich, Cornelia M; Burwinkel, Barbara; Brenner, Hermann; Chang-Claude, Jenny

    2016-06-15

    Oxaliplatin is frequently used as part of a chemotherapeutic regimen with 5-fluorouracil in the treatment of colorectal cancer (CRC). The cellular availability of oxaliplatin is dependent on metabolic and transporter enzymes. Variants in genes encoding these enzymes may cause variation in response to oxaliplatin and could be potential predictive markers. Therefore, we used a two-step procedure to comprehensively investigate 1,444 single nucleotide polymorphisms (SNPs) from these pathways for their potential as predictive markers for oxaliplatin treatment, using 623 stage II-IV CRC patients (of whom 201 patients received oxaliplatin) from a German prospective patient cohort treated with adjuvant or palliative chemotherapy. First, all genes were screened using the global test that evaluated SNP*oxaliplatin interaction terms per gene. Second, one model was created by backward elimination on all SNP*oxaliplatin interactions of the selected genes. The statistical procedure was evaluated using bootstrap analyses. Nine genes differentially associated with overall survival according to oxaliplatin treatment (unadjusted p values analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. If confirmed, inclusion of these variants in a predictive test could identify patients who are more likely to benefit from treatment with oxaliplatin. PMID:26835885

  10. 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.)

  11. Predictive biomarkers for personalised anti-cancer drug use: discovery to clinical implementation.

    Science.gov (United States)

    Alymani, Nayef A; Smith, Murray D; Williams, David J; Petty, Russell D

    2010-03-01

    A priority translational research objective in cancer medicine is the discovery of novel therapeutic targets for solid tumours. Ideally, co-discovery of predictive biomarkers occurs in parallel to facilitate clinical development of agents and ultimately personalise clinical use. However, the identification of clinically useful predictive biomarkers for solid tumours has proven challenging with many initially promising biomarkers failing to translate into clinically useful applications. In particular, the 'failure' of a predictive biomarker has often only become apparent at a relatively late stage in investigation. Recently, the field has recognised the need to develop a robust clinical biomarker development methodology to facilitate the process. This review discusses the recent progress in this area focusing on the key stages in the biomarker development process: discovery, validation, qualification and implementation. Concentrating on predictive biomarkers for selecting systemic therapies for individual patients in the clinic, the advances and progress in each of these stages in biomarker development are outlined and the key remaining challenges are discussed. Specific examples are discussed to illustrate the challenges identified and how they have been addressed. Overall, we find that significant progress has been made towards a formalised biomarker developmental process. This holds considerable promise for facilitating the translation of predictive biomarkers from discovery to clinical implementation. Further enhancements could eventually be found through alignment with regulatory processes. PMID:20138504

  12. 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.)

  13. 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

  14. Combined-modality treatment and organ preservation in bladder cancer. Do molecular markers predict outcome?

    International Nuclear Information System (INIS)

    Purpose: in invasive bladder cancer, several groups have reported the value of organ preservation by a combined-treatment approach, including transurethral resection (TUR-BT) and radiochemotherapy (RCT). As more experience is acquired with this organ-sparing treatment, patient selection needs to be optimized. Clinical factors are limited in their potential to identify patients most likely to respond to RCT, thus, additional molecular markers for predicting treatment response of individual lesions are sorely needed. Patients and methods: the apoptotic index (AI) and Ki-67 index were evaluated by immunohistochemistry on pretreatment biopsies of 134 patients treated for bladder cancer by TUR-BT and RCT. Expression of each marker as well as clinicopathologic factors were then correlated with initial response, local control and cancer-specific survival with preserved bladder in univariate and multivariate analysis. Results: the median AI for all patients was 1.5% (range 0.2-7.4%). The percentage of Ki-67-positive cells in the tumors ranged from 0.2% to 85% with a median of 14.2%. A significant correlation was found for AI and tumor differentiation (G1/2: AI = 1.3% vs. G3/4: AI = 1.6%; p = 0.01). A complete response at restaging TUR-BT was achieved in 76% of patients. Factors predictive of complete response included T-category (p < 0.0001), resection status (p = 0.02), lymphovascular invasion (p = 0.01), and Ki-67 index (p = 0.02). For local control, AI (p = 0.04) and Ki-67 index (p = 0.05) as well as T-category (p = 0.005), R-status (p = 0.05), and lymphatic vessel invasion (p = 0.05) reached statistical significance. Out of the molecular markers only high Ki-67 levels were associated to cause-specific survival with preserved bladder. On multivariate analysis, T-category was the strongest independent factor for initial response, local control and cancer-specific survival with preserved bladder. Conclusion: The indices of pretreatment apoptosis and Ki-67 predict a

  15. 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.

  16. 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.

  17. 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.

  18. Systematic review and meta-analysis of tumor biomarkers in predicting prognosis in esophageal cancer

    International Nuclear Information System (INIS)

    Esophageal cancer (EC) is a frequently occurring cancer with poor prognosis despite combined therapeutic strategies. Many biomarkers have been proposed as predictors of adverse events. We sought to assess the prognostic value of biomarkers in predicting the overall survival of esophageal cancer and to help guide personalized cancer treatment to give patients the best chance at remission. We conducted a systematic review and meta-analysis of the published literature to summarize evidence for the discriminatory ability of prognostic biomarkers for esophageal cancer. Relevant literature was identified using the PubMed database on April 11, 2012, and conformed to the REMARK criteria. The primary endpoint was overall survival and data were synthesized with hazard ratios (HRs). We included 109 studies, exploring 13 different biomarkers, which were subjected to quantitative meta-analysis. Promising markers that emerged for the prediction of overall survival in esophageal squamous cell cancer included VEGF (18 eligible studies, n = 1476, HR = 1.85, 95% CI, 1.55-2.21), cyclin D1 (12 eligible studies, n = 1476, HR = 1.82, 95% CI, 1.50-2.20), Ki-67 (3 eligible studies, n = 308, HR = 1.11, 95% CI, 0.70-1.78) and squamous cell carcinoma antigen (5 eligible studies, n = 700, HR = 1.28, 95% CI, 0.97-1.69); prognostic markers for esophageal adenocarcinoma included COX-2 (2 eligible studies, n = 235, HR = 3.06, 95% CI, 2.01-4.65) and HER-2 (3 eligible studies, n = 291, HR = 2.15, 95% CI, 1.39-3.33); prognostic markers for uncategorized ECs included p21 (9 eligible studies, n = 858, HR = 1.27, 95% CI, 0.75-2.16), p53 (31 eligible studies, n = 2851, HR = 1.34, 95% CI, 1.21-1.48), CRP (8 eligible studies, n = 1382, HR = 2.65, 95% CI, 1.64-4.27) and hemoglobin (5 eligible studies, n = 544, HR = 0.91, 95% CI, 0.83-1.00). Although some modest bias cannot be excluded, this review supports the involvement of biomarkers to be associated with EC overall survival

  19. 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.

  20. 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

  1. Transcription factor E2F3 overexpressed in prostate cancer independently predicts clinical outcome.

    Science.gov (United States)

    Foster, Christopher S; Falconer, Alison; Dodson, Andrew R; Norman, Andrew R; Dennis, Nening; Fletcher, Anne; Southgate, Christine; Dowe, Anna; Dearnaley, David; Jhavar, Sameer; Eeles, Rosalind; Feber, Andrew; Cooper, Colin S

    2004-08-01

    E2F transcription factors, including E2F3, directly modulate expression of EZH2. Recently, overexpression of the EZH2 gene has been implicated in the development of human prostate cancer. In tissue microrarray studies we now show that expression of high levels of nuclear E2F3 occurs in a high proportion (98/147, 67%) of human prostate cancers, but is a rare event in non-neoplastic prostatic epithelium suggesting a role for E2F3 overexpression in prostate carcinogenesis. Patients with prostate cancer exhibiting immunohistochemically detectable nuclear E2F3 expression have poorer overall survival (P=0.0022) and cause-specific survival (P=0.0047) than patients without detectable E2F3 expression. When patients are stratified according to the maximum percentage of E2F3-positive nuclei identified within their prostate cancers (up to 20, 21-40%, etc.), there is an increasingly significant association between E2F3 staining and risk of death both for overall survival (P=0.0014) and for cause-specific survival (P=0.0004). Multivariate analyses select E2F3 expression as an independent factor predicting overall survival (unstratified P=0.0103, stratified P=0.0086) and cause-specific survival (unstratified P=0.0288, stratified P=0.0072). When these results are considered together with published data on EZH2 and on the E2F3 control protein pRB, we conclude that the pRB-E2F3-EZH2 control axis may have a critical role in modulating aggressiveness of individual human prostate cancer. PMID:15184867

  2. Predictive value of MSH2 gene expression in colorectal cancer treated with capecitabine

    DEFF Research Database (Denmark)

    Jensen, Lars H; Danenberg, Kathleen D; Danenberg, Peter V; Jakobsen, Anders

    2007-01-01

    PURPOSE: The objective of the present study was to evaluate the gene expression of the DNA mismatch repair gene MSH2 as a predictive marker in advanced colorectal cancer (CRC) treated with first-line capecitabine. PATIENTS AND METHODS: Microdissection of paraffin-embedded tumor tissue, RNA...... extraction, and quantitative polymerase chain reaction were performed on tumors obtained from 37 patients with advanced CRC. RESULTS: The median relative gene expression of MSH2 was 0.65 (quartiles 0.5-0.8) in nonresponders and 1.25 (quartiles 0.92-1.38) for responders (P = 0.038). High expression of MSH2...

  3. Metabolic Tumor Burden Predicts for Disease Progression and Death in Lung Cancer

    International Nuclear Information System (INIS)

    Purpose: In lung cancer, stage is an important prognostic factor for disease progression and survival. However, stage may be simply a surrogate for underlying tumor burden. Our purpose was to assess the prognostic value of tumor burden measured by 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging. Patients and Methods: We identified 19 patients with lung cancer who had staging PET-CT scans before any therapy, and adequate follow-up (complete to time of progression for 18, and death for 15 of 19). Metabolically active tumor regions were segmented on pretreatment PET scans semi-automatically using custom software. We determined the relationship between times to progression (TTP) and death (OS) and two PET parameters: total metabolic tumor volume (MTV), and standardized uptake value (SUV). Results: The estimated median TTP and OS for the cohort were 9.3 months and 14.8 months. On multivariate Cox proportional hazards regression analysis, an increase in MTV of 25 ml (difference between the 75th and 25th percentiles) was associated with increased hazard of progression and of death (5.4-fold and 7.6-fold), statistically significant (p = 0.0014 and p = 0.001) after controlling for stage, treatment intent (definitive or palliative), age, Karnofsky performance status, and weight loss. We did not find a significant relationship between SUV and TTP or OS. Conclusions: In this study, high tumor burden assessed by PET MTV is an independent poor prognostic feature in lung cancer, promising for stratifying patients in randomized trials and ultimately for selecting risk-adapted therapies. These results will need to be validated in larger cohorts with longer follow-up, and evaluated prospectively

  4. 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.

  5. Prognostic and predictive value of liver volume on colorectal cancer patients with unresectable liver metastases

    International Nuclear Information System (INIS)

    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.

  6. 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

  7. 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

  8. 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.

  9. Development of a Multicomponent Prediction Model for Acute Esophagitis in Lung Cancer Patients Receiving Chemoradiotherapy

    International Nuclear Information System (INIS)

    Purpose: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. Patients and Methods: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidate genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. Results: A total of 110 patients (40%) developed acute esophagitis Grade ≥2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. Conclusion: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.

  10. Can perfusion MRI predict response to preoperative treatment in rectal cancer?

    International Nuclear Information System (INIS)

    Background and purpose: Dynamic contrast-enhanced MRI (DCE-MRI) provides information on perfusion and could identify good prognostic tumors. Aim of this study was to evaluate whether DCE-MRI using a novel blood pool contrast-agent can accurately predict the response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Materials and methods: Thirty patients underwent DCE-MRI before and 7–10 weeks after chemoradiotherapy. Regions of interest were drawn on DCE-MRI with T2W-images as reference. DCE-MRI-based kinetic parameters (initial slope, initial peak, late slope, and AUC at 60, 90, and 120 s) determined pre- and post-CRT and their Δ were compared between good (TRG1–2) and poor (TRG3–5) responders. Optimal thresholds were determined and sensitivities, specificities, positive predictive values (PPV), and negative predictive values (NPV) were calculated. Results: Pre-therapy, the late slope was able to discriminate between good and poor responders (−0.05 × 10−3 vs. 0.62 × 10−3, p < 0.001) with an AUC of 0.90, sensitivity 92%, specificity 82%, PPV 80%, and NPV 93%. Other pre-CRT parameters showed no significant differences, nor any post-CRT parameters or their Δ. Conclusions: The kinetic parameter ‘late slope’ derived from DCE-MRI could potentially be helpful to predict before the onset of neoadjuvant chemoradiotherapy which tumors are likely going to respond. This could allow for personalized treatment-options in rectal cancer patients

  11. Validation that Metabolic Tumor Volume Predicts Outcome in Head-and-Neck Cancer

    International Nuclear Information System (INIS)

    Purpose: We have previously reported that metabolic tumor volume (MTV) obtained from pretreatment 18F-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 p16INK4a 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 (SUVmax) 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 cm3 (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). SUVmax was not associated with either outcome. Primary tumor MTV predicted progression-free (hazard ratio [HR] = 1.94; p 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.

  12. Random Forests to Predict Rectal Toxicity Following Prostate Cancer Radiation Therapy

    International Nuclear Information System (INIS)

    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

  13. PET-Based Treatment Response Evaluation in Rectal Cancer: Prediction and Validation

    International Nuclear Information System (INIS)

    Purpose: To develop a positron emission tomography (PET)-based response prediction model to differentiate pathological responders from nonresponders. The predictive strength of the model was validated in a second patient group, treated and imaged identical to the patients on which the predictive model was based. Methods and Materials: Fifty-one rectal cancer patients were prospectively included in this study. All patients underwent fluorodeoxyglucose (FDG) PET-computed tomography (CT) imaging both before the start of chemoradiotherapy (CRT) and after 2 weeks of treatment. Preoperative treatment with CRT was followed by a total mesorectal excision. From the resected specimen, the tumor regression grade (TRG) was scored according to the Mandard criteria. From one patient group (n = 30), the metabolic treatment response was correlated with the pathological treatment response, resulting in a receiver operating characteristic (ROC) curve based cutoff value for the reduction of maximum standardized uptake value (SUVmax) within the tumor to differentiate pathological responders (TRG 1–2) from nonresponders (TRG 3–5). The applicability of the selected cutoff value for new patients was validated in a second patient group (n = 21). Results: When correlating the metabolic and pathological treatment response for the first patient group using ROC curve analysis (area under the curve = 0.98), a cutoff value of 48% SUVmax reduction was selected to differentiate pathological responders from nonresponders (specificity of 100%, sensitivity of 64%). Applying this cutoff value to the second patient group resulted in a specificity and sensitivity of, respectively, 93% and 83%, with only one of the pathological nonresponders being false positively predicted as pathological responding. Conclusions: For rectal cancer, an accurate PET-based prediction of the pathological treatment response is feasible already after 2 weeks of CRT. The presented predictive model could be used to select

  14. MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences

    Science.gov (United States)

    2011-01-01

    Background MicroRNAs are a family of ~22 nt small RNAs that can regulate gene expression at the post-transcriptional level. Identification of these molecules and their targets can aid understanding of regulatory processes. Recently, HTS has become a common identification method but there are two major limitations associated with the technique. Firstly, the method has low efficiency, with typically less than 1 in 10,000 sequences representing miRNA reads and secondly the method preferentially targets highly expressed miRNAs. If sequences are available, computational methods can provide a screening step to investigate the value of an HTS study and aid interpretation of results. However, current methods can only predict miRNAs for short fragments and have usually been trained against small datasets which don't always reflect the diversity of these molecules. Results We have developed a software tool, miRPara, that predicts most probable mature miRNA coding regions from genome scale sequences in a species specific manner. We classified sequences from miRBase into animal, plant and overall categories and used a support vector machine to train three models based on an initial set of 77 parameters related to the physical properties of the pre-miRNA and its miRNAs. By applying parameter filtering we found a subset of ~25 parameters produced higher prediction ability compared to the full set. Our software achieves an accuracy of up to 80% against experimentally verified mature miRNAs, making it one of the most accurate methods available. Conclusions miRPara is an effective tool for locating miRNAs coding regions in genome sequences and can be used as a screening step prior to HTS experiments. It is available at http://www.whiov.ac.cn/bioinformatics/mirpara PMID:21504621

  15. 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

  16. 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.

  17. Predicting recurrence of non-muscle-invasive bladder cancer after transurethral resection

    Directory of Open Access Journals (Sweden)

    Haris Ðug

    2016-02-01

    Full Text Available Aim To determine clinical prognostic factors and their impact on the risk of recurrence of newly discovered non-muscle-invasive bladder cancer. Methods The study included 120 patients of both sexes aged 45-80 years with newly discovered non-muscle-invasive bladder cancer. All the patients were treated surgically by transurethral electro resection (TUER. The outcome of patients with and without recurrence was followed at intervals of three months after surgery, the total of two years. For monitoring the probability of early recurrence the criteria of the European Organization for Research and Treatment of Cancer (EORTC were used. Results The average age of the patients was 65.9 years, 79 (79.2% males and 21 (20.8% females. The total of 67 (55.8% patients had a recurrence during the period of monitoring. The average time to the first and fourth recurrence was 15.4 and 23.9 months, respectively. Numbers of tumors and a degree of invasion had a significant prognostic impact on the risk of recurrence. The EORTC score was a highly significant predictor of recurrence (OR=1.237; p<0.001. Conclusion Based on available clinical and pathological prognostic factors and by stratification of patients into three disease risk groups it is possible to predict the possibility of disease. Individual approach and recommendations for the treatment using EORTC risk tables should improve the quality of treatment.

  18. 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.

  19. 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

  20. 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.

  1. Merging person-specific bio-markers for predicting oral cancer recurrence through an ontology.

    Science.gov (United States)

    Salvi, Dario; Picone, Marco; Arredondo, María Teresa; Cabrera-Umpierrez, María Fernanda; Esteban, Ángel; Steger, Sebastian; Poli, Tito

    2013-01-01

    One of the major problems related to cancer treatment is its recurrence. Without knowing in advance how likely the cancer will relapse, clinical practice usually recommends adjuvant treatments that have strong side effects. A way to optimize treatments is to predict the recurrence probability by analyzing a set of bio-markers. The NeoMark European project has identified a set of preliminary bio-markers for the case of oral cancer by collecting a large series of data from genomic, imaging, and clinical evidence. This heterogeneous set of data needs a proper representation in order to be stored, computed, and communicated efficiently. Ontologies are often considered the proper mean to integrate biomedical data, for their high level of formality and for the need of interoperable, universally accepted models. This paper presents the NeoMark system and how an ontology has been designed to integrate all its heterogeneous data. The system has been validated in a pilot in which data will populate the ontology and will be made public for further research. PMID:22955869

  2. 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.

  3. 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.

  4. 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.)

  5. 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.)

  6. 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...

  7. 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

  8. 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.

  9. Predicting the presence of extracranial metastases in patients with brain metastases upon first diagnosis of cancer

    International Nuclear Information System (INIS)

    This study aimed to determine factors allowing the prediction of extracranial metastases in patients presenting with brain metastases at the first diagnosis of cancer. Data from 659 patients with brain metastases upon first diagnosis of cancer were retrospectively analyzed. The parameters age, gender, Karnofsky performance score (KPS), primary tumor type and number of brain metastases were compared between 359 patients with extracranial metastases and 300 patients without extracranial metastases. Additional analyses were performed for patients with the most unfavorable and those with the most favorable characteristics. The comparison of patients with versus without extracranial metastases revealed significant differences between the groups in terms of KPS (p < 0.001) and number of brain metastases (p < 0.001). Of the study patients, 113 had both most unfavorable characteristics, i.e. KPS ≤ 50 and ≥ 4 brain metastases. The sensitivity for identifying patients with extracranial metastases was 82 %; specificity was 51 %. A total of 50 patients had KPS ≥ 90 and only one brain metastasis. The sensitivity for identifying patients without extracranial metastases was 86 %; specificity was 58 %. The combination of KPS and the number of brain metastases can help to predict the presence or absence of extracranial metastases. (orig.)

  10. 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

  11. 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

  12. 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.

  13. Predicting the pathological features of the mesorectum before the laparoscopic approach to rectal cancer.

    Science.gov (United States)

    Fernández Ananín, Sonia; Targarona, Eduardo M; Martinez, Carmen; Pernas, Juan Carlos; Hernández, Diana; Gich, Ignasi; Sancho, Francesc J; Trias, Manuel

    2014-12-01

    Pelvic anatomy and tumour features play a role in the difficulty of the laparoscopic approach to total mesorectal excision in rectal cancer. The aim of the study was to analyse whether these characteristics also influence the quality of the surgical specimen. We performed a prospective study in consecutive patients with rectal cancer located less than 12 cm from the anal verge who underwent laparoscopic surgery between January 2010 and July 2013. Exclusion criteria were T1 and T4 tumours, abdominoperineal resections, obstructive and perforated tumours, or any major contraindication for laparoscopic surgery. Dependent variables were the circumferential resection margin (CMR) and the quality of the mesorectum. Sixty-four patients underwent laparoscopic sphincter-preserving total mesorectal excision. Resection was complete in 79.1% of specimens and CMR was positive in 9.7%. Univariate analysis showed tumour depth (T status) (P = 0.04) and promontorium-subsacrum angle (P = 0.02) independently predicted CRM (circumferential resection margin) positivity. Tumour depth (P CRM. Bony pelvis dimensions influenced the quality of the specimen obtained by laparoscopy. These measurements may be useful to predict which patients will benefit most from laparoscopic surgery and also to select patients in accordance with the learning curve of trainee surgeons. PMID:24950725

  14. 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.

  15. The value of metabolic imaging to predict tumour response after chemoradiation in locally advanced rectal cancer

    International Nuclear Information System (INIS)

    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). 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. 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. 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

  16. 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)

  17. Serial cytological assay of micronucleus induction: a new tool to predict human cancer radiosensitivity

    International Nuclear Information System (INIS)

    Background and purpose: The micronucleus test, generally done in cultured tumour cells irradiated in vitro, has not gained wide acceptance in predicting human cancer radiosensitivity. The purpose of this study was to see if micronucleus assay by serial scrape smear cytology can predict oral cancer radiosensitivity. Materials and methods: Forty nine oral cancer patients given radiotherapy (60 Gy/25 fractions/5 weeks) form the study population. Serial scrape smears were taken from their tumours before treatment and after delivery of 2, 5, 8 and 12 fractions, stained by Giemsa and the number of micronucleated cells (MNC) noted. The patients were grouped to those who developed tumour recurrence ('Resistant') and those who did not ('Sensitive'), and the pattern of micronucleus induction compared. Results: Both groups of tumours had MNC even before treatment, with statistically significant dose-related increase with radiotherapy. The sensitive group had a higher mean increase in MNC count than the resistant group (6.1 times and 3.6 times the pre-treatment value, respectively) and better correlation with dose (r=0.54 vs. 0.43). The increase in MNC count occurred earlier in the resistant group than in the sensitive, the TMNC (time for the pre-treatment value to double) being 3.3 days and 7.6 days, respectively. Also, the resistant group showed a plateauing of the MNC count which the sensitive group lacked. Conclusion: The higher MNC induction in the sensitive tumours suggests the usefulness of the assay as a test of radiosensitivity. The differing patterns of MNC increase suggest that differences in proliferation rate is an important cause of tumour failure. Serial cytological assay of micronucleus induction can identify both radiosensitivity and proliferation characteristics of tumours, and thus may turn out to be a useful test of radiocurability

  18. Molecular Markers Predict Distant Metastases After Adjuvant Chemoradiation for Rectal Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jun Won; Kim, Yong Bae [Department of Radiation Oncology, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Choi, Jun Jeong [Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Koom, Woong Sub [Department of Radiation Oncology, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Kim, Hoguen [Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Kim, Nam-Kyu [Department of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Ahn, Joong Bae [Department of Medical Oncology, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Lee, Ikjae; Cho, Jae Ho [Department of Radiation Oncology, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Keum, Ki Chang, E-mail: kckeum@yuhs.ac [Department of Radiation Oncology, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of)

    2012-12-01

    Purpose: The outcomes of adjuvant chemoradiation for locally advanced rectal cancer are nonuniform among patients with matching prognostic factors. We explored the role of molecular markers for predicting the outcome of adjuvant chemoradiation for rectal cancer patients. Methods and Materials: The study included 68 patients with stages II to III rectal adenocarcinoma who were treated with total mesorectal excision and adjuvant chemoradiation. Chemotherapy based on 5-fluorouracil and leucovorin was intravenously administered each month for 6-12 cycles. Radiation therapy consisted of 54 Gy delivered in 30 fractions. Immunostaining of surgical specimens for COX-2, EGFR, VEGF, thymidine synthase (TS), and Raf kinase inhibitor protein (RKIP) was performed. Results: The median follow-up was 65 months. Eight locoregional (11.8%) and 13 distant (19.1%) recurrences occurred. Five-year locoregional failure-free survival (LRFFS), distant metastasis-free survival (DMFS), disease-free survival (DFS), and overall survival (OS) rates for all patients were 83.9%, 78.7%, 66.7%, and 73.8%, respectively. LRFFS was not correlated with TNM stage, surgical margin, or any of the molecular markers. VEGF overexpression was significantly correlated with decreased DMFS (P=.045), while RKIP-positive results were correlated with increased DMFS (P=.025). In multivariate analyses, positive findings for COX-2 (COX-2+) and VEGF (VEGF+) and negative findings for RKIP (RKIP-) were independent prognostic factors for DMFS, DFS, and OS (P=.035, .014, and .007 for DMFS; .021, .010, and <.0001 for DFS; and .004, .012, and .001 for OS). The combination of both COX-2+ and VEGF+ (COX-2+/VEGF+) showed a strong correlation with decreased DFS (P=.007), and the combinations of RKIP+/COX-2- and RKIP+/VEGF- showed strong correlations with improved DFS compared with the rest of the patients (P=.001 and <.0001, respectively). Conclusions: Molecular markers can be valuable in predicting treatment outcome of adjuvant

  19. Incorporating epistasis interaction of genetic susceptibility single nucleotide polymorphisms in a lung cancer risk prediction model.

    Science.gov (United States)

    Marcus, Michael W; Raji, Olaide Y; Duffy, Stephen W; Young, Robert P; Hopkins, Raewyn J; Field, John K

    2016-07-01

    Incorporation of genetic variants such as single nucleotide polymorphisms (SNPs) into risk prediction models may account for a substantial fraction of attributable disease risk. Genetic data, from 2385 subjects recruited into the Liverpool Lung Project (LLP) between 2000 and 2008, consisting of 20 SNPs independently validated in a candidate-gene discovery study was used. Multifactor dimensionality reduction (MDR) and random forest (RF) were used to explore evidence of epistasis among 20 replicated SNPs. Multivariable logistic regression was used to identify similar risk predictors for lung cancer in the LLP risk model for the epidemiological model and extended model with SNPs. Both models were internally validated using the bootstrap method and model performance was assessed using area under the curve (AUC) and net reclassification improvement (NRI). Using MDR and RF, the overall best classifier of lung cancer status were SNPs rs1799732 (DRD2), rs5744256 (IL-18), rs2306022 (ITGA11) with training accuracy of 0.6592 and a testing accuracy of 0.6572 and a cross-validation consistency of 10/10 with permutation testing Pmodel was 0.75 (95% CI 0.73-0.77). When epistatic data were incorporated in the extended model, the AUC increased to 0.81 (95% CI 0.79-0.83) which corresponds to 8% increase in AUC (DeLong's test P=2.2e-16); 17.5% by NRI. After correction for optimism, the AUC was 0.73 for the epidemiological model and 0.79 for the extended model. Our results showed modest improvement in lung cancer risk prediction when the SNP epistasis factor was added. PMID:27121382

  20. Experiences from treatment-predictive KRAS testing; high mutation frequency in rectal cancers from females and concurrent mutations in the same tumor

    DEFF Research Database (Denmark)

    Jönsson, Mats; Ekstrand, Anna; Edekling, Thomas; Eberhard, Jakob; Grabau, Dorthe; Borg, David; Nilbert, Mef

    2009-01-01

    BACKGROUND: KRAS mutations represent key alterations in colorectal cancer development and lead to constitutive EGFR signaling. Since EGFR inhibition represents a therapeutic strategy in advanced colorectal cancer, KRAS mutation analysis has quickly been introduced as a treatment-predictive test. ...

  1. Is Tc-99m sestamibi scintimammography useful in the prediction of neoadjuvant chemotherapy responses in breast cancer? A systematic review and meta-analysis.

    Science.gov (United States)

    Guo, Cui; Zhang, Chengpeng; Liu, Jianjun; Tong, Linjun; Huang, Gang

    2016-07-01

    To evaluate the accuracy of Tc-99m sestamibi (MIBI) scintimammography in the prediction of neoadjuvant chemotherapy response in breast cancer. 'PubMed' (MEDLINE included) and Embase database were searched for relevant publications in English. Methodological quality of the included studies was assessed with Quality Assessment of Diagnosis Accuracy Studies (QUADAS), and 'Meta-Disc' and 'Stata' software were used to determine pooled sensitivity, specificity, and diagnostic odds ratio (DOR), and construct a summary receiver-operating characteristic curve. Fourteen studies (a total of 503 individuals) fulfilled the inclusion criteria. The pooled sensitivity was 0.86 [95% confidence interval (CI): 0.78-0.92] and the pooled specificity was 0.69 (95% CI: 0.64-0.74). Pooled likelihood ratio (LRp), negative likelihood ratio (LR-), and DOR were 2.64 (95% CI: 1.81-3.85), 0.26 (95% CI: 0.15-0.46), and 12.06 (95% CI: 6.94-20.96), respectively. The area under the summary receiver-operating characteristic curve was 0.86. For the prediction of pathological complete response (10 studies included), the pooled sensitivity and specificity and DOR were 0.86 (95% CI: 0.77-0.93), 0.67 (95% CI: 0.62-0.72), and 11.43 (95% CI: 5.95-21.97). Our results indicated that Tc-99m MIBI scintimammography had acceptable sensitivity in the prediction of neoadjuvant chemotherapy response in breast cancer; however, its relatively low specificity showed that a combination of other imaging modalities would still be needed. Subgroup analysis indicated that performing early mid-treatment Tc-99m MIBI scintimammography (using the reduction rate of one or two cycles or within the first half-courses of chemotherapy compared with the baseline) was better than carrying out later (after three or more courses) or post-treatment scintimammography in the prediction of neoadjuvant chemotherapy response. PMID:26974314

  2. 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

  3. 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

  4. 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

  5. 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

  6. Aneuploidy involving chromosome 1 may be an early predictive marker of intestinal type gastric cancer

    International Nuclear Information System (INIS)

    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-κB), 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-κB) consecutive patients, aneuploidy was assessed by interphase fluorescent in situ hybridisation (FISH) for chromosome 1. NF-κB 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-κB 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 clinically useful in pre

  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. Role of nutritional status in predicting quality of life outcomes in cancer--a systematic review of the epidemiological literature.

    Science.gov (United States)

    Lis, Christopher G; Gupta, Digant; Lammersfeld, Carolyn A; Markman, Maurie; Vashi, Pankaj G

    2012-01-01

    Malnutrition is a significant factor in predicting cancer patients' quality of life (QoL). We systematically reviewed the literature on the role of nutritional status in predicting QoL in cancer. We searched MEDLINE database using the terms "nutritional status" in combination with "quality of life" together with "cancer". Human studies published in English, having nutritional status as one of the predictor variables, and QoL as one of the outcome measures were included. Of the 26 included studies, 6 investigated head and neck cancer, 8 gastrointestinal, 1 lung, 1 gynecologic and 10 heterogeneous cancers. 24 studies concluded that better nutritional status was associated with better QoL, 1 study showed that better nutritional status was associated with better QoL only in high-risk patients, while 1 study concluded that there was no association between nutritional status and QoL. Nutritional status is a strong predictor of QoL in cancer patients. We recommend that more providers implement the American Society of Parenteral and Enteral Nutrition (ASPEN) guidelines for oncology patients, which includes nutritional screening, nutritional assessment and intervention as appropriate. Correcting malnutrition may improve QoL in cancer patients, an important outcome of interest to cancer patients, their caregivers, and families. PMID:22531478

  9. 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.

  10. 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 ...

  11. 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.)

  12. 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

  13. 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

  14. 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.

  15. A predictive model for survival in metastatic cancer patients attending an outpatient palliative radiotherapy clinic

    International Nuclear Information System (INIS)

    Purpose: To develop a predictive model for survival from the time of presentation in an outpatient palliative radiotherapy clinic. Methods and Materials: Sixteen factors were analyzed prospectively in 395 patients seen in a dedicated palliative radiotherapy clinic in a large tertiary cancer center using Cox's proportional hazards regression model. Results: Six prognostic factors had a statistically significant impact on survival, as follows: primary cancer site, site of metastases, Karnofsky performance score (KPS), and fatigue, appetite, and shortness of breath scores from the modified Edmonton Symptom Assessment Scale. Risk group stratification was performed (1) by assigning weights to the prognostic factors based on their levels of significance, and (2) by the number of risk factors present. The weighting method provided a Survival Prediction Score (SPS), ranging from 0 to 32. The survival probability at 3, 6, and 12 months was 83%, 70%, and 51%, respectively, for patients with SPS ≤13 (n=133); 67%, 41%, and 20% for patients with SPS 14-19 (n=129); and 36%, 18%, and 4% for patients with SPS ≥20 (n=133) (p<0.0001). Corresponding survival probabilities based on number of risk factors were as follows: 85%, 72%, and 52% (≤3 risk factors) (n=98); 68%, 47%, and 24% (4 risk factors) (n=117); and 46%, 24%, and 11% (≥5 factors) (n=180) (p<0.0001). Conclusion: Clinical prognostic factors can be used to predict prognosis among patients attending a palliative radiotherapy clinic. If validated in an independent series of patients, the model can be used to guide clinical decisions, plan supportive services, and allocate resource use

  16. Radiotherapy on the neck nodes predicts severe weight loss in patients with early stage laryngeal cancer

    International Nuclear Information System (INIS)

    Background and purpose: Although patients with early stage (T1/T2) laryngeal cancer (LC) are thought to have a low incidence of malnutrition, severe weight loss is observed in a subgroup of these patients during radiotherapy (RT). The objective of this study was to evaluate weight loss and nutrition-related symptoms in patients with T1/T2 LC during RT and to select predictive factors for early identification of malnourished patients. Methods: Of all patients with T1/T2 LC, who received primary RT between 1999 and 2007, the following characteristics were recorded: sex, age, TNM classification, tumour location, radiation schedule, performance status, quality of life, weight loss, and nutrition-related symptoms. The association between baseline characteristics and malnutrition (>5% weight loss during RT) was investigated by Cox regression analysis. Results: The study population consisted of 238 patients. During RT, 44% of patients developed malnutrition. Tumour location, TNM classification, RT on the neck nodes, RT dose, nausea/vomiting, pain, swallowing, senses problems, trouble with social eating, dry mouth and the use of painkillers were all significantly associated with malnutrition. In the multivariate analysis, RTs on both the neck nodes (HR 4.16, 95% CI 2.62-6.60) and dry mouth (HR 1.72, 95% CI 1.14-2.60) remained predictive. Nevertheless, RT on the neck nodes alone resulted in the best predictive model for malnutrition scores. Conclusions: Patients with early stage laryngeal cancer are at risk of malnutrition during radiotherapy. Radiotherapy on the neck nodes is the best predictor of malnutrition during radiotherapy. Therefore, we suggest to offer nutritional counselling to all the patients who receive nodal irradiation.

  17. 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.

  18. Exome mutation burden predicts clinical outcome in ovarian cancer carrying mutated BRCA1 and BRCA2 genes

    DEFF Research Database (Denmark)

    Birkbak, Nicolai Juul; Kochupurakkal, Bose; Gonzalez-Izarzugaza, Jose Maria;

    2013-01-01

    Reliable biomarkers predicting resistance or sensitivity to anti-cancer therapy are critical for oncologists to select proper therapeutic drugs in individual cancer patients. Ovarian and breast cancer patients carrying germline mutations in BRCA1 or BRCA2 genes are often sensitive to DNA damaging......-type BRCA1 and BRCA2 genes. These results suggest that in cancers with DNA repair deficiency caused by functional BRCA loss, higher versus lower Nmut may reflect the status of deficiency or rescue by alternative mechanism(s) for DNA repair, with lower Nmut predicting for resistance to DNA-damaging drugs in...... drugs and relative to non-mutation carriers present a favorable clinical outcome following therapy. Genome sequencing studies have shown a high number of mutations in the tumor genome in patients carrying BRCA1 or BRCA2 mutations (mBRCA). The present study used exome-sequencing and SNP 6 array data of...

  19. Prediction methods for the calculation of the flammability properties of gases and vapors: CHETAH and ASTM software. Part 1. Esters and Ethers

    International Nuclear Information System (INIS)

    After the law by decree of the 12. June 2003, N 233 (ATEX Directive) and REACH regulation (Regulation EC n. 2907/2006 of the European Parliament), several industrial fields, also not chemical, need the flammability data for the substances used. Perhaps, many of these data, especially for compounds with not common uses, are not easy to collect. It would be helpful to provide prediction methods in order to calculate these data without any experimentation that sometimes results time consuming, expensive and practically impossible for all the commercial compounds. In this research the ASTM software CHETAH (CHEmical Thermodynamic And Hazard evaluation) has been used in order to compute the lower flammability limit (Li), the limiting oxygen concentration (LOC, using nitrogen as inert gas) as a function of temperature, the adiabatic flame temperature Tflame, the fundamental burning velocity (Su), the quenching distance (Qd), the minimum ignition energy (MIE) for esters and ethers, substances highly used in industry.

  20. Predicting the response to preoperative radiation or chemoradiation by a microarray analysis of the gene expression profiles in rectal cancer

    International Nuclear Information System (INIS)

    Preoperative radiotherapy or chemoradiotherapy (CRT) has become a standard treatment for patients with locally advanced rectal cancer. However, there is a wide spectrum of responses to preoperative CRT, ranging from none to complete. There has been intense interest in the identification of molecular biomarkers to predict the response to preoperative CRT, in order to spare potentially non-responsive patients from unnecessary treatment. However, no specific molecular biomarkers have yet been definitively proven to be predictive of the response to CRT. Instead of focusing on specific factors, microarray-based gene expression profiling technology enables the simultaneous analysis of large numbers of genes, and might therefore have immense potential for predicting the response to preoperative CRT. We herein review published studies using a microarray-based analysis to identify gene expression profiles associated with the response of rectal cancer to radiation or CRT. Although some studies have reported gene expression signatures capable of high predictive accuracy, the compositions of these signatures have differed considerably, with little gene overlap. However, considering the promising data regarding gene profiling in breast cancer, the microarray analysis could still have potential to improve the management of locally advanced rectal cancer. Increasing the number of patients analyzed for more accurate prediction and the extensive validation of predictive classifiers in prospective clinical trials are necessary before such profiling can be incorporated into future clinical practice. (author)