WorldWideScience

Sample records for cancer prediction software

  1. Quantitative computed tomography for the prediction of pulmonary function after lung cancer surgery: a simple method using simulation software.

    Science.gov (United States)

    Ueda, Kazuhiro; Tanaka, Toshiki; Li, Tao-Sheng; Tanaka, Nobuyuki; Hamano, Kimikazu

    2009-03-01

    The prediction of pulmonary functional reserve is mandatory in therapeutic decision-making for patients with resectable lung cancer, especially those with underlying lung disease. Volumetric analysis in combination with densitometric analysis of the affected lung lobe or segment with quantitative computed tomography (CT) helps to identify residual pulmonary function, although the utility of this modality needs investigation. The subjects of this prospective study were 30 patients with resectable lung cancer. A three-dimensional CT lung model was created with voxels representing normal lung attenuation (-600 to -910 Hounsfield units). Residual pulmonary function was predicted by drawing a boundary line between the lung to be preserved and that to be resected, directly on the lung model. The predicted values were correlated with the postoperative measured values. The predicted and measured values corresponded well (r=0.89, plung cancer surgery and helped to identify patients whose functional reserves are likely to be underestimated. Hence, this modality should be utilized for patients with marginal pulmonary function.

  2. PISCES: A Tool for Predicting Software Testability

    Science.gov (United States)

    Voas, Jeffrey M.; Miller, Keith W.; Payne, Jeffery E.

    1991-01-01

    Before a program can fail, a software fault must be executed, that execution must alter the data state, and the incorrect data state must propagate to a state that results directly in an incorrect output. This paper describes a tool called PISCES (developed by Reliable Software Technologies Corporation) for predicting the probability that faults in a particular program location will accomplish all three of these steps causing program failure. PISCES is a tool that is used during software verification and validation to predict a program's testability.

  3. Using Software Dependency to Bug Prediction

    Directory of Open Access Journals (Sweden)

    Peng He

    2013-01-01

    Full Text Available Software maintenance, especially bug prediction, plays an important role in evaluating software quality and balancing development costs. This study attempts to use several quantitative network metrics to explore their relationships with bug prediction in terms of software dependency. Our work consists of four main steps. First, we constructed software dependency networks regarding five dependency scenes at the class-level granularity. Second, we used a set of nine representative and commonly used metrics—namely, centrality, degree, PageRank, and HITS, as well as modularity—to quantify the importance of each class. Third, we identified how these metrics were related to the proneness and severity of fixed bugs in Tomcat and Ant and determined the extent to which they were related. Finally, the significant metrics were considered as predictors for bug proneness and severity. The result suggests that there is a statistically significant relationship between class’s importance and bug prediction. Furthermore, betweenness centrality and out-degree metric yield an impressive accuracy for bug prediction and test prioritization. The best accuracy of our prediction for bug proneness and bug severity is up to 54.7% and 66.7% (top 50, Tomcat and 63.8% and 48.7% (top 100, Ant, respectively, within these two cases.

  4. Bottlenecks in Software Defect Prediction Implementation in Industrial Projects

    OpenAIRE

    Hryszko Jarosław; Madeyski Lech

    2015-01-01

    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 Syst...

  5. Software Used to Generate Cancer Statistics - SEER Cancer Statistics

    Science.gov (United States)

    Videos that highlight topics and trends in cancer statistics and definitions of statistical terms. Also software tools for analyzing and reporting cancer statistics, which are used to compile SEER's annual reports.

  6. Becoming Predictably Adaptable in Software Development

    Directory of Open Access Journals (Sweden)

    Michael Vakoc

    2017-10-01

    Full Text Available It’s difficult to state exact timelines in software development and it is even more difficult to say when features that users want will be delivered. We propose changes to current software development methodologies that enable companies to be predictably adaptable and deliver both on time and what customer asked for. We do so through research of current literature, interviews and personal experience working at an international company that builds products for millions of customers and is facing exactly the challenges described above.

  7. ROLE OF DATA MINING CLASSIFICATION TECHNIQUE IN SOFTWARE DEFECT PREDICTION

    OpenAIRE

    Dr.A.R.Pon Periyasamy; Mrs A.Misbahulhuda

    2017-01-01

    Software defect prediction is the process of locating defective modules in software. Software quality may be a field of study and apply that describes the fascinating attributes of software package product. The performance should be excellent with none defects. Software quality metrics are a set of software package metrics that target the standard aspects of the product, process, and project. The software package defect prediction model helps in early detection of defects and contributes to t...

  8. Evaluating Predictive Models of Software Quality

    Science.gov (United States)

    Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

    2014-06-01

    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.

  9. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D

    2014-01-01

    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.

  10. Software reliability prediction using SPN | Abbasabadee | Journal of ...

    African Journals Online (AJOL)

    Software reliability prediction using SPN. ... In this research for computation of software reliability, component reliability model based on SPN would be proposed. An isomorphic markov ... EMAIL FREE FULL TEXT EMAIL FREE FULL TEXT

  11. 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.

  12. 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

  13. 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…

  14. Empirical analysis of change metrics for software fault prediction

    NARCIS (Netherlands)

    Choudhary, Garvit Rajesh; Kumar, Sandeep; Kumar, Kuldeep; Mishra, Alok; Catal, Cagatay

    2018-01-01

    A quality assurance activity, known as software fault prediction, can reduce development costs and improve software quality. The objective of this study is to investigate change metrics in conjunction with code metrics to improve the performance of fault prediction models. Experimental studies are

  15. Breast cancer risks and risk prediction models.

    Science.gov (United States)

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  16. Prediction of software operational reliability using testing environment factors

    International Nuclear Information System (INIS)

    Jung, Hoan Sung; Seong, Poong Hyun

    1995-01-01

    A number of software reliability models have been developed to estimate and to predict software reliability. However, there are no established standard models to quantify software reliability. Most models estimate the quality of software in reliability figures such as remaining faults, failure rate, or mean time to next failure at the testing phase, and they consider them ultimate indicators of software reliability. Experience shows that there is a large gap between predicted reliability during development and reliability measured during operation, which means that predicted reliability, or so-called test reliability, is not operational reliability. Customers prefer operational reliability to test reliability. In this study, we propose a method that predicts operational reliability rather than test reliability by introducing the testing environment factor that quantifies the changes in environments

  17. Risky module prediction for nuclear I and C software

    International Nuclear Information System (INIS)

    Kim, Young Mi; Kim, Hyeon Soo

    2012-01-01

    As software based digital I and C (Instrumentation and Control) systems are used more prevalently in nuclear plants, enhancement of software dependability has become an important issue in the area of nuclear I and C systems. Critical attributes of software dependability are safety and reliability. These attributes are tightly related to software failures caused by faults. Software testing and V and V (Verification and Validation) activities are hence important for enhancing software dependability. If the risky modules of safety-critical software can be predicted, it will be possible to focus on testing and V and V activities more efficiently and effectively. It should also make it possible to better allocate resources for regulation activities. We propose a prediction technique to estimate risky software modules by adopting machine learning models based on software complexity metrics. An empirical study with various machine learning algorithms was executed for comparing the prediction performance. Experimental results show SVMs (Support Vector Machines) perform as well or better than the other methods.

  18. Beyond Reactive Planning: Self Adaptive Software and Self Modeling Software in Predictive Deliberation Management

    National Research Council Canada - National Science Library

    Lenahan, Jack; Nash, Michael P; Charles, Phil

    2008-01-01

    .... We present the following hypothesis: predictive deliberation management using self-adapting and self-modeling software will be required to provide mission planning adjustments after the start of a mission...

  19. Presenting an Evaluation Model for the Cancer Registry Software.

    Science.gov (United States)

    Moghaddasi, Hamid; Asadi, Farkhondeh; Rabiei, Reza; Rahimi, Farough; Shahbodaghi, Reihaneh

    2017-12-01

    As cancer is increasingly growing, cancer registry is of great importance as the main core of cancer control programs, and many different software has been designed for this purpose. Therefore, establishing a comprehensive evaluation model is essential to evaluate and compare a wide range of such software. In this study, the criteria of the cancer registry software have been determined by studying the documents and two functional software of this field. The evaluation tool was a checklist and in order to validate the model, this checklist was presented to experts in the form of a questionnaire. To analyze the results of validation, an agreed coefficient of %75 was determined in order to apply changes. Finally, when the model was approved, the final version of the evaluation model for the cancer registry software was presented. The evaluation model of this study contains tool and method of evaluation. The evaluation tool is a checklist including the general and specific criteria of the cancer registry software along with their sub-criteria. The evaluation method of this study was chosen as a criteria-based evaluation method based on the findings. The model of this study encompasses various dimensions of cancer registry software and a proper method for evaluating it. The strong point of this evaluation model is the separation between general criteria and the specific ones, while trying to fulfill the comprehensiveness of the criteria. Since this model has been validated, it can be used as a standard to evaluate the cancer registry software.

  20. Evolutionary neural network modeling for software cumulative failure time prediction

    International Nuclear Information System (INIS)

    Tian Liang; Noore, Afzel

    2005-01-01

    An evolutionary neural network modeling approach for software cumulative failure time prediction based on multiple-delayed-input single-output architecture is proposed. Genetic algorithm is used to globally optimize the number of the delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg-Marquardt algorithm with Bayesian regularization is used to improve the ability to predict software cumulative failure time. The performance of our proposed approach has been compared using real-time control and flight dynamic application data sets. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches

  1. Predicting Software Projects Cost Estimation Based on Mining Historical Data

    OpenAIRE

    Najadat, Hassan; Alsmadi, Izzat; Shboul, Yazan

    2012-01-01

    In this research, a hybrid cost estimation model is proposed to produce a realistic prediction model that takes into consideration software project, product, process, and environmental elements. A cost estimation dataset is built from a large number of open source projects. Those projects are divided into three domains: communication, finance, and game projects. Several data mining techniques are used to classify software projects in terms of their development complexity. Data mining techniqu...

  2. Designing Prediction Tasks in a Mathematics Software Environment

    Science.gov (United States)

    Brunström, Mats; Fahlgren, Maria

    2015-01-01

    There is a recognised need in mathematics teaching for new kinds of tasks which exploit the affordances provided by new technology. This paper focuses on the design of prediction tasks to foster student reasoning about exponential functions in a mathematics software environment. It draws on the first iteration of a design based research study…

  3. Prediction of software operational reliability using testing environment factor

    International Nuclear Information System (INIS)

    Jung, Hoan Sung

    1995-02-01

    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

  4. Prediction of software operational reliability using testing environment factors

    International Nuclear Information System (INIS)

    Jung, Hoan Sung; Seong, Poong Hyun

    1995-01-01

    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. The experiences show that the operational reliability is higher than the test reliability User's interest is on the operational reliability rather than on the test reliability, however. With the assumption that the difference in reliability results from the change of environment, testing environment factors comprising the aging factor and the coverage factor are defined in this study to predict the ultimate operational reliability with the failure data. It is by incorporating test environments applied beyond the operational profile into testing environment factors. The application results are close to the actual data

  5. Early experiences building a software quality prediction model

    Science.gov (United States)

    Agresti, W. W.; Evanco, W. M.; Smith, M. C.

    1990-01-01

    Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.

  6. Understanding and Predicting the Process of Software Maintenance Releases

    Science.gov (United States)

    Basili, Victor; Briand, Lionel; Condon, Steven; Kim, Yong-Mi; Melo, Walcelio L.; Valett, Jon D.

    1996-01-01

    One of the major concerns of any maintenance organization is to understand and estimate the cost of maintenance releases of software systems. Planning the next release so as to maximize the increase in functionality and the improvement in quality are vital to successful maintenance management. The objective of this paper is to present the results of a case study in which an incremental approach was used to better understand the effort distribution of releases and build a predictive effort model for software maintenance releases. This study was conducted in the Flight Dynamics Division (FDD) of NASA Goddard Space Flight Center(GSFC). This paper presents three main results: 1) a predictive effort model developed for the FDD's software maintenance release process; 2) measurement-based lessons learned about the maintenance process in the FDD; and 3) a set of lessons learned about the establishment of a measurement-based software maintenance improvement program. In addition, this study provides insights and guidelines for obtaining similar results in other maintenance organizations.

  7. Prediction of Software Reliability using Bio Inspired Soft Computing Techniques.

    Science.gov (United States)

    Diwaker, Chander; Tomar, Pradeep; Poonia, Ramesh C; Singh, Vijander

    2018-04-10

    A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing

  8. RNAstructure: software for RNA secondary structure prediction and analysis.

    Science.gov (United States)

    Reuter, Jessica S; Mathews, David H

    2010-03-15

    To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence. RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html.

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

    International Nuclear Information System (INIS)

    Yoneda, Kimitoshi; Morita, Ryo; Inada, Fumio; Fujiwara, Kazutoshi

    2012-01-01

    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)

  10. Analyzing and Predicting Micro-Location Patterns of Software Firms

    Directory of Open Access Journals (Sweden)

    Jan Kinne

    2017-12-01

    Full Text Available While the effects of non-geographic aggregation on statistical inference are well studied in economics, research on the effects of geographic aggregation on regression analysis is rather scarce. This knowledge gap, together with the use of aggregated spatial units in previous firm location studies, results in a lack of understanding of firm location determinants at the microgeographic level. Suitable data for microgeographic location analysis has become available only recently through the emergence of Volunteered Geographic Information (VGI, especially the OpenStreetMap (OSM project, and the increasing availability of official (open geodata. In this paper, we use a comprehensive dataset of three million street-level geocoded firm observations to explore the location pattern of software firms in an Exploratory Spatial Data Analysis (ESDA. Based on the ESDA results, we develop a software firm location prediction model using Poisson regression and OSM data. Our findings offer novel insights into the mode of operation of the Modifiable Areal Unit Problem (MAUP in the context of a microgeographic location analysis: We find that non-aggregated data can be used to detect information on location determinants, which are superimposed when aggregated spatial units are analyzed, and that some findings of previous firm location studies are not robust at the microgeographic level. However, we also conclude that the lack of high-resolution geodata on socio-economic population characteristics causes systematic prediction errors, especially in cities with diverse and segregated populations.

  11. Predictive model for survival in patients with gastric cancer.

    Science.gov (United States)

    Goshayeshi, Ladan; Hoseini, Benyamin; Yousefli, Zahra; Khooie, Alireza; Etminani, Kobra; Esmaeilzadeh, Abbas; Golabpour, Amin

    2017-12-01

    Gastric cancer is one of the most prevalent cancers in the world. Characterized by poor prognosis, it is a frequent cause of cancer in Iran. The aim of the study was to design a predictive model of survival time for patients suffering from gastric cancer. This was a historical cohort conducted between 2011 and 2016. Study population were 277 patients suffering from gastric cancer. Data were gathered from the Iranian Cancer Registry and the laboratory of Emam Reza Hospital in Mashhad, Iran. Patients or their relatives underwent interviews where it was needed. Missing values were imputed by data mining techniques. Fifteen factors were analyzed. Survival was addressed as a dependent variable. Then, the predictive model was designed by combining both genetic algorithm and logistic regression. Matlab 2014 software was used to combine them. Of the 277 patients, only survival of 80 patients was available whose data were used for designing the predictive model. Mean ?SD of missing values for each patient was 4.43?.41 combined predictive model achieved 72.57% accuracy. Sex, birth year, age at diagnosis time, age at diagnosis time of patients' family, family history of gastric cancer, and family history of other gastrointestinal cancers were six parameters associated with patient survival. The study revealed that imputing missing values by data mining techniques have a good accuracy. And it also revealed six parameters extracted by genetic algorithm effect on the survival of patients with gastric cancer. Our combined predictive model, with a good accuracy, is appropriate to forecast the survival of patients suffering from Gastric cancer. So, we suggest policy makers and specialists to apply it for prediction of patients' survival.

  12. 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 mortali...

  13. P-MartCancer-Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets.

    Science.gov (United States)

    Webb-Robertson, Bobbie-Jo M; Bramer, Lisa M; Jensen, Jeffrey L; Kobold, Markus A; Stratton, Kelly G; White, Amanda M; Rodland, Karin D

    2017-11-01

    P-MartCancer is an interactive web-based software environment that enables statistical analyses of peptide or protein data, quantitated from mass spectrometry-based global proteomics experiments, without requiring in-depth knowledge of statistical programming. P-MartCancer offers a series of statistical modules associated with quality assessment, peptide and protein statistics, protein quantification, and exploratory data analyses driven by the user via customized workflows and interactive visualization. Currently, P-MartCancer offers access and the capability to analyze multiple cancer proteomic datasets generated through the Clinical Proteomics Tumor Analysis Consortium at the peptide, gene, and protein levels. P-MartCancer is deployed as a web service (https://pmart.labworks.org/cptac.html), alternatively available via Docker Hub (https://hub.docker.com/r/pnnl/pmart-web/). Cancer Res; 77(21); e47-50. ©2017 AACR . ©2017 American Association for Cancer Research.

  14. Assessing the accuracy of software predictions of mammalian and microbial metabolites

    Science.gov (United States)

    New chemical development and hazard assessments benefit from accurate predictions of mammalian and microbial metabolites. Fourteen biotransformation libraries encoded in eight software packages that predict metabolite structures were assessed for their sensitivity (proportion of ...

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

    International Nuclear Information System (INIS)

    Son, H.S.; Seong, P.H.

    2001-01-01

    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)

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

    International Nuclear Information System (INIS)

    Son, H.S.; Seong, P.H.

    2000-01-01

    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)

  17. Basic Modelling principles and Validation of Software for Prediction of Collision Damage

    DEFF Research Database (Denmark)

    Simonsen, Bo Cerup

    2000-01-01

    This report describes basic modelling principles, the theoretical background and validation examples for the collision damage prediction module in the ISESO stand-alone software.......This report describes basic modelling principles, the theoretical background and validation examples for the collision damage prediction module in the ISESO stand-alone software....

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

    DEFF Research Database (Denmark)

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

    2015-01-01

    Assessing the presence and availability of a remote colleague is key in coordination in global software development but is not easily done using existing computer-mediated channels. Previous research has shown that automated estimation of interruptibility is feasible and can achieve a precision....... These early but promising results represent a starting point for designing tools with support for interruptibility capable of improving distributed awareness and cooperation to be used in global software development....

  19. 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.

  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. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

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

  2. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

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

  3. 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.

  4. Breast Cancer Risk Prediction Models

    Science.gov (United States)

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

  5. 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.

  6. 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.

  7. 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.

  8. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

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

  9. 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.

  10. Androgen receptor profiling predicts prostate cancer outcome

    NARCIS (Netherlands)

    S. Stelloo (Suzan); E. Nevedomskaya (Ekaterina); H.G. van der Poel (Henk G.); J. de Jong (Jeroen); G.J.H.L. Leenders (Geert); G.W. Jenster (Guido); L. Wessels (Lodewyk); A.M. Bergman (Andries); W. Zwart (Wilbert)

    2015-01-01

    textabstractProstate 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

  11. Predictive and therapeutic markers in ovarian cancer

    Science.gov (United States)

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

    2013-03-26

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

  12. Advanced quality prediction model for software architectural knowledge sharing

    NARCIS (Netherlands)

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

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

  13. Software

    Energy Technology Data Exchange (ETDEWEB)

    Macedo, R.; Budd, G.; Ross, E.; Wells, P.

    2010-07-15

    The software section of this journal presented new software programs that have been developed to help in the exploration and development of hydrocarbon resources. Software provider IHS Inc. has made additions to its geological and engineering analysis software tool, IHS PETRA, a product used by geoscientists and engineers to visualize, analyze and manage well production, well log, drilling, reservoir, seismic and other related information. IHS PETRA also includes a directional well module and a decline curve analysis module to improve analysis capabilities in unconventional reservoirs. Petris Technology Inc. has developed a software to help manage the large volumes of data. PetrisWinds Enterprise (PWE) helps users find and manage wellbore data, including conventional wireline and MWD core data; analysis core photos and images; waveforms and NMR; and external files documentation. Ottawa-based Ambercore Software Inc. has been collaborating with Nexen on the Petroleum iQ software for steam assisted gravity drainage (SAGD) producers. Petroleum iQ integrates geology and geophysics data with engineering data in 3D and 4D. Calgary-based Envirosoft Corporation has developed a software that reduces the costly and time-consuming effort required to comply with Directive 39 of the Alberta Energy Resources Conservation Board. The product includes an emissions modelling software. Houston-based Seismic Micro-Technology (SMT) has developed the Kingdom software that features the latest in seismic interpretation. Holland-based Joa Oil and Gas and Calgary-based Computer Modelling Group have both supplied the petroleum industry with advanced reservoir simulation software that enables reservoir interpretation. The 2010 software survey included a guide to new software applications designed to facilitate petroleum exploration, drilling and production activities. Oil and gas producers can use the products for a range of functions, including reservoir characterization and accounting. In

  14. Ovarian Autoantibodies Predict Ovarian Cancer

    Science.gov (United States)

    2010-11-01

    Expression of thymidine 459 phosphorylase in epithelial ovarian cancer: correlation with angiogenesis, apoptosis , and 460 ultrasound-derived peak...trafficking, activation of S1P1 can promote or inhibit apoptosis of 41 immune cells depending on the balance of cytokines [7]. Knockout of S1P1 (LP(B1...EDG-1) in 42 mice is embryologically lethal [8]. S1P1 also has a role in inflammatory disease such as graft 43 versus host disease and multiple

  15. Architecture of 'Ecoview' software for prediction of impurities migration in soil

    International Nuclear Information System (INIS)

    Kundas, S.P.; Kovalenko, V.I.

    2008-01-01

    Architecture of developing software 'EcoView' for prediction of impurity migration in soil is based on hybrid expert system and includes several fuzzy instruments. Besides it modules of physic-mathematical modeling, geographic information system, data base for storing necessary information (maps, various experimental data, calculating results etc.) are incorporated in software. (authors)

  16. 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.

  17. Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies

    Directory of Open Access Journals (Sweden)

    Aakanshi Gupta

    2018-05-01

    Full Text Available The current era demands high quality software in a limited time period to achieve new goals and heights. To meet user requirements, the source codes undergo frequent modifications which can generate the bad smells in software that deteriorate the quality and reliability of software. Source code of the open source software is easily accessible by any developer, thus frequently modifiable. In this paper, we have proposed a mathematical model to predict the bad smells using the concept of entropy as defined by the Information Theory. Open-source software Apache Abdera is taken into consideration for calculating the bad smells. Bad smells are collected using a detection tool from sub components of the Apache Abdera project, and different measures of entropy (Shannon, Rényi and Tsallis entropy. By applying non-linear regression techniques, the bad smells that can arise in the future versions of software are predicted based on the observed bad smells and entropy measures. The proposed model has been validated using goodness of fit parameters (prediction error, bias, variation, and Root Mean Squared Prediction Error (RMSPE. The values of model performance statistics ( R 2 , adjusted R 2 , Mean Square Error (MSE and standard error also justify the proposed model. We have compared the results of the prediction model with the observed results on real data. The results of the model might be helpful for software development industries and future researchers.

  18. OncoTREAT: a software assistant for cancer therapy monitoring

    International Nuclear Information System (INIS)

    Bornemann, Lars; Dicken, Volker; Kuhnigk, Jan-Martin; Krass, Stefan; Peitgen, Heinz-Otto; Wormanns, Dag; Shin, Hoen-Oh; Bauknecht, Hans-Christian; Diehl, Volker; Fabel, Michael; Meier, Stefan; Kress, Oliver

    2007-01-01

    ObjectCancer is one of the leading causes of death worldwide and therapy options are often associated with severe stress for the patient and high costs. Therefore, precise evaluation of therapy success is essential. Material and Methods In the framework of the VICORA research project (Virtual Institute for Computer Assistance in Clinical Radiology), a software application was developed to support the radiologist in evaluating the response to tumor therapy. The application provides follow-up support for oncological therapy monitoring by volumetric quantification of lung, liver and brain metastases as well as enlarged lymph nodes and assists the user by temporal registration of lesion positions. Results With close cooperation between computer scientists and radiologists the application was tested and optimized to achieve a high degree of usability. Several clinical studies were carried out to evaluate the robustness and reproducibility of the volumetry methods. Conclusion Automatic volumetry and segmentation allows reliable detection of tumor growth and has the potential to increase reliability and significance of monitoring tumor growth in follow-up examinations. (orig.)

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

    Science.gov (United States)

    Kumudha, P; Venkatesan, R

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

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

    International Nuclear Information System (INIS)

    Johnson, G.; Lawrence, D.; Yu, H.

    2000-01-01

    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

  1. Failure mitigation in software defined networking employing load type prediction

    KAUST Repository

    Bouacida, Nader

    2017-07-31

    The controller is a critical piece of the SDN architecture, where it is considered as the mastermind of SDN networks. Thus, its failure will cause a significant portion of the network to fail. Overload is one of the common causes of failure since the controller is frequently invoked by new flows. Even through SDN controllers are often replicated, the significant recovery time can be an overkill for the availability of the entire network. In order to overcome the problem of the overloaded controller failure in SDN, this paper proposes a novel controller offload solution for failure mitigation based on a prediction module that anticipates the presence of a harmful long-term load. In fact, the long-standing load would eventually overwhelm the controller leading to a possible failure. To predict whether the load in the controller is short-term or long-term load, we used three different classification algorithms: Support Vector Machine, k-Nearest Neighbors, and Naive Bayes. Our evaluation results demonstrate that Support Vector Machine algorithm is applicable for detecting the type of load with an accuracy of 97.93% in a real-time scenario. Besides, our scheme succeeded to offload the controller by switching between the reactive and proactive mode in response to the prediction module output.

  2. Network information improves cancer outcome prediction.

    Science.gov (United States)

    Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

    2014-07-01

    Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  3. 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

  4. SOFTWARE EFFORT PREDICTION: AN EMPIRICAL EVALUATION OF METHODS TO TREAT MISSING VALUES WITH RAPIDMINER ®

    OpenAIRE

    OLGA FEDOTOVA; GLADYS CASTILLO; LEONOR TEIXEIRA; HELENA ALVELOS

    2011-01-01

    Missing values is a common problem in the data analysis in all areas, being software engineering not an exception. Particularly, missing data is a widespread phenomenon observed during the elaboration of effort prediction models (EPMs) required for budget, time and functionalities planning. Current work presents the results of a study carried out on a Portuguese medium-sized software development organization in order to obtain a formal method for EPMs elicitation in development processes. Thi...

  5. Discrete Address Beacon System (DABS) Software System Reliability Modeling and Prediction.

    Science.gov (United States)

    1981-06-01

    Service ( ATARS ) module because of its interim status. Reliability prediction models for software modules were derived and then verified by matching...System (A’iCR3BS) and thus can be introduced gradually and economically without ma jor olper- ational or procedural change. Since DABS uses monopulse...lineanaly- sis tools or are ured during maintenance or pre-initialization were not modeled because they are not part of the mission software. The ATARS

  6. Utility of ck metrics in predicting size of board-based software games

    International Nuclear Information System (INIS)

    Sabhat, N.; Azam, F.; Malik, A.A.

    2017-01-01

    Software size is one of the most important inputs of many software cost and effort estimation models. Early estimation of software plays an important role at the time of project inception. An accurate estimate of software size is, therefore, crucial for planning, managing, and controlling software development projects dealing with the development of software games. However, software size is unavailable during early phase of software development. This research determines the utility of CK (Chidamber and Kemerer) metrics, a well-known suite of object-oriented metrics, in estimating the size of software applications using the information from its UML (Unified Modeling Language) class diagram. This work focuses on a small subset dealing with board-based software games. Almost sixty games written using an object-oriented programming language are downloaded from open source repositories, analyzed and used to calibrate a regression-based size estimation model. Forward stepwise MLR (Multiple Linear Regression) is used for model fitting. The model thus obtained is assessed using a variety of accuracy measures such as MMRE (Mean Magnitude of Relative Error), Prediction of x(PRED(x)), MdMRE (Median of Relative Error) and validated using K-fold cross validation. The accuracy of this model is also compared with an existing model tailored for size estimation of board games. Based on a small subset of desktop games developed in various object-oriented languages, we obtained a model using CK metrics and forward stepwise multiple linear regression with reasonable estimation accuracy as indicated by the value of the coefficient of determination (R2 = 0.756).Comparison results indicate that the existing size estimation model outperforms the model derived using CK metrics in terms of accuracy of prediction. (author)

  7. Prognostic and predictive factors in colorectal cancer.

    Science.gov (United States)

    Bolocan, A; Ion, D; Ciocan, D N; Paduraru, D N

    2012-01-01

    Colorectal cancer (CRC) is an important public health problem; it is a leading cause of cancer mortality in the industrialized world, second to lung cancer: each year there are nearly one million new cases of CRC diagnosed worldwide and half a million deaths (1). This review aims to summarise the most important currently available markers for CRC that provide prognostic or predictive information. Amongst others, it covers serum markers such as CEA and CA19-9, markers expressed by tumour tissues, such as thymidylate synthase, and also the expression/loss of expression of certain oncogenes and tumour suppressor genes such as K-ras and p53. The prognostic value of genomic instability, angiogenesis and proliferative indices, such as the apoptotic index, are discussed. The advent of new therapies created the pathway for a personalized approach of the patient. This will take into consideration the complex genetic mechanisms involved in tumorigenesis, besides the classical clinical and pathological stagings. The growing number of therapeutic agents and known molecular targets in oncology lead to a compulsory study of the clinical use of biomarkers with role in improving response and survival, as well as in reducing toxicity and establishing economic stability. The potential predictive and prognostic biomarkers which have arisen from the study of the genetic basis of colorectal cancer and their therapeutical significance are discussed. RevistaChirurgia.

  8. Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

    OpenAIRE

    Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor, Maureen

    2014-01-01

    We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial i...

  9. 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.

  10. Research on cross - Project software defect prediction based on transfer learning

    Science.gov (United States)

    Chen, Ya; Ding, Xiaoming

    2018-04-01

    According to the two challenges in the prediction of cross-project software defects, the distribution differences between the source project and the target project dataset and the class imbalance in the dataset, proposing a cross-project software defect prediction method based on transfer learning, named NTrA. Firstly, solving the source project data's class imbalance based on the Augmented Neighborhood Cleaning Algorithm. Secondly, the data gravity method is used to give different weights on the basis of the attribute similarity of source project and target project data. Finally, a defect prediction model is constructed by using Trad boost algorithm. Experiments were conducted using data, come from NASA and SOFTLAB respectively, from a published PROMISE dataset. The results show that the method has achieved good values of recall and F-measure, and achieved good prediction results.

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

    International Nuclear Information System (INIS)

    Hu, Q.P.; Xie, M.; Ng, S.H.; Levitin, G.

    2007-01-01

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

  12. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

    Science.gov (United States)

    Huang, Cai; Mezencev, Roman; McDonald, John F; Vannberg, Fredrik

    2017-01-01

    Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

  13. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

    Directory of Open Access Journals (Sweden)

    Cai Huang

    Full Text Available Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM algorithm combined with a standard recursive feature elimination (RFE approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60. The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

  14. 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…

  15. Microarray-based cancer prediction using soft computing approach.

    Science.gov (United States)

    Wang, Xiaosheng; Gotoh, Osamu

    2009-05-26

    One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

  16. P-MartCancer–Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets

    Energy Technology Data Exchange (ETDEWEB)

    Webb-Robertson, Bobbie-Jo M.; Bramer, Lisa M.; Jensen, Jeffrey L.; Kobold, Markus A.; Stratton, Kelly G.; White, Amanda M.; Rodland, Karin D.

    2017-10-31

    P-MartCancer is a new interactive web-based software environment that enables biomedical and biological scientists to perform in-depth analyses of global proteomics data without requiring direct interaction with the data or with statistical software. P-MartCancer offers a series of statistical modules associated with quality assessment, peptide and protein statistics, protein quantification and exploratory data analyses driven by the user via customized workflows and interactive visualization. Currently, P-MartCancer offers access to multiple cancer proteomic datasets generated through the Clinical Proteomics Tumor Analysis Consortium (CPTAC) at the peptide, gene and protein levels. P-MartCancer is deployed using Azure technologies (http://pmart.labworks.org/cptac.html), the web-service is alternatively available via Docker Hub (https://hub.docker.com/r/pnnl/pmart-web/) and many statistical functions can be utilized directly from an R package available on GitHub (https://github.com/pmartR).

  17. 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

  18. 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.

  19. Software Tools | Office of Cancer Clinical Proteomics Research

    Science.gov (United States)

    The CPTAC program develops new approaches to elucidate aspects of the molecular complexity of cancer made from large-scale proteogenomic datasets, and advance them toward precision medicine.  Part of the CPTAC mission is to make data and tools available and accessible to the greater research community to accelerate the discovery process.

  20. Prediction of safety critical software operational reliability from test reliability using testing environment factors

    International Nuclear Information System (INIS)

    Jung, Hoan Sung; Seong, Poong Hyun

    1999-01-01

    It has been a critical issue to predict the safety critical software reliability in nuclear engineering area. For many years, many researches have focused on the quantification of software reliability and there have been 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 however on the operational reliability rather than on the test reliability. 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, from testing to operation, testing environment factors comprising the aging factor and the coverage factor are developed in this paper and used to predict the ultimate operational reliability with the failure data in testing phase. It is by incorporating test environments applied beyond the operational profile into testing environment factors. The application results show that the proposed method can estimate the operational reliability accurately. (Author). 14 refs., 1 tab., 1 fig

  1. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

    Science.gov (United States)

    Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

    2018-01-01

    The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

  2. 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).

  3. 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.

  4. The predictive power of SIMION/SDS simulation software for modeling ion mobility spectrometry instruments

    Science.gov (United States)

    Lai, Hanh; McJunkin, Timothy R.; Miller, Carla J.; Scott, Jill R.; Almirall, José R.

    2008-09-01

    The combined use of SIMION 7.0 and the statistical diffusion simulation (SDS) user program in conjunction with SolidWorks® with COSMSOSFloWorks® fluid dynamics software to model a complete, commercial ion mobility spectrometer (IMS) was demonstrated for the first time and compared to experimental results for tests using compounds of immediate interest in the security industry (e.g., 2,4,6-trinitrotoluene, 2,7-dinitrofluorene, and cocaine). The effort of this research was to evaluate the predictive power of SIMION/SDS for application to IMS instruments. The simulation was evaluated against experimental results in three studies: (1) a drift:carrier gas flow rates study assesses the ability of SIMION/SDS to correctly predict the ion drift times; (2) a drift gas composition study evaluates the accuracy in predicting the resolution; (3) a gate width study compares the simulated peak shape and peak intensity with the experimental values. SIMION/SDS successfully predicted the correct drift time, intensity, and resolution trends for the operating parameters studied. Despite the need for estimations and assumptions in the construction of the simulated instrument, SIMION/SDS was able to predict the resolution between two ion species in air within 3% accuracy. The preliminary success of IMS simulations using SIMION/SDS software holds great promise for the design of future instruments with enhanced performance.

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

    Science.gov (United States)

    Mawatari, Yuki; Fukushima, Mikiko

    2016-01-01

    This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection. Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller's muscle complex (levator resection). Predictive images were prepared from preoperative photographs using the image processing software (Adobe Photoshop ® ). Images of selected eyes were digitally enlarged in an appropriate manner and shown to patients prior to surgery. Approximately 1 month postoperatively, we surveyed our patients using questionnaires. Fifty-six patients (89.2%) were satisfied with their postoperative appearances, and 55 patients (84.8%) positively responded to the usefulness of processed images to predict postoperative appearance. Showing processed images that predict postoperative appearance to patients prior to blepharoptosis surgery can be useful for those patients concerned with their postoperative appearance. This approach may serve as a useful tool to simulate blepharoptosis surgery.

  6. Prostate Cancer Probability Prediction By Machine Learning Technique.

    Science.gov (United States)

    Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

    2017-11-26

    The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

  7. ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes

    Directory of Open Access Journals (Sweden)

    King Brian R

    2012-07-01

    Full Text Available Abstract Background Understanding protein subcellular localization is a necessary component toward understanding the overall function of a protein. Numerous computational methods have been published over the past decade, with varying degrees of success. Despite the large number of published methods in this area, only a small fraction of them are available for researchers to use in their own studies. Of those that are available, many are limited by predicting only a small number of organelles in the cell. Additionally, the majority of methods predict only a single location for a sequence, even though it is known that a large fraction of the proteins in eukaryotic species shuttle between locations to carry out their function. Findings We present a software package and a web server for predicting the subcellular localization of protein sequences based on the ngLOC method. ngLOC is an n-gram-based Bayesian classifier that predicts subcellular localization of proteins both in prokaryotes and eukaryotes. The overall prediction accuracy varies from 89.8% to 91.4% across species. This program can predict 11 distinct locations each in plant and animal species. ngLOC also predicts 4 and 5 distinct locations on gram-positive and gram-negative bacterial datasets, respectively. Conclusions ngLOC is a generic method that can be trained by data from a variety of species or classes for predicting protein subcellular localization. The standalone software is freely available for academic use under GNU GPL, and the ngLOC web server is also accessible at http://ngloc.unmc.edu.

  8. EPILAB: a software package for studies on the prediction of epileptic seizures.

    Science.gov (United States)

    Teixeira, C A; Direito, B; Feldwisch-Drentrup, H; Valderrama, M; Costa, R P; Alvarado-Rojas, C; Nikolopoulos, S; Le Van Quyen, M; Timmer, J; Schelter, B; Dourado, A

    2011-09-15

    A Matlab®-based software package, EPILAB, was developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented. This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines. EPILAB proved to be an efficient tool for seizure prediction, and aims to be a way to communicate, evaluate, and compare results and data among the seizure prediction community. Copyright © 2011 Elsevier B.V. All rights reserved.

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

    International Nuclear Information System (INIS)

    Goh, Vicky; Shastry, Manu; Endozo, Raymondo; Groves, Ashley M.; Engledow, Alec; Peck, Jacqui; Reston, Jonathan; Wellsted, David M.; Rodriguez-Justo, Manuel; Taylor, Stuart A.; Halligan, Steve

    2011-01-01

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

  10. Individual Prediction of Heart Failure Among Childhood Cancer Survivors

    NARCIS (Netherlands)

    Chow, Eric J.; Chen, Yan; Kremer, Leontien C.; Breslow, Norman E.; Hudson, Melissa M.; Armstrong, Gregory T.; Border, William L.; Feijen, Elizabeth A. M.; Green, Daniel M.; Meacham, Lillian R.; Meeske, Kathleen A.; Mulrooney, Daniel A.; Ness, Kirsten K.; Oeffinger, Kevin C.; Sklar, Charles A.; Stovall, Marilyn; van der Pal, Helena J.; Weathers, Rita E.; Robison, Leslie L.; Yasui, Yutaka

    2015-01-01

    Purpose To create clinically useful models that incorporate readily available demographic and cancer treatment characteristics to predict individual risk of heart failure among 5-year survivors of childhood cancer. Patients and Methods Survivors in the Childhood Cancer Survivor Study (CCSS) free of

  11. SPOCS: Software for Predicting and Visualizing Orthology/Paralogy Relationships Among Genomes

    Energy Technology Data Exchange (ETDEWEB)

    Curtis, Darren S.; Phillips, Aaron R.; Callister, Stephen J.; Conlan, Sean; McCue, Lee Ann

    2013-10-15

    At the rate that prokaryotic genomes can now be generated, comparative genomics studies require a flexible method for quickly and accurately predicting orthologs among the rapidly changing set of genomes available. SPOCS implements a graph-based ortholog prediction method to generate a simple tab-delimited table of orthologs and in addition, html files that provide a visualization of the predicted ortholog/paralog relationships to which gene/protein expression metadata may be overlaid. AVAILABILITY AND IMPLEMENTATION: A SPOCS web application is freely available at http://cbb.pnnl.gov/portal/tools/spocs.html. Source code for Linux systems is also freely available under an open source license at http://cbb.pnnl.gov/portal/software/spocs.html; the Boost C++ libraries and BLAST are required.

  12. 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.

  13. Fast and predictable video compression in software design and implementation of an H.261 codec

    Science.gov (United States)

    Geske, Dagmar; Hess, Robert

    1998-09-01

    The use of software codecs for video compression becomes commonplace in several videoconferencing applications. In order to reduce conflicts with other applications used at the same time, mechanisms for resource reservation on endsystems need to determine an upper bound for computing time used by the codec. This leads to the demand for predictable execution times of compression/decompression. Since compression schemes as H.261 inherently depend on the motion contained in the video, an adaptive admission control is required. This paper presents a data driven approach based on dynamical reduction of the number of processed macroblocks in peak situations. Besides the absolute speed is a point of interest. The question, whether and how software compression of high quality video is feasible on today's desktop computers, is examined.

  14. A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance.

    Science.gov (United States)

    Jones, Stephen; Cournane, Seán; Sheehy, Niall; Hederman, Lucy

    2016-12-01

    Business analytics (BA) is increasingly being utilised by radiology departments to analyse and present data. It encompasses statistical analysis, forecasting and predictive modelling and is used as an umbrella term for decision support and business intelligence systems. The primary aim of this study was to determine whether utilising BA technologies could contribute towards improved decision support and resource management within radiology departments. A set of information technology requirements were identified with key stakeholders, and a prototype BA software tool was designed, developed and implemented. A qualitative evaluation of the tool was carried out through a series of semi-structured interviews with key stakeholders. Feedback was collated, and emergent themes were identified. The results indicated that BA software applications can provide visibility of radiology performance data across all time horizons. The study demonstrated that the tool could potentially assist with improving operational efficiencies and management of radiology resources.

  15. Breast Cancer Patients' Depression Prediction by Machine Learning Approach.

    Science.gov (United States)

    Cvetković, Jovana

    2017-09-14

    One of the most common cancer in females is breasts cancer. This cancer can has high impact on the women including health and social dimensions. One of the most common social dimension is depression caused by breast cancer. Depression can impairs life quality. Depression is one of the symptom among the breast cancer patients. One of the solution is to eliminate the depression in breast cancer patients is by treatments but these treatments can has different unpredictable impacts on the patients. Therefore it is suitable to develop algorithm in order to predict the depression range.

  16. Comparison of Perfusion CT Software to Predict the Final Infarct Volume After Thrombectomy.

    Science.gov (United States)

    Austein, Friederike; Riedel, Christian; Kerby, Tina; Meyne, Johannes; Binder, Andreas; Lindner, Thomas; Huhndorf, Monika; Wodarg, Fritz; Jansen, Olav

    2016-09-01

    Computed tomographic perfusion represents an interesting physiological imaging modality to select patients for reperfusion therapy in acute ischemic stroke. The purpose of our study was to determine the accuracy of different commercial perfusion CT software packages (Philips (A), Siemens (B), and RAPID (C)) to predict the final infarct volume (FIV) after mechanical thrombectomy. Single-institutional computed tomographic perfusion data from 147 mechanically recanalized acute ischemic stroke patients were postprocessed. Ischemic core and FIV were compared about thrombolysis in cerebral infarction (TICI) score and time interval to reperfusion. FIV was measured at follow-up imaging between days 1 and 8 after stroke. In 118 successfully recanalized patients (TICI 2b/3), a moderately to strongly positive correlation was observed between ischemic core and FIV. The highest accuracy and best correlation are shown in early and fully recanalized patients (Pearson r for A=0.42, B=0.64, and C=0.83; P<0.001). Bland-Altman plots and boxplots demonstrate smaller ranges in package C than in A and B. Significant differences were found between the packages about over- and underestimation of the ischemic core. Package A, compared with B and C, estimated more than twice as many patients with a malignant stroke profile (P<0.001). Package C best predicted hypoperfusion volume in nonsuccessfully recanalized patients. Our study demonstrates best accuracy and approximation between the results of a fully automated software (RAPID) and FIV, especially in early and fully recanalized patients. Furthermore, this software package overestimated the FIV to a significantly lower degree and estimated a malignant mismatch profile less often than other software. © 2016 American Heart Association, Inc.

  17. FreeContact: fast and free software for protein contact prediction from residue co-evolution.

    Science.gov (United States)

    Kaján, László; Hopf, Thomas A; Kalaš, Matúš; Marks, Debora S; Rost, Burkhard

    2014-03-26

    20 years of improved technology and growing sequences now renders residue-residue contact constraints in large protein families through correlated mutations accurate enough to drive de novo predictions of protein three-dimensional structure. The method EVfold broke new ground using mean-field Direct Coupling Analysis (EVfold-mfDCA); the method PSICOV applied a related concept by estimating a sparse inverse covariance matrix. Both methods (EVfold-mfDCA and PSICOV) are publicly available, but both require too much CPU time for interactive applications. On top, EVfold-mfDCA depends on proprietary software. Here, we present FreeContact, a fast, open source implementation of EVfold-mfDCA and PSICOV. On a test set of 140 proteins, FreeContact was almost eight times faster than PSICOV without decreasing prediction performance. The EVfold-mfDCA implementation of FreeContact was over 220 times faster than PSICOV with negligible performance decrease. EVfold-mfDCA was unavailable for testing due to its dependency on proprietary software. FreeContact is implemented as the free C++ library "libfreecontact", complete with command line tool "freecontact", as well as Perl and Python modules. All components are available as Debian packages. FreeContact supports the BioXSD format for interoperability. FreeContact provides the opportunity to compute reliable contact predictions in any environment (desktop or cloud).

  18. SVM and SVM Ensembles in Breast Cancer Prediction

    OpenAIRE

    Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

    2017-01-01

    Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction per...

  19. Predictive values of symptoms in relation to cancer diagnosis

    DEFF Research Database (Denmark)

    Krasnik, Ivan; Andersen, John Sahl

    a manual describing the symptoms that should engender reasonable suspicion of malignancy (“alarm symptoms”) to the general practitioner. Objectives: To investigate the evidence in the literature of the predictive value (PPV) placed on the”alarm symptoms” for colon cancer, breast cancer, prostate cancer...... years (6,6%-21,2%), but much lower in younger age groups. ”Change in bowel habits” and ”Significant general symptoms” are more uncertain (3,5%-8,5%). Breast cancer: ”Palpable suspect tumor” is well supported (8,1%-24%). The predictive value of ”Pitting of the skin”, ”Papil-areola eczema......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...

  20. Prediction Model for Gastric Cancer Incidence in Korean Population.

    Directory of Open Access Journals (Sweden)

    Bang Wool Eom

    Full Text Available 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.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 prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.During a median of 11.4 years of follow-up, 19,465 (1.4% and 5,579 (0.7% newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women.In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.

  1. 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

    of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators. Results: A statistical...... significant trend (p classification accuracy (from 62.6 to 70.0%) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2%. Conclusion: Best...... classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented....

  2. Observed and Predicted Risk of Breast Cancer Death in Randomized Trials on Breast Cancer Screening.

    Science.gov (United States)

    Autier, Philippe; Boniol, Mathieu; Smans, Michel; Sullivan, Richard; Boyle, Peter

    2016-01-01

    The role of breast screening in breast cancer mortality declines is debated. Screening impacts cancer mortality through decreasing the number of advanced cancers with poor diagnosis, while cancer treatment works through decreasing the case-fatality rate. Hence, reductions in cancer death rates thanks to screening should directly reflect reductions in advanced cancer rates. We verified whether in breast screening trials, the observed reductions in the risk of breast cancer death could be predicted from reductions of advanced breast cancer rates. The Greater New York Health Insurance Plan trial (HIP) is the only breast screening trial that reported stage-specific cancer fatality for the screening and for the control group separately. The Swedish Two-County trial (TCT)) reported size-specific fatalities for cancer patients in both screening and control groups. We computed predicted numbers of breast cancer deaths, from which we calculated predicted relative risks (RR) and (95% confidence intervals). The Age trial in England performed its own calculations of predicted relative risk. The observed and predicted RR of breast cancer death were 0.72 (0.56-0.94) and 0.98 (0.77-1.24) in the HIP trial, and 0.79 (0.78-1.01) and 0.90 (0.80-1.01) in the Age trial. In the TCT, the observed RR was 0.73 (0.62-0.87), while the predicted RR was 0.89 (0.75-1.05) if overdiagnosis was assumed to be negligible and 0.83 (0.70-0.97) if extra cancers were excluded. In breast screening trials, factors other than screening have contributed to reductions in the risk of breast cancer death most probably by reducing the fatality of advanced cancers in screening groups. These factors were the better management of breast cancer patients and the underreporting of breast cancer as the underlying cause of death. Breast screening trials should publish stage-specific fatalities observed in each group.

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

    Directory of Open Access Journals (Sweden)

    Mawatari Y

    2016-09-01

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

  4. PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer.

    Science.gov (United States)

    Wishart, Gordon C; Azzato, Elizabeth M; Greenberg, David C; Rashbass, Jem; Kearins, Olive; Lawrence, Gill; Caldas, Carlos; Pharoah, Paul D P

    2010-01-01

    The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. Differences in overall actual and predicted mortality were detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.

  5. 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....

  6. Prediction of Bladder Cancer Recurrences Using Artificial Neural Networks

    Science.gov (United States)

    Zulueta Guerrero, Ekaitz; Garay, Naiara Telleria; Lopez-Guede, Jose Manuel; Vilches, Borja Ayerdi; Iragorri, Eider Egilegor; Castaños, David Lecumberri; de La Hoz Rastrollo, Ana Belén; Peña, Carlos Pertusa

    Even if considerable advances have been made in the field of early diagnosis, there is no simple, cheap and non-invasive method that can be applied to the clinical monitorisation of bladder cancer patients. Moreover, bladder cancer recurrences or the reappearance of the tumour after its surgical resection cannot be predicted in the current clinical setting. In this study, Artificial Neural Networks (ANN) were used to assess how different combinations of classical clinical parameters (stage-grade and age) and two urinary markers (growth factor and pro-inflammatory mediator) could predict post surgical recurrences in bladder cancer patients. Different ANN methods, input parameter combinations and recurrence related output variables were used and the resulting positive and negative prediction rates compared. MultiLayer Perceptron (MLP) was selected as the most predictive model and urinary markers showed the highest sensitivity, predicting correctly 50% of the patients that would recur in a 2 year follow-up period.

  7. xSyn: A Software Tool for Identifying Sophisticated 3-Way Interactions From Cancer Expression Data

    Directory of Open Access Journals (Sweden)

    Baishali Bandyopadhyay

    2017-08-01

    Full Text Available Background: Constructing gene co-expression networks from cancer expression data is important for investigating the genetic mechanisms underlying cancer. However, correlation coefficients or linear regression models are not able to model sophisticated relationships among gene expression profiles. Here, we address the 3-way interaction that 2 genes’ expression levels are clustered in different space locations under the control of a third gene’s expression levels. Results: We present xSyn, a software tool for identifying such 3-way interactions from cancer gene expression data based on an optimization procedure involving the usage of UPGMA (Unweighted Pair Group Method with Arithmetic Mean and synergy. The effectiveness is demonstrated by application to 2 real gene expression data sets. Conclusions: xSyn is a useful tool for decoding the complex relationships among gene expression profiles. xSyn is available at http://www.bdxconsult.com/xSyn.html .

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-07-01

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

  9. 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.

  10. Quality Market: Design and Field Study of Prediction Market for Software Quality Control

    Science.gov (United States)

    Krishnamurthy, Janaki

    2010-01-01

    Given the increasing competition in the software industry and the critical consequences of software errors, it has become important for companies to achieve high levels of software quality. While cost reduction and timeliness of projects continue to be important measures, software companies are placing increasing attention on identifying the user…

  11. Peritumoral eosinophils predict recurrence in colorectal cancer.

    Science.gov (United States)

    Harbaum, Lars; Pollheimer, Marion J; Kornprat, Peter; Lindtner, Richard A; Bokemeyer, Carsten; Langner, Cord

    2015-03-01

    In colorectal cancer, the presence and extent of eosinophil granulocyte infiltration may render important prognostic information. However, it remains unclear whether an increasing number of eosinophils might simply be linked to the overall inflammatory cell reaction or represent a self-contained, antitumoral mechanism that needs to be documented and promoted therapeutically. Peri- and intratumoral eosinophil counts were retrospectively assessed in 381 primary colorectal cancers from randomly selected patients. Tumors were diagnosed in American Joint Committee on Cancer (AJCC)/Union Internationale Contre le Cancer (UICC) stage I in 21%, stage II in 32%, stage III in 33%, and stage IV in 14%. Presence and extent of eosinophils was related to various histopathological parameters as well as patients' outcome. Overall, peri- and intratumoral eosinophils were observed in 86 and 75% cancer specimens. The peritumoral eosinophil count correlated strongly with the intratumoral eosinophil count (R=0.69; Peosinophil counts were significantly associated with lower T and N classification, better tumor differentiation, absence of vascular invasion, as well as improved progression-free and cancer-specific survival. However, only peritumoral eosinophils, but not intratumoral, were an independent prognosticator of favorable progression-free (hazard ratio 0.75; 95% confidence interval 0.58-0.98; P=0.04) and cancer-specific survival (hazard ratio 0.7; 95% confidence interval 0.52-0.93; P=0.01)-independent of the intensity of overall inflammatory cell reaction. This was also found for patients with AJCC/UICC stage II disease, wherein the presence of peritumoral eosinophils was significantly associated with favorable outcome. In conclusion, the number of peritumoral eosinophils had a significant favorable impact on prognosis of colorectal cancer patients independent of the overall tumor-associated inflammatory response. Evaluation of peritumoral eosinophils represents a promising

  12. Predicting the probability of mortality of gastric cancer patients using decision tree.

    Science.gov (United States)

    Mohammadzadeh, F; Noorkojuri, H; Pourhoseingholi, M A; Saadat, S; Baghestani, A R

    2015-06-01

    Gastric cancer is the fourth most common cancer worldwide. This reason motivated us to investigate and introduce gastric cancer risk factors utilizing statistical methods. The aim of this study was to identify the most important factors influencing the mortality of patients who suffer from gastric cancer disease and to introduce a classification approach according to decision tree model for predicting the probability of mortality from this disease. Data on 216 patients with gastric cancer, who were registered in Taleghani hospital in Tehran,Iran, were analyzed. At first, patients were divided into two groups: the dead and alive. Then, to fit decision tree model to our data, we randomly selected 20% of dataset to the test sample and remaining dataset considered as the training sample. Finally, the validity of the model examined with sensitivity, specificity, diagnosis accuracy and the area under the receiver operating characteristic curve. The CART version 6.0 and SPSS version 19.0 softwares were used for the analysis of the data. Diabetes, ethnicity, tobacco, tumor size, surgery, pathologic stage, age at diagnosis, exposure to chemical weapons and alcohol consumption were determined as effective factors on mortality of gastric cancer. The sensitivity, specificity and accuracy of decision tree were 0.72, 0.75 and 0.74 respectively. The indices of sensitivity, specificity and accuracy represented that the decision tree model has acceptable accuracy to prediction the probability of mortality in gastric cancer patients. So a simple decision tree consisted of factors affecting on mortality of gastric cancer may help clinicians as a reliable and practical tool to predict the probability of mortality in these patients.

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

    International Nuclear Information System (INIS)

    Luo Lijuan; Chen Bo; Zhuo Weihai; Lu Shuyu

    2011-01-01

    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)

  14. Predictive factors of thyroid cancer in patients with Graves' disease.

    Science.gov (United States)

    Ren, Meng; Wu, Mu Chao; Shang, Chang Zhen; Wang, Xiao Yi; Zhang, Jing Lu; Cheng, Hua; Xu, Ming Tong; Yan, Li

    2014-01-01

    The best preoperative examination in Graves' disease with thyroid cancer still remains uncertain. The objectives of the present study were to investigate the prevalence of thyroid cancer in Graves' disease patients, and to identify the predictive factors and ultrasonographic features of thyroid cancer that may aid the preoperative diagnosis in Graves' disease. This retrospective study included 423 patients with Graves' disease who underwent surgical treatment from 2002 to 2012 at our institution. The clinical features and ultrasonographic findings of thyroid nodules were recorded. The diagnosis of thyroid cancer was determined according to the pathological results. Thyroid cancer was discovered in 58 of the 423 (13.7 %) surgically treated Graves' disease patients; 46 of those 58 patients had thyroid nodules, and the other 12 patients were diagnosed with incidentally discovered thyroid carcinomas without thyroid nodules. Among the 58 patients with thyroid cancer, papillary microcarcinomas were discovered in 50 patients, and multifocality and lymph node involvement were detected in the other 8 patients. Multivariate regression analysis showed younger age was the only significant factor predictive of metastatic thyroid cancer. Ultrasonographic findings of calcification and intranodular blood flow in thyroid nodules indicate that they are more likely to harbor thyroid cancers. Because the influencing factor of metastatic thyroid cancers in Graves' disease is young age, every suspicious nodule in Graves' disease patients should be evaluated and treated carefully, especially in younger patients because of the potential for metastasis.

  15. Biomarkers for predicting complete debulking in ovarian cancer

    DEFF Research Database (Denmark)

    Fagö-Olsen, Carsten Lindberg; Ottesen, Bent; Christensen, Ib Jarle

    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...... used to construct predictive indices for complete tumour resection; Part II: sera from randomly selected patients from part I were analyzed using enzyme-linked immunosorbent assay (ELISA) to investigate the correlation to mass spectrometry; Part III: the indices from part I were validated in a new.......64. CONCLUSION: Our validated model based on biomarkers was unable to predict surgical outcome for patients with ovarian cancer....

  16. Implementation of Chaotic Gaussian Particle Swarm Optimization for Optimize Learning-to-Rank Software Defect Prediction Model Construction

    Science.gov (United States)

    Buchari, M. A.; Mardiyanto, S.; Hendradjaya, B.

    2018-03-01

    Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.

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

    International Nuclear Information System (INIS)

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

    2013-01-01

    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 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)

  18. SVM and SVM Ensembles in Breast Cancer Prediction.

    Science.gov (United States)

    Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

    2017-01-01

    Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

  19. Stage-specific predictive models for breast cancer survivability.

    Science.gov (United States)

    Kate, Rohit J; Nadig, Ramya

    2017-01-01

    Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  20. SVM and SVM Ensembles in Breast Cancer Prediction.

    Directory of Open Access Journals (Sweden)

    Min-Wei Huang

    Full Text Available Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

  1. 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

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

    Directory of Open Access Journals (Sweden)

    Robert J. Peterman

    2016-06-01

    Full Text Available Abstract 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 treated with maxillary advancement and mandibular setback and to validate the efficacy of the software in such complex cases. Methods This retrospective study analyzed the records of 14 patients treated with comprehensive orthodontics in conjunction with two-jaw orthognathic surgery. Pre- and post-treatment radiographs were traced and superimposed to determine the actual skeletal movements achieved in surgery. This information was then used to simulate surgery in the software and generate a final soft tissue patient profile prediction. Prediction images were then compared to the actual post-treatment profile photos to determine differences. Results Dolphin Imaging’s software was determined to be accurate within an error range of +/− 2 mm in the X-axis at most landmarks. The lower lip predictions were most inaccurate. Conclusions Clinically, the observed error suggests that the VTO may be used for demonstration and communication with a patient or consulting practitioner. However, Dolphin should not be useful for precise treatment planning of surgical movements. This program should be used with caution to prevent unrealistic patient expectations and dissatisfaction.

  3. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    Science.gov (United States)

    Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Xi Zhao

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

  5. Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance

    Science.gov (United States)

    Gonzalez de Castro, D; Clarke, P A; Al-Lazikani, B; Workman, P

    2013-01-01

    The progressive elucidation of the molecular pathogenesis of cancer has fueled the rational development of targeted drugs for patient populations stratified by genetic characteristics. Here we discuss general challenges relating to molecular diagnostics and describe predictive biomarkers for personalized cancer medicine. We also highlight resistance mechanisms for epidermal growth factor receptor (EGFR) kinase inhibitors in lung cancer. We envisage a future requiring the use of longitudinal genome sequencing and other omics technologies alongside combinatorial treatment to overcome cellular and molecular heterogeneity and prevent resistance caused by clonal evolution. PMID:23361103

  6. Predicting Scheduling and Attending for an Oral Cancer Examination

    Science.gov (United States)

    Shepperd, James A.; Emanuel, Amber S.; Howell, Jennifer L.; Logan, Henrietta L.

    2015-01-01

    Background Oral and pharyngeal cancer is highly treatable if diagnosed early, yet late diagnosis is commonplace apparently because of delays in undergoing an oral cancer examination. Purpose We explored predictors of scheduling and attending an oral cancer examination among a sample of Black and White men who were at high risk for oral cancer because they smoked. Methods During an in-person interview, participants (N = 315) from rural Florida learned about oral and pharyngeal cancer, completed survey measures, and were offered a free examination in the next week. Later, participants received a follow-up phone call to explore why they did or did not attend their examination. Results Consistent with the notion that scheduling and attending an oral cancer exam represent distinct decisions, we found that the two outcomes had different predictors. Defensive avoidance and exam efficacy predicted scheduling an examination; exam efficacy and having coping resources, time, and transportation predicted attending the examination. Open-ended responses revealed that the dominant reasons participants offered for missing a scheduled examination was conflicting obligations, forgetting, and confusion or misunderstanding about the examination. Conclusions The results suggest interventions to increase scheduling and attending an oral cancer examination. PMID:26152644

  7. Machine learning applications in cancer prognosis and prediction.

    Science.gov (United States)

    Kourou, Konstantina; Exarchos, Themis P; Exarchos, Konstantinos P; Karamouzis, Michalis V; Fotiadis, Dimitrios I

    2015-01-01

    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.

  8. 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.

  9. Assessing Prediction Performance of Neoadjuvant Chemotherapy Response in Bladder Cancer

    OpenAIRE

    Cremer, Chris

    2016-01-01

    Neoadjuvant chemotherapy is a treatment routinely prescribed to patients diagnosed with muscle-invasive bladder cancer. Unfortunately, not all patients are responsive to this treatment and would greatly benefit from an accurate prediction of their expected response to chemotherapy. In this project, I attempt to develop a model that will predict response using tumour microarray data. I show that using my dataset, every method is insufficient at accurately classifying responders and non-respond...

  10. Prognostic and predictive biomarkers in colorectal cancer. Towards precision medicine

    NARCIS (Netherlands)

    Reimers, Marlies Suzanne

    2015-01-01

    The aim of this thesis was to define prognostic and predictive biomarkers in colorectal cancer for improved risk stratification and treatment benefit in the individual patient, with the introduction of precision medicine in the near future as the ultimate goal. By definition, precision medicine is

  11. 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.

  12. Applications of machine learning in cancer prediction and prognosis.

    Science.gov (United States)

    Cruz, Joseph A; Wishart, David S

    2007-02-11

    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.

  13. Lung cancer in never smokers Epidemiology and risk prediction models

    Science.gov (United States)

    McCarthy, William J.; Meza, Rafael; Jeon, Jihyoun; Moolgavkar, Suresh

    2012-01-01

    In this chapter we review the epidemiology of lung cancer incidence and mortality among never smokers/ nonsmokers and describe the never smoker lung cancer risk models used by CISNET modelers. Our review focuses on those influences likely to have measurable population impact on never smoker risk, such as secondhand smoke, even though the individual-level impact may be small. Occupational exposures may also contribute importantly to the population attributable risk of lung cancer. We examine the following risk factors in this chapter: age, environmental tobacco smoke, cooking fumes, ionizing radiation including radon gas, inherited genetic susceptibility, selected occupational exposures, preexisting lung disease, and oncogenic viruses. We also compare the prevalence of never smokers between the three CISNET smoking scenarios and present the corresponding lung cancer mortality estimates among never smokers as predicted by a typical CISNET model. PMID:22882894

  14. Software Systems for Prediction and Immediate Assessment of Emergency Situations on Municipalities Territories

    Science.gov (United States)

    Poluyan, L. V.; Syutkina, E. V.; Guryev, E. S.

    2017-11-01

    The comparative analysis of key features of the software systems TOXI+Risk and ALOHA is presented. The authors made a comparison of domestic (TOXI+Risk) and foreign (ALOHA) software systems allowing to give the quantitative assessment of impact areas (pressure, thermal, toxic) in case of hypothetical emergencies in potentially hazardous objects of the oil, gas, chemical, petrochemical and oil-processing industry. Both software systems use different mathematical models for assessment of the release rate of a chemically hazardous substance from a storage tank and its evaporation. The comparison of the accuracy of definition of impact areas made by both software systems to verify the examples shows good convergence of both products. The analysis results showed that the ALOHA software can be actively used for forecasting and immediate assessment of emergency situations, assessment of damage as a result of emergencies on the territories of municipalities.

  15. Predicting the Survival of Gastric Cancer Patients Using

    Science.gov (United States)

    Korhani Kangi, Azam; Bahrampour, Abbas

    2018-02-26

    Introduction and purpose: In recent years the use of neural networks without any premises for investigation of prognosis in analyzing survival data has increased. Artificial neural networks (ANN) use small processors with a continuous network to solve problems inspired by the human brain. Bayesian neural networks (BNN) constitute a neural-based approach to modeling and non-linearization of complex issues using special algorithms and statistical methods. Gastric cancer incidence is the first and third ranking for men and women in Iran, respectively. The aim of the present study was to assess the value of an artificial neural network and a Bayesian neural network for modeling and predicting of probability of gastric cancer patient death. Materials and Methods: In this study, we used information on 339 patients aged from 20 to 90 years old with positive gastric cancer, referred to Afzalipoor and Shahid Bahonar Hospitals in Kerman City from 2001 to 2015. The three layers perceptron neural network (ANN) and the Bayesian neural network (BNN) were used for predicting the probability of mortality using the available data. To investigate differences between the models, sensitivity, specificity, accuracy and the area under receiver operating characteristic curves (AUROCs) were generated. Results: In this study, the sensitivity and specificity of the artificial neural network and Bayesian neural network models were 0.882, 0.903 and 0.954, 0.909, respectively. Prediction accuracy and the area under curve ROC for the two models were 0.891, 0.944 and 0.935, 0.961. The age at diagnosis of gastric cancer was most important for predicting survival, followed by tumor grade, morphology, gender, smoking history, opium consumption, receiving chemotherapy, presence of metastasis, tumor stage, receiving radiotherapy, and being resident in a village. Conclusion: The findings of the present study indicated that the Bayesian neural network is preferable to an artificial neural network for

  16. Fear of cancer recurrence and its predictive factors among Iranian cancer patients

    Directory of Open Access Journals (Sweden)

    Alireza Mohajjel Aghdam

    2014-01-01

    Full Text Available Context: Fear of cancer recurrence (FOCR is one of the most important psychological problems among cancer patients. In extensive review of related literature there were no articles on FOCR among Iranian cancer patients. Aim: The aim of present study was to investigation FOCR and its predictive factors among Iranian cancer patients. Materials and Methods: In this descriptive-correlational study 129 cancer patients participated. For data collection, the demographic checklist and short form of fear of progression questionnaire was used. Logistic regression was used to determine predictive factors of FOCR. Result: Mean score of FOCR among participants was 44.8 and about 50% of them had high level of FOCR. The most important worries of participants were about their family and the future of their children and their lesser worries were about the physical symptoms and fear of physical damage because of cancer treatments. Also, women, breast cancer patient, and patients with lower level of education have more FOCR. Discussion: There is immediate need for supportive care program designed for Iranian cancer patients aimed at decreasing their FOCR. Especially, breast cancer patients and the patient with low educational level need more attention.

  17. Module-based outcome prediction using breast cancer compendia.

    Directory of Open Access Journals (Sweden)

    Martin H van Vliet

    Full Text Available BACKGROUND: The availability of large collections of microarray datasets (compendia, or knowledge about grouping of genes into pathways (gene sets, is typically not exploited when training predictors of disease outcome. These can be useful since a compendium increases the number of samples, while gene sets reduce the size of the feature space. This should be favorable from a machine learning perspective and result in more robust predictors. METHODOLOGY: We extracted modules of regulated genes from gene sets, and compendia. Through supervised analysis, we constructed predictors which employ modules predictive of breast cancer outcome. To validate these predictors we applied them to independent data, from the same institution (intra-dataset, and other institutions (inter-dataset. CONCLUSIONS: We show that modules derived from single breast cancer datasets achieve better performance on the validation data compared to gene-based predictors. We also show that there is a trend in compendium specificity and predictive performance: modules derived from a single breast cancer dataset, and a breast cancer specific compendium perform better compared to those derived from a human cancer compendium. Additionally, the module-based predictor provides a much richer insight into the underlying biology. Frequently selected gene sets are associated with processes such as cell cycle, E2F regulation, DNA damage response, proteasome and glycolysis. We analyzed two modules related to cell cycle, and the OCT1 transcription factor, respectively. On an individual basis, these modules provide a significant separation in survival subgroups on the training and independent validation data.

  18. Hadamard Kernel SVM with applications for breast cancer outcome predictions.

    Science.gov (United States)

    Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong

    2017-12-21

    Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

  19. Individual Prediction of Heart Failure Among Childhood Cancer Survivors

    Science.gov (United States)

    Chow, Eric J.; Chen, Yan; Kremer, Leontien C.; Breslow, Norman E.; Hudson, Melissa M.; Armstrong, Gregory T.; Border, William L.; Feijen, Elizabeth A.M.; Green, Daniel M.; Meacham, Lillian R.; Meeske, Kathleen A.; Mulrooney, Daniel A.; Ness, Kirsten K.; Oeffinger, Kevin C.; Sklar, Charles A.; Stovall, Marilyn; van der Pal, Helena J.; Weathers, Rita E.; Robison, Leslie L.; Yasui, Yutaka

    2015-01-01

    Purpose To create clinically useful models that incorporate readily available demographic and cancer treatment characteristics to predict individual risk of heart failure among 5-year survivors of childhood cancer. Patients and Methods Survivors in the Childhood Cancer Survivor Study (CCSS) free of significant cardiovascular disease 5 years after cancer diagnosis (n = 13,060) were observed through age 40 years for the development of heart failure (ie, requiring medications or heart transplantation or leading to death). Siblings (n = 4,023) established the baseline population risk. An additional 3,421 survivors from Emma Children's Hospital (Amsterdam, the Netherlands), the National Wilms Tumor Study, and the St Jude Lifetime Cohort Study were used to validate the CCSS prediction models. Results Heart failure occurred in 285 CCSS participants. Risk scores based on selected exposures (sex, age at cancer diagnosis, and anthracycline and chest radiotherapy doses) achieved an area under the curve of 0.74 and concordance statistic of 0.76 at or through age 40 years. Validation cohort estimates ranged from 0.68 to 0.82. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups, corresponding to cumulative incidences of heart failure at age 40 years of 0.5% (95% CI, 0.2% to 0.8%), 2.4% (95% CI, 1.8% to 3.0%), and 11.7% (95% CI, 8.8% to 14.5%), respectively. In comparison, siblings had a cumulative incidence of 0.3% (95% CI, 0.1% to 0.5%). Conclusion Using information available to clinicians soon after completion of childhood cancer therapy, individual risk for subsequent heart failure can be predicted with reasonable accuracy and discrimination. These validated models provide a framework on which to base future screening strategies and interventions. PMID:25287823

  20. Predicting opportunities to increase utilization of laparoscopy for colon cancer.

    Science.gov (United States)

    Keller, Deborah S; Parikh, Niraj; Senagore, Anthony J

    2017-04-01

    Despite proven safety and efficacy, rates of minimally invasive approaches for colon cancer remain low in the USA. Given the known benefits, investigating the root causes of underutilization and methods to increase laparoscopy is warranted. Our goal was to develop a predictive model of factors impacting use of laparoscopic surgery for colon cancer. The Premier Hospital Database was reviewed for elective colorectal resections for colon cancer (2009-2014). Patients were identified by ICD-9-CM diagnosis code and then stratified into open or laparoscopic approaches by ICD-9-CM procedure codes. An adjusted multivariate logistic regression model identified variables predictive of use of laparoscopy for colon cancer. A total of 24,245 patients were included-12,523 (52 %) laparoscopic and 11,722 (48 %) open. General surgeons performed the majority of all procedures (77.99 % open, 71.60 % laparoscopic). Overall use of laparoscopy increased from 48.94 to 52.03 % over the study period (p colon cancer laparoscopically. Colorectal surgeons were 32 % more likely to approach a case laparoscopically than general surgeons (OR 1.315, 95 % CI [1.222, 1.415], p characteristics that can be identified preoperatively to predict who will undergo surgery for colon cancer using laparoscopy. However, additional patients may be eligible for laparoscopy based on patient-level characteristics. These results have implications for regionalization and increasing teaching of MIS. Recognizing and addressing these variables with training and recruiting could increase use of minimally invasive approaches, with the associated clinical and financial benefits.

  1. Predictive Biomarkers of Radiation Sensitivity in Rectal Cancer

    Science.gov (United States)

    Tut, Thein Ga

    Colorectal cancer (CRC) is the third most common cancer in the world. Australia, New Zealand, Canada, the United States, and parts of Europe have the highest incidence rates of CRC. China, India, South America and parts of Africa have the lowest risk of CRC. CRC is the second most common cancer in both sexes in Australia. Even though the death rates from CRC involving the colon have diminished, those arising from the rectum have revealed no improvement. The greatest obstacle in attaining a complete surgical resection of large rectal cancers is the close anatomical relation to surrounding structures, as opposed to the free serosal surfaces enfolding the colon. To assist complete resection, pre-operative radiotherapy (DXT) can be applied, but the efficacy of ionising radiation (IR) is extremely variable between individual tumours. Reliable predictive marker/s that enable patient stratification in the application of this otherwise toxic therapy is still not available. Current therapeutic management of rectal cancer can be improved with the availability of better predictive and prognostic biomarkers. Proteins such as Plk1, gammaH2AX and MMR proteins (MSH2, MSH6, MLH1 and PMS2), involved in DNA damage response (DDR) pathway may be possible biomarkers for radiation response prediction and prognostication of rectal cancer. Serine/threonine protein kinase Plk1 is overexpressed in most of cancers including CRC. Plk1 functional activity is essential in the restoration of DNA damage following IR, which causes DNA double strand break (DSB). The earliest manifestation of this reparative process is histone H2AX phosphorylation at serine 139, leading to gammaH2AX. Colorectal normal mucosa showed the lowest level of gammaH2AX with gradually increasing levels in early adenoma and then in advanced malignant colorectal tissues, leading to the possibility that gammaH2AX may be a prospective biomarker in rectal cancer management. There are numerous publications regarding DNA mismatch

  2. Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

    Science.gov (United States)

    Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor-McCourt, Maureen

    2015-02-01

    Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have

  3. U.S. Army Armament Research, Development and Engineering Center Grain Evaluation Software to Numerically Predict Linear Burn Regression for Solid Propellant Grain Geometries

    Science.gov (United States)

    2017-10-01

    ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID PROPELLANT GRAIN GEOMETRIES Brian...distribution is unlimited. AD U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER Munitions Engineering Technology Center Picatinny...U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID

  4. 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.

  5. A software package for predicting design-flood hydrographs in small and ungauged basins

    OpenAIRE

    Rodolfo Piscopia; Andrea Petroselli; Salvatore Grimaldi

    2015-01-01

    In this study, software for estimating design hydrographs in small and ungauged basins is presented. The main aim is to propose a fast and user-friendly empirical tool that the practitioner can apply for hydrological studies characterised by a lack of observed data. The software implements a homonymous framework called event-based approach for small and ungauged basins (EBA4SUB) that was recently developed and tested by the authors to estimate the design peak discharge using the same input in...

  6. Korean risk assessment model for breast cancer risk prediction.

    Science.gov (United States)

    Park, Boyoung; Ma, Seung Hyun; Shin, Aesun; Chang, Myung-Chul; Choi, Ji-Yeob; Kim, Sungwan; Han, Wonshik; Noh, Dong-Young; Ahn, Sei-Hyun; Kang, Daehee; Yoo, Keun-Young; Park, Sue K

    2013-01-01

    We evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk. Using 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort. The major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (p = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls (pKorean women, especially urban women.

  7. MOlecular MAterials Property Prediction Package (MOMAP) 1.0: a software package for predicting the luminescent properties and mobility of organic functional materials

    Science.gov (United States)

    Niu, Yingli; Li, Wenqiang; Peng, Qian; Geng, Hua; Yi, Yuanping; Wang, Linjun; Nan, Guangjun; Wang, Dong; Shuai, Zhigang

    2018-04-01

    MOlecular MAterials Property Prediction Package (MOMAP) is a software toolkit for molecular materials property prediction. It focuses on luminescent properties and charge mobility properties. This article contains a brief descriptive introduction of key features, theoretical models and algorithms of the software, together with examples that illustrate the performance. First, we present the theoretical models and algorithms for molecular luminescent properties calculation, which includes the excited-state radiative/non-radiative decay rate constant and the optical spectra. Then, a multi-scale simulation approach and its algorithm for the molecular charge mobility are described. This approach is based on hopping model and combines with Kinetic Monte Carlo and molecular dynamics simulations, and it is especially applicable for describing a large category of organic semiconductors, whose inter-molecular electronic coupling is much smaller than intra-molecular charge reorganisation energy.

  8. Machine learning models in breast cancer survival prediction.

    Science.gov (United States)

    Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

    2016-01-01

    Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

  9. Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction.

    Science.gov (United States)

    Zhao, Di; Weng, Chunhua

    2011-10-01

    In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

  10. Breast cancer data analysis for survivability studies and prediction.

    Science.gov (United States)

    Shukla, Nagesh; Hagenbuchner, Markus; Win, Khin Than; Yang, Jack

    2018-03-01

    Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to assess/predict the survival prospects of patients. The main objectives of this paper is to develop a robust data analytical model which can assist in (i) a better understanding of breast cancer survivability in presence of missing data, (ii) providing better insights into factors associated with patient survivability, and (iii) establishing cohorts of patients that share similar properties. Unsupervised data mining methods viz. the self-organising map (SOM) and density-based spatial clustering of applications with noise (DBSCAN) is used to create patient cohort clusters. These clusters, with associated patterns, were used to train multilayer perceptron (MLP) model for improved patient survivability analysis. A large dataset available from SEER program is used in this study to identify patterns associated with the survivability of breast cancer patients. Information gain was computed for the purpose of variable selection. All of these methods are data-driven and require little (if any) input from users or experts. SOM consolidated patients into cohorts of patients with similar properties. From this, DBSCAN identified and extracted nine cohorts (clusters). It is found that patients in each of the nine clusters have different survivability time. The separation of patients into clusters improved the overall survival prediction accuracy based on MLP and revealed intricate conditions that affect the accuracy of a prediction. A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and

  11. A Review of Predictive Software for the Design of Community Microgrids

    Directory of Open Access Journals (Sweden)

    Mina Rahimian

    2018-01-01

    Full Text Available This paper discusses adding a spatial dimension to the design of community microgrid projects in the interest of expanding the existing discourse related to energy performance optimization measures. A multidimensional vision for designing community microgrids with higher energy performance is considered, leveraging urban form (superstructure to understand how it impacts the performance of the system’s distributed energy resources and loads (infrastructure. This vision engages the design sector in the technical conversation of developing community microgrids, leading to energy efficient designs of microgrid-connected communities well before their construction. A new generation of computational modeling and simulation tools that address this interaction are required. In order to position the research, this paper presents a survey of existing software packages, belonging to two distinct categories of modeling, simulation, and evaluation of community microgrids: the energy infrastructure modeling and the urban superstructure energy modeling. Results of this software survey identify a lack in software tools and simulation packages that simultaneously address the necessary interaction between the superstructure and infrastructure of community microgrids, given the importance of its study. Conclusions represent how a proposed experimental software prototype may fill an existing gap in current related software packages.

  12. Plateletpheresis efficiency and mathematical correction of software-derived platelet yield prediction: A linear regression and ROC modeling approach.

    Science.gov (United States)

    Jaime-Pérez, José Carlos; Jiménez-Castillo, Raúl Alberto; Vázquez-Hernández, Karina Elizabeth; Salazar-Riojas, Rosario; Méndez-Ramírez, Nereida; Gómez-Almaguer, David

    2017-10-01

    Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections. This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling. Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP. © 2016 Wiley Periodicals, Inc.

  13. Predicting Chernobyl childhood thyroid cancers from incoming data

    International Nuclear Information System (INIS)

    Thomas, P.J.

    1997-01-01

    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)

  14. Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

    Directory of Open Access Journals (Sweden)

    Liying Yang

    2016-01-01

    Full Text Available Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.

  15. A model for predicting lung cancer response to therapy

    International Nuclear Information System (INIS)

    Seibert, Rebecca M.; Ramsey, Chester R.; Hines, J. Wesley; Kupelian, Patrick A.; Langen, Katja M.; Meeks, Sanford L.; Scaperoth, Daniel D.

    2007-01-01

    Purpose: Volumetric computed tomography (CT) images acquired by image-guided radiation therapy (IGRT) systems can be used to measure tumor response over the course of treatment. Predictive adaptive therapy is a novel treatment technique that uses volumetric IGRT data to actively predict the future tumor response to therapy during the first few weeks of IGRT treatment. The goal of this study was to develop and test a model for predicting lung tumor response during IGRT treatment using serial megavoltage CT (MVCT). Methods and Materials: Tumor responses were measured for 20 lung cancer lesions in 17 patients that were imaged and treated with helical tomotherapy with doses ranging from 2.0 to 2.5 Gy per fraction. Five patients were treated with concurrent chemotherapy, and 1 patient was treated with neoadjuvant chemotherapy. Tumor response to treatment was retrospectively measured by contouring 480 serial MVCT images acquired before treatment. A nonparametric, memory-based locally weight regression (LWR) model was developed for predicting tumor response using the retrospective tumor response data. This model predicts future tumor volumes and the associated confidence intervals based on limited observations during the first 2 weeks of treatment. The predictive accuracy of the model was tested using a leave-one-out cross-validation technique with the measured tumor responses. Results: The predictive algorithm was used to compare predicted verse-measured tumor volume response for all 20 lesions. The average error for the predictions of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest model uncertainty occurred near the middle of the course of treatment, in which the tumor response relationships were more complex, the model has less information, and the predictors were more varied. The optimal days for measuring the tumor response on the MVCT images were on elapsed Days 1, 2, 5, 9, 11, 12, 17, and 18 during

  16. Convolutional neural networks for prostate cancer recurrence prediction

    Science.gov (United States)

    Kumar, Neeraj; Verma, Ruchika; Arora, Ashish; Kumar, Abhay; Gupta, Sanchit; Sethi, Amit; Gann, Peter H.

    2017-03-01

    Accurate prediction of the treatment outcome is important for cancer treatment planning. We present an approach to predict prostate cancer (PCa) recurrence after radical prostatectomy using tissue images. We used a cohort whose case vs. control (recurrent vs. non-recurrent) status had been determined using post-treatment follow up. Further, to aid the development of novel biomarkers of PCa recurrence, cases and controls were paired based on matching of other predictive clinical variables such as Gleason grade, stage, age, and race. For this cohort, tissue resection microarray with up to four cores per patient was available. The proposed approach is based on deep learning, and its novelty lies in the use of two separate convolutional neural networks (CNNs) - one to detect individual nuclei even in the crowded areas, and the other to classify them. To detect nuclear centers in an image, the first CNN predicts distance transform of the underlying (but unknown) multi-nuclear map from the input HE image. The second CNN classifies the patches centered at nuclear centers into those belonging to cases or controls. Voting across patches extracted from image(s) of a patient yields the probability of recurrence for the patient. The proposed approach gave 0.81 AUC for a sample of 30 recurrent cases and 30 non-recurrent controls, after being trained on an independent set of 80 case-controls pairs. If validated further, such an approach might help in choosing between a combination of treatment options such as active surveillance, radical prostatectomy, radiation, and hormone therapy. It can also generalize to the prediction of treatment outcomes in other cancers.

  17. Updating risk prediction tools: a case study in prostate cancer.

    Science.gov (United States)

    Ankerst, Donna P; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J; Feng, Ziding; Sanda, Martin G; Partin, Alan W; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M

    2012-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

    Science.gov (United States)

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

    2015-01-01

    Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. Statistical dependence with PC and BPH was found for prostate volume (P-value BPH prediction. PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced.

  19. Pretreatment tables predicting pathologic stage of locally advanced prostate cancer.

    Science.gov (United States)

    Joniau, Steven; Spahn, Martin; Briganti, Alberto; Gandaglia, Giorgio; Tombal, Bertrand; Tosco, Lorenzo; Marchioro, Giansilvio; Hsu, Chao-Yu; Walz, Jochen; Kneitz, Burkhard; Bader, Pia; Frohneberg, Detlef; Tizzani, Alessandro; Graefen, Markus; van Cangh, Paul; Karnes, R Jeffrey; Montorsi, Francesco; van Poppel, Hein; Gontero, Paolo

    2015-02-01

    Pretreatment tables for the prediction of pathologic stage have been published and validated for localized prostate cancer (PCa). No such tables are available for locally advanced (cT3a) PCa. To construct tables predicting pathologic outcome after radical prostatectomy (RP) for patients with cT3a PCa with the aim to help guide treatment decisions in clinical practice. This was a multicenter retrospective cohort study including 759 consecutive patients with cT3a PCa treated with RP between 1987 and 2010. Retropubic RP and pelvic lymphadenectomy. Patients were divided into pretreatment prostate-specific antigen (PSA) and biopsy Gleason score (GS) subgroups. These parameters were used to construct tables predicting pathologic outcome and the presence of positive lymph nodes (LNs) after RP for cT3a PCa using ordinal logistic regression. In the model predicting pathologic outcome, the main effects of biopsy GS and pretreatment PSA were significant. A higher GS and/or higher PSA level was associated with a more unfavorable pathologic outcome. The validation procedure, using a repeated split-sample method, showed good predictive ability. Regression analysis also showed an increasing probability of positive LNs with increasing PSA levels and/or higher GS. Limitations of the study are the retrospective design and the long study period. These novel tables predict pathologic stage after RP for patients with cT3a PCa based on pretreatment PSA level and biopsy GS. They can be used to guide decision making in men with locally advanced PCa. Our study might provide physicians with a useful tool to predict pathologic stage in locally advanced prostate cancer that might help select patients who may need multimodal treatment. Copyright © 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.

  20. Development of decision tree software and protein profiling using surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS) in papillary thyroid cancer

    International Nuclear Information System (INIS)

    Yoon, Joon Kee; An, Young Sil; Park, Bok Nam; Yoon, Seok Nam; Lee, Jun

    2007-01-01

    The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups (ρ < 0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer

  1. Development of decision tree software and protein profiling using surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS) in papillary thyroid cancer

    Energy Technology Data Exchange (ETDEWEB)

    Yoon, Joon Kee; An, Young Sil; Park, Bok Nam; Yoon, Seok Nam [Ajou University School of Medicine, Suwon (Korea, Republic of); Lee, Jun [Konkuk University, Seoul (Korea, Republic of)

    2007-08-15

    The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups ({rho} < 0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer.

  2. A software package for predicting design-flood hydrographs in small and ungauged basins

    Directory of Open Access Journals (Sweden)

    Rodolfo Piscopia

    2015-06-01

    Full Text Available In this study, software for estimating design hydrographs in small and ungauged basins is presented. The main aim is to propose a fast and user-friendly empirical tool that the practitioner can apply for hydrological studies characterised by a lack of observed data. The software implements a homonymous framework called event-based approach for small and ungauged basins (EBA4SUB that was recently developed and tested by the authors to estimate the design peak discharge using the same input information necessary to apply the rational formula. EBA4SUB is a classical hydrological event-based model in which each step (design hyetograph, net rainfall estimation, and rainfall-runoff transformation is appropriately adapted for empirical applications without calibration. As a case study, the software is applied in a small watershed while varying the hyetograph shape, rainfall peak position, and return time. The results provide an overview of the software and confirm the secondary role of the design rainfall peak position.

  3. Development of an irrigation scheduling software based on model predicted crop water stress

    Science.gov (United States)

    Modern irrigation scheduling methods are generally based on sensor-monitored soil moisture regimes rather than crop water stress which is difficult to measure in real-time, but can be computed using agricultural system models. In this study, an irrigation scheduling software based on RZWQM2 model pr...

  4. A prospective development study of software-guided radio-frequency ablation of primary and secondary liver tumors: Clinical intervention modelling, planning and proof for ablation cancer treatment (ClinicIMPPACT).

    Science.gov (United States)

    Reinhardt, Martin; Brandmaier, Philipp; Seider, Daniel; Kolesnik, Marina; Jenniskens, Sjoerd; Sequeiros, Roberto Blanco; Eibisberger, Martin; Voglreiter, Philip; Flanagan, Ronan; Mariappan, Panchatcharam; Busse, Harald; Moche, Michael

    2017-12-01

    Radio-frequency ablation (RFA) is a promising minimal-invasive treatment option for early liver cancer, however monitoring or predicting the size of the resulting tissue necrosis during the RFA-procedure is a challenging task, potentially resulting in a significant rate of under- or over treatments. Currently there is no reliable lesion size prediction method commercially available. ClinicIMPPACT is designed as multicenter-, prospective-, non-randomized clinical trial to evaluate the accuracy and efficiency of innovative planning and simulation software. 60 patients with early liver cancer will be included at four European clinical institutions and treated with the same RFA system. The preinterventional imaging datasets will be used for computational planning of the RFA treatment. All ablations will be simulated simultaneously to the actual RFA procedure, using the software environment developed in this project. The primary outcome measure is the comparison of the simulated ablation zones with the true lesions shown in follow-up imaging after one month, to assess accuracy of the lesion prediction. This unique multicenter clinical trial aims at the clinical integration of a dedicated software solution to accurately predict lesion size and shape after radiofrequency ablation of liver tumors. Accelerated and optimized workflow integration, and real-time intraoperative image processing, as well as inclusion of patient specific information, e.g. organ perfusion and registration of the real RFA needle position might make the introduced software a powerful tool for interventional radiologists to optimize patient outcomes.

  5. 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

  6. Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers.

    Science.gov (United States)

    Choi, Jonghwan; Park, Sanghyun; Yoon, Youngmi; Ahn, Jaegyoon

    2017-11-15

    Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. https://github.com/mathcom/CPR. jgahn@inu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  7. Assessment of the Unstructured Grid Software TetrUSS for Drag Prediction of the DLR-F4 Configuration

    Science.gov (United States)

    Pirzadeh, Shahyar Z.; Frink, Neal T.

    2002-01-01

    An application of the NASA unstructured grid software system TetrUSS is presented for the prediction of aerodynamic drag on a transport configuration. The paper briefly describes the underlying methodology and summarizes the results obtained on the DLR-F4 transport configuration recently presented in the first AIAA computational fluid dynamics (CFD) Drag Prediction Workshop. TetrUSS is a suite of loosely coupled unstructured grid CFD codes developed at the NASA Langley Research Center. The meshing approach is based on the advancing-front and the advancing-layers procedures. The flow solver employs a cell-centered, finite volume scheme for solving the Reynolds Averaged Navier-Stokes equations on tetrahedral grids. For the present computations, flow in the viscous sublayer has been modeled with an analytical wall function. The emphasis of the paper is placed on the practicality of the methodology for accurately predicting aerodynamic drag data.

  8. Western Validation of a Novel Gastric Cancer Prognosis Prediction Model in US Gastric Cancer Patients.

    Science.gov (United States)

    Woo, Yanghee; Goldner, Bryan; Son, Taeil; Song, Kijun; Noh, Sung Hoon; Fong, Yuman; Hyung, Woo Jin

    2018-03-01

    A novel prediction model for accurate determination of 5-year overall survival of gastric cancer patients was developed by an international collaborative group (G6+). This prediction model was created using a single institution's database of 11,851 Korean patients and included readily available and clinically relevant factors. Already validated using external East Asian cohorts, its applicability in the American population was yet to be determined. Using the Surveillance, Epidemiology, and End Results (SEER) dataset, 2014 release, all patients diagnosed with gastric adenocarcinoma who underwent surgical resection between 2002 and 2012, were selected. Characteristics for analysis included: age, sex, depth of tumor invasion, number of positive lymph nodes, total lymph nodes retrieved, presence of distant metastasis, extent of resection, and histology. Concordance index (C-statistic) was assessed using the novel prediction model and compared with the prognostic index, the seventh edition of the TNM staging system. Of the 26,019 gastric cancer patients identified from the SEER database, 15,483 had complete datasets. Validation of the novel prediction tool revealed a C-statistic of 0.762 (95% CI 0.754 to 0.769) compared with the seventh TNM staging model, C-statistic 0.683 (95% CI 0.677 to 0.689), (p prediction model for gastric cancer in the American patient population. Its superior prediction of the 5-year survival of gastric cancer patients in a large Western cohort strongly supports its global applicability. Importantly, this model allows for accurate prognosis for an increasing number of gastric cancer patients worldwide, including those who received inadequate lymphadenectomy or underwent a noncurative resection. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

  9. Lung cancer mortality in European women: trends and predictions.

    Science.gov (United States)

    Bosetti, Cristina; Malvezzi, Matteo; Rosso, Tiziana; Bertuccio, Paola; Gallus, Silvano; Chatenoud, Liliane; Levi, Fabio; Negri, Eva; La Vecchia, Carlo

    2012-12-01

    Female lung cancer mortality increased by 50% between the mid 1960s and the early 2000s in the European Union (EU). To monitor the current lung cancer epidemic in European women, we analyzed mortality trends in 33 European countries between 1970 and 2009 and estimated rates for the year 2015 using data from the World Health Organization. Female lung cancer mortality has been increasing up to recent calendar years in most European countries, with the exceptions of Belarus, Russia, and Ukraine, with relatively low rates, and the UK, Iceland and Ireland, where high rates were reached in mid/late 1990s to leveled off thereafter. In the EU, female lung cancer mortality rates rose over the last decade from 11.3 to 12.7/100,000 (+2.3% per year) at all ages and from 18.6 to 21.5/100,000 (+3.0% per year) in middle-age. A further increase is predicted, to reach 14/100,000 women in 2015. Lung cancer mortality trends have been more favorable over the last decade in young women (20-44 years), particularly in the UK and other former high-risk countries from northern and central/eastern Europe, but also in France, Italy, and Spain where mortality in young women has been increasing up to the early 2000s. In the EU as a whole, mortality at age 20-44 years decreased from 1.6 to 1.4/100,000 (-2.2% per year). Although the female lung cancer epidemic in Europe is still expanding, the epidemic may be controlled through the implementation of effective anti-tobacco measures, and it will probably never reach the top US rates. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  10. Reproducibility of Lobar Perfusion and Ventilation Quantification Using SPECT/CT Segmentation Software in Lung Cancer Patients.

    Science.gov (United States)

    Provost, Karine; Leblond, Antoine; Gauthier-Lemire, Annie; Filion, Édith; Bahig, Houda; Lord, Martin

    2017-09-01

    Planar perfusion scintigraphy with 99m Tc-labeled macroaggregated albumin is often used for pretherapy quantification of regional lung perfusion in lung cancer patients, particularly those with poor respiratory function. However, subdividing lung parenchyma into rectangular regions of interest, as done on planar images, is a poor reflection of true lobar anatomy. New tridimensional methods using SPECT and SPECT/CT have been introduced, including semiautomatic lung segmentation software. The present study evaluated inter- and intraobserver agreement on quantification using SPECT/CT software and compared the results for regional lung contribution obtained with SPECT/CT and planar scintigraphy. Methods: Thirty lung cancer patients underwent ventilation-perfusion scintigraphy with 99m Tc-macroaggregated albumin and 99m Tc-Technegas. The regional lung contribution to perfusion and ventilation was measured on both planar scintigraphy and SPECT/CT using semiautomatic lung segmentation software by 2 observers. Interobserver and intraobserver agreement for the SPECT/CT software was assessed using the intraclass correlation coefficient, Bland-Altman plots, and absolute differences in measurements. Measurements from planar and tridimensional methods were compared using the paired-sample t test and mean absolute differences. Results: Intraclass correlation coefficients were in the excellent range (above 0.9) for both interobserver and intraobserver agreement using the SPECT/CT software. Bland-Altman analyses showed very narrow limits of agreement. Absolute differences were below 2.0% in 96% of both interobserver and intraobserver measurements. There was a statistically significant difference between planar and SPECT/CT methods ( P software is highly reproducible. This tridimensional method yields statistically significant differences in measurements for right lung lobes when compared with planar scintigraphy. We recommend that SPECT/CT-based quantification be used for all lung

  11. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

    Science.gov (United States)

    Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

    2017-05-22

    PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age

  12. Surgical planning of total hip arthroplasty: accuracy of computer-assisted EndoMap software in predicting component size

    International Nuclear Information System (INIS)

    Davila, Jesse A.; Kransdorf, Mark J.; Duffy, Gavan P.

    2006-01-01

    The purpose of our study was to assess the accuracy of a computer-assisted templating in the surgical planning of patients undergoing total hip arthroplasty utilizing EndoMap software (Siemans AG, Medical Solutions, Erlangen, Germany). Endomap Software is an electronic program that uses DICOM images to analyze standard anteroposterior radiographs for determination of optimal prosthesis component size. We retrospectively reviewed the preoperative radiographs of 36 patients undergoing uncomplicated primary total hip arthroplasty, utilizing EndoMap software, Version VA20. DICOM anteroposterior radiographs were analyzed using standard manufacturer supplied electronic templates to determine acetabular and femoral component sizes. No additional clinical information was reviewed. Acetabular and femoral component sizes were assessed by an orthopedic surgeon and two radiologists. Mean and estimated component size was compared with component size as documented in operative reports. The mean estimated acetabular component size was 53 mm (range 48-60 mm), 1 mm larger than the mean implanted size of 52 mm (range 48-62 mm). Thirty-one of 36 acetabular component sizes (86%) were accurate within one size. The mean calculated femoral component size was 4 (range 2-7), 1 size smaller than the actual mean component size of 5 (range 2-9). Twenty-six of 36 femoral component sizes (72%) were accurate within one size, and accurate within two sizes in all but four cases (94%). EndoMap Software predicted femoral component size well, with 72% within one component size of that used, and 94% within two sizes. Acetabular component size was predicted slightly better with 86% within one component size and 94% within two component sizes. (orig.)

  13. Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging.

    Directory of Open Access Journals (Sweden)

    Matthias C Roethke

    Full Text Available To evaluate the diagnostic performance of an automated analysis tool for the assessment of prostate cancer based on multiparametric magnetic resonance imaging (mpMRI of the prostate.A fully automated analysis tool was used for a retrospective analysis of mpMRI sets (T2-weighted, T1-weighted dynamic contrast-enhanced, and diffusion-weighted sequences. The software provided a malignancy prediction value for each image pixel, defined as Malignancy Attention Index (MAI that can be depicted as a colour map overlay on the original images. The malignancy maps were compared to histopathology derived from a combination of MRI-targeted and systematic transperineal MRI/TRUS-fusion biopsies.In total, mpMRI data of 45 patients were evaluated. With a sensitivity of 85.7% (with 95% CI of 65.4-95.0, a specificity of 87.5% (with 95% CI of 69.0-95.7 and a diagnostic accuracy of 86.7% (with 95% CI of 73.8-93.8 for detection of prostate cancer, the automated analysis results corresponded well with the reported diagnostic accuracies by human readers based on the PI-RADS system in the current literature.The study revealed comparable diagnostic accuracies for the detection of prostate cancer of a user-independent MAI-based automated analysis tool and PI-RADS-scoring-based human reader analysis of mpMRI. Thus, the analysis tool could serve as a detection support system for less experienced readers. The results of the study also suggest the potential of MAI-based analysis for advanced lesion assessments, such as cancer extent and staging prediction.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

    INTRODUCTION: Although prostate cancer (PCa) incidence is lower in HIV+ men than in HIV- men, the usefulness of prostate-specific antigen (PSA) screening in this population is not well defined and may have higher false negative rates than in HIV- men. We aimed to describe the kinetics and predict......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 predictive value of PSA in HIV+ men. METHODS: Men with PCa (n=21) and up to two matched controls (n=40) with prospectively stored plasma samples before PCa (or matched date in controls) were selected. Cases and controls were matched on date of first and last sample, age, region of residence and CD4 count...... at first sample date. Total PSA (tPSA), free PSA (fPSA), testosterone and sex hormone binding globulin (SHBG) were measured. Conditional logistic regression models investigated associations between markers and PCa. Sensitivity and specificity of using tPSA >4 µg/L to predict PCa was calculated. Mixed...

  15. 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.

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

    Science.gov (United States)

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

    2016-03-01

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

  17. A comprehensive software suite for protein family construction and functional site prediction.

    Directory of Open Access Journals (Sweden)

    David Renfrew Haft

    Full Text Available In functionally diverse protein families, conservation in short signature regions may outperform full-length sequence comparisons for identifying proteins that belong to a subgroup within which one specific aspect of their function is conserved. The SIMBAL workflow (Sites Inferred by Metabolic Background Assertion Labeling is a data-mining procedure for finding such signature regions. It begins by using clues from genomic context, such as co-occurrence or conserved gene neighborhoods, to build a useful training set from a large number of uncharacterized but mutually homologous proteins. When training set construction is successful, the YES partition is enriched in proteins that share function with the user's query sequence, while the NO partition is depleted. A selected query sequence is then mined for short signature regions whose closest matches overwhelmingly favor proteins from the YES partition. High-scoring signature regions typically contain key residues critical to functional specificity, so proteins with the highest sequence similarity across these regions tend to share the same function. The SIMBAL algorithm was described previously, but significant manual effort, expertise, and a supporting software infrastructure were required to prepare the requisite training sets. Here, we describe a new, distributable software suite that speeds up and simplifies the process for using SIMBAL, most notably by providing tools that automate training set construction. These tools have broad utility for comparative genomics, allowing for flexible collection of proteins or protein domains based on genomic context as well as homology, a capability that can greatly assist in protein family construction. Armed with this new software suite, SIMBAL can serve as a fast and powerful in silico alternative to direct experimentation for characterizing proteins and their functional interactions.

  18. Does Small Prostate Predict High Grade Prostate Cancer?

    International Nuclear Information System (INIS)

    Caliskan, S.; Kaba, S.; Koca, O.; Ozturk, M. I.

    2017-01-01

    Objective: The current study is aimed to assess the patients who underwent radical prostatectomy for prostate cancer and investigate the association between prostate size and adverse outcomes at final pathology. Study Design: Comparative, descriptive study. Place and Duration of Study: Haydarpasa Numune Training and Research Hospital, Turkey, from January 2008 to January 2016. Methodology: The patients treated with open radical prostatectomy for prostate cancer were reviewed. Patient characteristics including prostate specific antigen (PSA), free PSA levels, age, biopsy, and radical prostatectomy results were recorded. The patients whose data were complete or prostate weight was equal to or less than 80 gm, were included in the study. Patients with < 40 gm prostate weight was in group 1 and the patients in group 2 had a prostate weight from 40 to 80 gm. High grade prostate cancer was defined to have a Gleason score between 7 or higher at biopsy and final pathology. Pathology and biopsy results were compared within groups. MedCalc Statistical Software demo version was used for statistical analyses. Results: There were 162 patients in this study. Of these, 71 (43.82 percent) patients were in group 1 and 91 (56.17 percent) patients were in group 2. The age ranged from 49 to 76 years. Mean value of 62.70 +-6.82 and 65.82 +- 5.66 years in group 1 and 2, respectively. Fifty (70.42 percent) and 68 patients (74.74 percent) had a Gleason score of 6 in group 1 and 2, respectively. Organconfined disease was reported in 53 patients (74.64 percent) in group 1 and in 78 patients (85.71 percent) in group 2. Gleason score concordance between biopsy and prostatectomy was reported in 61 patients (67.03 percent) and downgrading was detected in 4 patients (4.4 percent) in group 2. The median tumor volume of the patients was 4.47 cm/sup 3/ in group 1 and 6 cm/sup 3/ in group 2 (p=0.502). High grade prostate cancer was reported in 52.11 percent and 45.05 percent of the patients in

  19. 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.

  20. A METHOD OF PREDICTING BREAST CANCER USING QUESTIONNAIRES

    Directory of Open Access Journals (Sweden)

    V. N. Malashenko

    2017-01-01

    Full Text Available Purpose. Simplify and increase the accuracy of the questionnaire method of predicting breast cancer (BC for subsequent computer processing and Automated dispensary at risk without the doctor.Materials and methods. The work was based on statistical data obtained by surveying 305 women. The questionnaire included 63 items: 17 open-ended questions, 46 — with a choice of response. It was established multifactor model, the development of which, in addition to the survey data were used materials from the medical histories of patients and respondents data immuno-histochemical studies. Data analysis was performed using Statistica 10.0 and MedCalc 12.7.0 programs.Results. The ROC analysis was performas and the questionnaire data revealed 8 significant predictors of breast cancer. On their basis we created the formula for calculating the prognostic factor of risk of development of breast cancer with a sensitivity 83,12% and a specificity of 91,43%.Conclusions. The completed developments allow to create a computer program for automated processing of profiles on the formation of groups at risk of breast cancer and clinical supervision. The introduction of a screening questionnaire over the Internet with subsequent computer processing of the results, without the direct involvement of doctors, will increase the coverage of the female population of the Russian Federation activities related to the prevention of breast cancer. It can free up time for physicians to receive primary patients, as well as improve oncological vigilance of the female population of the Russian Federation.

  1. Using HPV prevalence to predict cervical cancer incidence.

    Science.gov (United States)

    Sharma, Monisha; Bruni, Laia; Diaz, Mireia; Castellsagué, Xavier; de Sanjosé, Silvia; Bosch, F Xavier; Kim, Jane J

    2013-04-15

    Knowledge of a country's cervical cancer (CC) burden is critical to informing decisions about resource allocation to combat the disease; however, many countries lack cancer registries to provide such data. We developed a prognostic model to estimate CC incidence rates in countries without cancer registries, leveraging information on human papilloma virus (HPV) prevalence, screening, and other country-level factors. We used multivariate linear regression models to identify predictors of CC incidence in 40 countries. We extracted age-specific HPV prevalence (10-year age groups) by country from a meta-analysis in women with normal cytology (N = 40) and matched to most recent CC incidence rates from Cancer Incidence in Five Continents when available (N = 36), or Globocan 2008 (N = 4). We evaluated country-level behavioral, economic, and public health indicators. CC incidence was significantly associated with age-specific HPV prevalence in women aged 35-64 (adjusted R-squared 0.41) ("base model"). Adding geographic region to the base model increased the adjusted R-squared to 0.77, but the further addition of screening was not statistically significant. Similarly, country-level macro-indicators did not improve predictive validity. Age-specific HPV prevalence at older ages was found to be a better predictor of CC incidence than prevalence in women under 35. However, HPV prevalence could not explain the entire CC burden as many factors modify women's risk of progression to cancer. Geographic region seemed to serve as a proxy for these country-level indicators. Our analysis supports the assertion that conducting a population-based HPV survey targeting women over age 35 can be valuable in approximating the CC risk in a given country. Copyright © 2012 UICC.

  2. Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer

    Science.gov (United States)

    2011-01-01

    Background The immunohistochemical detection of estrogen (ER) and progesterone (PR) receptors in breast cancer is routinely used for prognostic and predictive testing. Whole slide digitalization supported by dedicated software tools allows quantization of the image objects (e.g. cell membrane, nuclei) and an unbiased analysis of immunostaining results. Validation studies of image analysis applications for the detection of ER and PR in breast cancer specimens provided strong concordance between the pathologist's manual assessment of slides and scoring performed using different software applications. Methods The effectiveness of two connected semi-automated image analysis software (NuclearQuant v. 1.13 application for Pannoramic™ Viewer v. 1.14) for determination of ER and PR status in formalin-fixed paraffin embedded breast cancer specimens immunostained with the automated Leica Bond Max system was studied. First the detection algorithm was calibrated to the scores provided an independent assessors (pathologist), using selected areas from 38 small digital slides (created from 16 cases) containing a mean number of 195 cells. Each cell was manually marked and scored according to the Allred-system combining frequency and intensity scores. The performance of the calibrated algorithm was tested on 16 cases (14 invasive ductal carcinoma, 2 invasive lobular carcinoma) against the pathologist's manual scoring of digital slides. Results The detection was calibrated to 87 percent object detection agreement and almost perfect Total Score agreement (Cohen's kappa 0.859, quadratic weighted kappa 0.986) from slight or moderate agreement at the start of the study, using the un-calibrated algorithm. The performance of the application was tested against the pathologist's manual scoring of digital slides on 53 regions of interest of 16 ER and PR slides covering all positivity ranges, and the quadratic weighted kappa provided almost perfect agreement (κ = 0.981) among the two

  3. Software Infrastructure to Support DSAP (Dynamic Situational Awareness and Prediction) Capabilities

    National Research Council Canada - National Science Library

    McGraw, Robert

    2006-01-01

    Today's C4I systems will be required to support faster-than-real-time predictive simulation that can determine possible outcomes by re-calibrating with real-time sensor data or extracted knowledge in real-time...

  4. 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...

  5. Using software to predict occupational hearing loss in the mining industry.

    Science.gov (United States)

    Azman, A S; Li, M; Thompson, J K

    2016-01-01

    Powerful mining systems typically generate high-level noise that can damage the hearing ability of miners. Engineering noise controls are the most desirable and effective control for overexposure to noise. However, the effects of these noise controls on the actual hearing status of workers are not easily measured. A tool that can provide guidance in assigning workers to jobs based on the noise levels to which they will be exposed is highly desirable. Therefore, the Pittsburgh Mining Research Division (PMRD) of the U.S. National Institute for Occupational Safety and Health (NIOSH) developed a tool to estimate in a systematic way the hearing loss due to occupational noise exposure and to evaluate the effectiveness of developed engineering controls. This computer program is based on the ISO 1999 standard and can be used to estimate the loss of hearing ability caused by occupational noise exposures. In this paper, the functionalities of this software are discussed and several case studies related to mining machinery are presented to demonstrate the functionalities of this software.

  6. Prediction of ice accretion and anti-icing heating power on wind turbine blades using standard commercial software

    International Nuclear Information System (INIS)

    Villalpando, Fernando; Reggio, Marcelo; Ilinca, Adrian

    2016-01-01

    An approach to numerically simulate ice accretion on 2D sections of a wind turbine blade is presented. The method uses standard commercial ANSYS-Fluent and Matlab tools. The Euler-Euler formulation is used to calculate the water impingement on the airfoil, and a UDF (Used Defined Function) has been devised to turn the airfoil's solid wall into a permeable boundary. Mayer's thermodynamic model is implemented in Matlab for computing ice thickness and for updating the airfoil contour. A journal file is executed to systematize the procedure: meshing, droplet trajectory calculation, thermodynamic model application for computing ice accretion, and the updating of airfoil contours. The proposed ice prediction strategy has been validated using iced airfoil contours obtained experimentally in the AMIL refrigerated wind tunnel (Anti-icing Materials International Laboratory). Finally, a numerical prediction method has been generated for anti-icing assessment, and its results compared with data obtained in this laboratory. - Highlights: • A methodology for ice accretion prediction using commercial software is proposed. • Euler model gives better prediction of airfoil water collection with detached flow. • A source term is used to change from a solid wall to a permeable wall in Fluent. • Energy needed for ice-accretion mitigation system is predicted.

  7. Evaluation of three state-of-the-art metabolite prediction software packages (Meteor, MetaSite, and StarDrop) through independent and synergistic use.

    Science.gov (United States)

    T'jollyn, H; Boussery, K; Mortishire-Smith, R J; Coe, K; De Boeck, B; Van Bocxlaer, J F; Mannens, G

    2011-11-01

    The aim of this study was to evaluate three different metabolite prediction software packages (Meteor, MetaSite, and StarDrop) with respect to their ability to predict loci of metabolism and suggest relative proportions of metabolites. A chemically diverse test set of 22 compounds, for which in vivo human mass balance studies and metabolic schemes were available, was used as basis for the evaluation. Each software package was provided with structures of the parent compounds, and predicted metabolites were compared with experimentally determined human metabolites. The evaluation consisted of two parts. First, different settings within each software package were investigated and the software was evaluated using those settings determined to give the best prediction. Second, the three different packages were combined using the optimized settings to see whether a synergistic effect concerning the overall metabolism prediction could be established. The performance of the software was scored for both sensitivity and precision, taking into account the capabilities/limitations of the particular software. Varying results were obtained for the individual packages. Meteor showed a general tendency toward overprediction, and this led to a relatively low precision (∼35%) but high sensitivity (∼70%). MetaSite and StarDrop both exhibited a sensitivity and precision of ∼50%. By combining predictions obtained with the different packages, we found that increased precision can be obtained. We conclude that the state-of-the-art individual metabolite prediction software has many advantageous features but needs refinement to obtain acceptable prediction profiles. Synergistic use of different software packages could prove useful.

  8. [Prognostic and predictive molecular markers for urologic cancers].

    Science.gov (United States)

    Hartmann, A; Schlomm, T; Bertz, S; Heinzelmann, J; Hölters, S; Simon, R; Stoehr, R; Junker, K

    2014-04-01

    Molecular prognostic factors and genetic alterations as predictive markers for cancer-specific targeted therapies are used today in the clinic for many malignancies. In recent years, many molecular markers for urogenital cancers have also been identified. However, these markers are not clinically used yet. In prostate cancer, novel next-generation sequencing methods revealed a detailed picture of the molecular changes. There is growing evidence that a combination of classical histopathological and validated molecular markers could lead to a more precise estimation of prognosis, thus, resulting in an increasing number of patients with active surveillance as a possible treatment option. In patients with urothelial carcinoma, histopathological factors but also the proliferation of the tumor, mutations in oncogenes leading to an increasing proliferation rate and changes in genes responsible for invasion and metastasis are important. In addition, gene expression profiles which could distinguish aggressive tumors with high risk of metastasis from nonmetastasizing tumors have been recently identified. In the future, this could potentially allow better selection of patients needing systemic perioperative treatment. In renal cell carcinoma, many molecular markers that are associated with metastasis and survival have been identified. Some of these markers were also validated as independent prognostic markers. Selection of patients with primarily organ-confined tumors and increased risk of metastasis for adjuvant systemic therapy could be clinically relevant in the future.

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

    Directory of Open Access Journals (Sweden)

    Sauter Edward R

    2012-01-01

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

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

    International Nuclear Information System (INIS)

    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 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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-02-25

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

  12. Predicting death from kala-azar: construction, development, and validation of a score set and accompanying software.

    Science.gov (United States)

    Costa, Dorcas Lamounier; Rocha, Regina Lunardi; Chaves, Eldo de Brito Ferreira; Batista, Vivianny Gonçalves de Vasconcelos; Costa, Henrique Lamounier; Costa, Carlos Henrique Nery

    2016-01-01

    Early identification of patients at higher risk of progressing to severe disease and death is crucial for implementing therapeutic and preventive measures; this could reduce the morbidity and mortality from kala-azar. We describe a score set composed of four scales in addition to software for quick assessment of the probability of death from kala-azar at the point of care. Data from 883 patients diagnosed between September 2005 and August 2008 were used to derive the score set, and data from 1,031 patients diagnosed between September 2008 and November 2013 were used to validate the models. Stepwise logistic regression analyses were used to derive the optimal multivariate prediction models. Model performance was assessed by its discriminatory accuracy. A computational specialist system (Kala-Cal(r)) was developed to speed up the calculation of the probability of death based on clinical scores. The clinical prediction score showed high discrimination (area under the curve [AUC] 0.90) for distinguishing death from survival for children ≤2 years old. Performance improved after adding laboratory variables (AUC 0.93). The clinical score showed equivalent discrimination (AUC 0.89) for older children and adults, which also improved after including laboratory data (AUC 0.92). The score set also showed a high, although lower, discrimination when applied to the validation cohort. This score set and Kala-Cal(r) software may help identify individuals with the greatest probability of death. The associated software may speed up the calculation of the probability of death based on clinical scores and assist physicians in decision-making.

  13. Methylation of cancer-stem-cell-associated Wnt target genes predicts poor prognosis in colorectal cancer patients

    NARCIS (Netherlands)

    de Sousa E Melo, Felipe; Colak, Selcuk; Buikhuisen, Joyce; Koster, Jan; Cameron, Kate; de Jong, Joan H.; Tuynman, Jurriaan B.; Prasetyanti, Pramudita R.; Fessler, Evelyn; van den Bergh, Saskia P.; Rodermond, Hans; Dekker, Evelien; van der Loos, Chris M.; Pals, Steven T.; van de Vijver, Marc J.; Versteeg, Rogier; Richel, Dick J.; Vermeulen, Louis; Medema, Jan Paul

    2011-01-01

    Gene signatures derived from cancer stem cells (CSCs) predict tumor recurrence for many forms of cancer. Here, we derived a gene signature for colorectal CSCs defined by high Wnt signaling activity, which in agreement with previous observations predicts poor prognosis. Surprisingly, however, we

  14. Predicting Brain Metastasis in Breast Cancer Patients: Stage Versus Biology.

    Science.gov (United States)

    Azim, Hamdy A; Abdel-Malek, Raafat; Kassem, Loay

    2018-04-01

    Brain metastasis (BM) is a life-threatening event in breast cancer patients. Identifying patients at a high risk for BM can help to adopt screening programs and test preventive interventions. We tried to identify the incidence of BM in different stages and subtypes of breast cancer. We reviewed the clinical records of 2193 consecutive breast cancer patients who presented between January 1999 and December 2010. We explored the incidence of BM in relation to standard clinicopathological factors, and determined the cumulative risk of BM according to the disease stage and phenotype. Of the 2193 included women, 160 (7.3%) developed BM at a median follow-up of 5.8 years. Age younger than 60 years (P = .015), larger tumors (P = .004), lymph node (LN) positivity (P < .001), high tumor grade (P = .012), and HER2 positivity (P < .001) were associated with higher incidence of BM in the whole population. In patients who presented with locoregional disease, 3 factors independently predicted BM: large tumors (hazard ratio [HR], 3.60; 95% confidence interval [CI], 1.54-8.38; P = .003), axillary LN metastasis (HR, 4.03; 95% CI, 1.91-8.52; P < .001), and HER2 positivity (HR, 1.89; 95% CI, 1.0-3.41; P = .049). A Brain Relapse Index was formulated using those 3 factors, with 5-year cumulative incidence of BM of 19.2% in those having the 2 or 3 risk factors versus 2.5% in those with no or 1 risk factor (P < .001). In metastatic patients, 3 factors were associated with higher risk of BM: HER2 positivity (P = .007), shorter relapse-free interval (P < .001), and lung metastasis (P < .001). Disease stage and biological subtypes predict the risk for BM and subsequent treatment outcome. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

    Science.gov (United States)

    Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

    2018-02-01

    To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ishiyama, Hiromichi, E-mail: hishiyam@kitasato-u.ac.jp [Department of Radiology, Kitasato University School of Medicine, Sagamihara, Kanagawa (Japan); Nakamura, Ryuji [Department of Radiology, Iwate Medical University, Morioka, Iwate (Japan); Satoh, Takefumi [Department of Urology, Kitasato University School of Medicine, Sagamihara, Kanagawa (Japan); Tanji, Susumu [Department of Urology, Iwate Medical University, Morioka, Iwate (Japan); Teh, Bin S. [Department of Radiation Oncology, The Methodist Hospital, Houston, TX (United States); Uemae, Mineko [Division of Radiation Oncology, Kitasato University Hospital, Sagamihara, Kanagawa (Japan); Baba, Shiro [Department of Urology, Kitasato University School of Medicine, Sagamihara, Kanagawa (Japan); Hayakawa, Kazushige [Department of Radiology, Kitasato University School of Medicine, Sagamihara, Kanagawa (Japan)

    2012-04-01

    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.

  17. A 7 gene expression score predicts for radiation response in cancer cervix

    International Nuclear Information System (INIS)

    Rajkumar, Thangarajan; Vijayalakshmi, Neelakantan; Sabitha, Kesavan; Shirley, Sundersingh; Selvaluxmy, Ganesharaja; Bose, Mayil Vahanan; Nambaru, Lavanya

    2009-01-01

    Cervical cancer is the most common cancer among Indian women. The current recommendations are to treat the stage IIB, IIIA, IIIB and IVA with radical radiotherapy and weekly cisplatin based chemotherapy. However, Radiotherapy alone can help cure more than 60% of stage IIB and up to 40% of stage IIIB patients. Archival RNA samples from 15 patients who had achieved complete remission and stayed disease free for more than 36 months (No Evidence of Disease or NED group) and 10 patients who had failed radical radiotherapy (Failed group) were included in the study. The RNA were amplified, labelled and hybridized to Stanford microarray chips and analyzed using BRB Array Tools software and Significance Analysis of Microarray (SAM) analysis. 20 genes were selected for further validation using Relative Quantitation (RQ) Taqman assay in a Taqman Low-Density Array (TLDA) format. The RQ value was calculated, using each of the NED sample once as a calibrator. A scoring system was developed based on the RQ value for the genes. Using a seven gene based scoring system, it was possible to distinguish between the tumours which were likely to respond to the radiotherapy and those likely to fail. The mean score ± 2 SE (standard error of mean) was used and at a cut-off score of greater than 5.60, the sensitivity, specificity, Positive predictive value (PPV) and Negative predictive value (NPV) were 0.64, 1.0, 1.0, 0.67, respectively, for the low risk group. We have identified a 7 gene signature which could help identify patients with cervical cancer who can be treated with radiotherapy alone. However, this needs to be validated in a larger patient population

  18. Development of associations and kinetic models for microbiological data to be used in comprehensive food safety prediction software.

    Science.gov (United States)

    Halder, Amit; Black, D Glenn; Davidson, P Michael; Datta, Ashim

    2010-08-01

    The objective of this study was to use an existing database of food products and their associated processes, link it with a list of the foodborne pathogenic microorganisms associated with those products and finally identify growth and inactivation kinetic parameters associated with those pathogens. The database was to be used as a part of the development of comprehensive software which could predict food safety and quality for any food product. The main issues in building such a predictive system included selection of predictive models, associations of different food types with pathogens (as determined from outbreak histories), and variability in data from different experiments. More than 1000 data sets from published literature were analyzed and grouped according to microorganisms and food types. Final grouping of data consisted of the 8 most prevalent pathogens for 14 different food groups, covering all of the foods (>7000) listed in the USDA Natl. Nutrient Database. Data for each group were analyzed in terms of 1st-order inactivation, 1st-order growth, and sigmoidal growth models, and their kinetic response for growth and inactivation as a function of temperature were reported. Means and 95% confidence intervals were calculated for prediction equations. The primary advantage in obtaining group-specific kinetic data is the ability to extend microbiological growth and death simulation to a large array of product and process possibilities, while still being reasonably accurate. Such simulation capability could provide vital ''what if'' scenarios for industry, Extension, and academia in food safety.

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

    OpenAIRE

    Mawatari, Yuki; Fukushima, Mikiko

    2016-01-01

    Yuki Mawatari,1 Mikiko Fukushima2 1Igo Ophthalmic Clinic, Kagoshima, 2Department of Ophthalmology, Faculty of Life Science, Kumamoto University, Chuo-ku, Kumamoto, Japan Purpose: This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection.Methods: Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller’s muscle complex (levator resection). P...

  20. 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.

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

    NARCIS (Netherlands)

    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, N

    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

  2. Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population

    NARCIS (Netherlands)

    Maaren, M.C. van; Steenbeek, C.D. van; Pharoah, P.D.; Witteveen, A.; Sonke, G.S.; Strobbe, L.J.A.; Poortmans, P.; Siesling, S.

    2017-01-01

    BACKGROUND: PREDICT version 2.0 is increasingly used to estimate prognosis in breast cancer. This study aimed to validate this tool in specific prognostic subgroups in the Netherlands. METHODS: All operated women with non-metastatic primary invasive breast cancer, diagnosed in 2005, were selected

  3. Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population

    NARCIS (Netherlands)

    van Maaren, M. C.; van Steenbeek, C. D.; Pharoah, P. D.P.; Witteveen, A.; Sonke, Gabe S.; Strobbe, L.J.A.; Poortmans, P.M.P.; Siesling, S.

    2017-01-01

    Background PREDICT version 2.0 is increasingly used to estimate prognosis in breast cancer. This study aimed to validate this tool in specific prognostic subgroups in the Netherlands. Methods All operated women with non-metastatic primary invasive breast cancer, diagnosed in 2005, were selected from

  4. MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data.

    Science.gov (United States)

    Gupta, Ankit; Kapil, Rohan; Dhakan, Darshan B; Sharma, Vineet K

    2014-01-01

    The identification of virulent proteins in any de-novo sequenced genome is useful in estimating its pathogenic ability and understanding the mechanism of pathogenesis. Similarly, the identification of such proteins could be valuable in comparing the metagenome of healthy and diseased individuals and estimating the proportion of pathogenic species. However, the common challenge in both the above tasks is the identification of virulent proteins since a significant proportion of genomic and metagenomic proteins are novel and yet unannotated. The currently available tools which carry out the identification of virulent proteins provide limited accuracy and cannot be used on large datasets. Therefore, we have developed an MP3 standalone tool and web server for the prediction of pathogenic proteins in both genomic and metagenomic datasets. MP3 is developed using an integrated Support Vector Machine (SVM) and Hidden Markov Model (HMM) approach to carry out highly fast, sensitive and accurate prediction of pathogenic proteins. It displayed Sensitivity, Specificity, MCC and accuracy values of 92%, 100%, 0.92 and 96%, respectively, on blind dataset constructed using complete proteins. On the two metagenomic blind datasets (Blind A: 51-100 amino acids and Blind B: 30-50 amino acids), it displayed Sensitivity, Specificity, MCC and accuracy values of 82.39%, 97.86%, 0.80 and 89.32% for Blind A and 71.60%, 94.48%, 0.67 and 81.86% for Blind B, respectively. In addition, the performance of MP3 was validated on selected bacterial genomic and real metagenomic datasets. To our knowledge, MP3 is the only program that specializes in fast and accurate identification of partial pathogenic proteins predicted from short (100-150 bp) metagenomic reads and also performs exceptionally well on complete protein sequences. MP3 is publicly available at http://metagenomics.iiserb.ac.in/mp3/index.php.

  5. Risk stratification and prediction of cancer of focal thyroid fluorodeoxyglucose uptake during cancer evaluation

    International Nuclear Information System (INIS)

    Kim, Bo-Hyun; Na, Min-A.; Kim, In-Joo; Kim, Seong-Jang; Kim, Yong-Ki

    2010-01-01

    Focal thyroid incidentaloma by F-18 2-deoxy-2-F18-fluoro-D-glucose (FDG) positron emission tomography (PET) has been reported 1-4% of cancer patients and normal healthy population, with a risk of cancer ranging 14-50%. The aim of this study was to investigate the prevalence of thyroid incidentaloma in F-18 FDG PET/CT and risk of cancer, usefulness of visual and SUV max and SUV mean differentiating malignant nodules and to define the predictable variables. A total 159 patients with focal thyroid FDG incidentaloma during cancer evaluation with non-thyroid cancer were enrolled. After F-18 PET/CT, we analyzed the image visually and obtained semiquantitative indices. The incidence of focal FDG thyroid incidentaloma is 1.36% and cancer risk is 23.3%. The incidence of focal thyroid FDG uptake was significantly higher in women (2.88 vs. 0.31%; X 2 =136.4, p max (malignant: median 4.53, range 2.1-12.0; benign: median 3.08, range 1.6-35, p=0.0093). However, SUV mean have no statistical differences (malignant: median 2.17, range 1.77-3.19; benign: median 2.05, range 1.15-5.77, p=0.0541). In ROC analyses, the optimal visual grades were >grade 3, and the optimal semiquantitative indices were 4.46 for SUV max , 2.03 for SUV mean . The visual grade was superior to other variables for the differentiation malignant from benign thyroid incidentalomas. The size and visual grade was the potent predictor by logistic regression analysis. Focal thyroid FDG incidentalomas in non-thyroid cancer patients during evaluation have a high risk of malignancy. The size and visual grade are potential predictors for malignant thyroid incidentaloma. (author)

  6. Integrated Design Software Predicts the Creep Life of Monolithic Ceramic Components

    Science.gov (United States)

    1996-01-01

    Significant improvements in propulsion and power generation for the next century will require revolutionary advances in high-temperature materials and structural design. Advanced ceramics are candidate materials for these elevated-temperature applications. As design protocols emerge for these material systems, designers must be aware of several innate features, including the degrading ability of ceramics to carry sustained load. Usually, time-dependent failure in ceramics occurs because of two different, delayedfailure mechanisms: slow crack growth and creep rupture. Slow crack growth initiates at a preexisting flaw and continues until a critical crack length is reached, causing catastrophic failure. Creep rupture, on the other hand, occurs because of bulk damage in the material: void nucleation and coalescence that eventually leads to macrocracks which then propagate to failure. Successful application of advanced ceramics depends on proper characterization of material behavior and the use of an appropriate design methodology. The life of a ceramic component can be predicted with the NASA Lewis Research Center's Ceramics Analysis and Reliability Evaluation of Structures (CARES) integrated design programs. CARES/CREEP determines the expected life of a component under creep conditions, and CARES/LIFE predicts the component life due to fast fracture and subcritical crack growth. The previously developed CARES/LIFE program has been used in numerous industrial and Government applications.

  7. Circulating tumor cells predict survival benefit from chemotherapy in patients with lung cancer.

    Science.gov (United States)

    Wu, Zhuo-Xuan; Liu, Zhen; Jiang, Han-Ling; Pan, Hong-Ming; Han, Wei-Dong

    2016-10-11

    This meta-analysis was to explore the clinical significance of circulating tumor cells (CTCs) in predicting the tumor response to chemotherapy and prognosis of patients with lung cancer. We searched PubMed, Embase, Cochrane Database, Web of Science and reference lists of relevant articles. Our meta-analysis was performed by Stata software, version 12.0, with a random effects model. Risk ratio (RR), hazard ratio (HR) and 95% confidence intervals (CI) were used as effect measures. 8 studies, including 453 patients, were eligible for analyses. We showed that the disease control rate (DCR) in CTCs-negative patients was significantly higher than CTCs-positive patients at baseline (RR = 2.56, 95%CI [1.36, 4.82], p chemotherapy (RR = 9.08, CI [3.44, 23.98], p chemotherapy had a worse disease progression than those with CTC-positive to negative or persistently negative (RR = 8.52, CI [1.66, 43.83], p chemotherapy also indicated poor overall survival (OS) (baseline: HR = 3.43, CI [2.21, 5.33], pchemotherapy: HR = 3.16, CI [2.23, 4.48], p chemotherapy: HR = 3.78, CI [2.33, 6.13], p chemotherapy and poor prognosis in patients with lung cancer.

  8. Scoring system development for prediction of extravesical bladder cancer

    Directory of Open Access Journals (Sweden)

    Prelević Rade

    2014-01-01

    Full Text Available Background/Aim. Staging of bladder cancer is crucial for optimal management of the disease. However, clinical staging is not perfectly accurate. The aim of this study was to derive a simple scoring system in prediction of pathological advanced muscle-invasive bladder cancer (MIBC. Methods. Logistic regression and bootstrap methods were used to create an integer score for estimating the risk in prediction of pathological advanced MIBC using precystectomy clinicopathological data: demographic, initial transurethral resection (TUR [grade, stage, multiplicity of tumors, lymphovascular invasion (LVI], hydronephrosis, abdominal and pelvic CT radiography (size of the tumor, tumor base width, and pathological stage after radical cystectomy (RC. Advanced MIBC in surgical specimen was defined as pT3-4 tumor. Receiving operating characteristic (ROC curve quantified the area under curve (AUC as predictive accuracy. Clinical usefulness was assessed by using decision curve analysis. Results. This single-center retrospective study included 233 adult patients with BC undergoing RC at the Military Medical Academy, Belgrade. Organ confined disease was observed in 101 (43.3% patients, and 132 (56.7% had advanced MIBC. In multivariable analysis, 3 risk factors most strongly associated with advanced MIBC: grade of initial TUR [odds ratio (OR = 4.7], LVI (OR = 2, and hydronephrosis (OR = 3.9. The resultant total possible score ranged from 0 to 15, with the cut-off value of > 8 points, the AUC was 0.795, showing good discriminatory ability. The model showed excellent calibration. Decision curve analysis showed a net benefit across all threshold probabilities and clinical usefulness of the model. Conclusion. We developed a unique scoring system which could assist in predicting advanced MIBC in patients before RC. The scoring system showed good performance characteristics and introducing of such a tool into daily clinical decision-making may lead to more appropriate

  9. Predicted cycloartenol synthase protein from Kandelia obovata and Rhizophora stylosa using online software of Phyre2 and Swiss-model

    Science.gov (United States)

    Basyuni, M.; Sulistiyono, N.; Wati, R.; Sumardi; Oku, H.; Baba, S.; Sagami, H.

    2018-03-01

    Cloning of Kandelia obovata KcCAS gene (previously known as Kandelia candel) and Rhizophora stylosa RsCAS have already have been reported and encoded cycloartenol synthases. In this study, the predicted KcCAS and RsCAS protein were analyzed using online software of Phyre2 and Swiss-model. The protein modelling for KcCAS and RsCAS cycloartenol synthases was determined using Pyre2 had similar results with slightly different in sequence identity. By contrast, the Swiss-model for KcCAS slightly had higher sequence identity (47.31%) and Qmean (0.70) compared to RsCAS. No difference of ligands binding site which is considered as modulators for both cycloartenol synthases. The range of predicted protein derived from 91-757 amino acid residues with coverage sequence similarities 0.86, respectively from template model of lanosterol synthase from the human. Homology modelling revealed that 706 residues (93% of the amino acid sequence) had been modelled with 100.0% confidence by the single highest scoring template for both KcCAS and RsCAS using Phyre2. This coverage was more elevated than swiss-model predicted (86%). The present study suggested that both genes are responsible for the genesis of cycloartenol in these mangrove plants.

  10. Migration Phenotype of Brain-Cancer Cells Predicts Patient Outcomes

    Directory of Open Access Journals (Sweden)

    Chris L. Smith

    2016-06-01

    Full Text Available Glioblastoma multiforme is a heterogeneous and infiltrative cancer with dismal prognosis. Studying the migratory behavior of tumor-derived cell populations can be informative, but it places a high premium on the precision of in vitro methods and the relevance of in vivo conditions. In particular, the analysis of 2D cell migration may not reflect invasion into 3D extracellular matrices in vivo. Here, we describe a method that allows time-resolved studies of primary cell migration with single-cell resolution on a fibrillar surface that closely mimics in vivo 3D migration. We used this platform to screen 14 patient-derived glioblastoma samples. We observed that the migratory phenotype of a subset of cells in response to platelet-derived growth factor was highly predictive of tumor location and recurrence in the clinic. Therefore, migratory phenotypic classifiers analyzed at the single-cell level in a patient-specific way can provide high diagnostic and prognostic value for invasive cancers.

  11. From data to the decision: A software architecture to integrate predictive modelling in clinical settings.

    Science.gov (United States)

    Martinez-Millana, A; Fernandez-Llatas, C; Sacchi, L; Segagni, D; Guillen, S; Bellazzi, R; Traver, V

    2015-08-01

    The application of statistics and mathematics over large amounts of data is providing healthcare systems with new tools for screening and managing multiple diseases. Nonetheless, these tools have many technical and clinical limitations as they are based on datasets with concrete characteristics. This proposition paper describes a novel architecture focused on providing a validation framework for discrimination and prediction models in the screening of Type 2 diabetes. For that, the architecture has been designed to gather different data sources under a common data structure and, furthermore, to be controlled by a centralized component (Orchestrator) in charge of directing the interaction flows among data sources, models and graphical user interfaces. This innovative approach aims to overcome the data-dependency of the models by providing a validation framework for the models as they are used within clinical settings.

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

    International Nuclear Information System (INIS)

    Boutilier, Justin J.; Lee, Taewoo; Craig, Tim; Sharpe, Michael B.; Chan, Timothy C. Y.

    2015-01-01

    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

  13. 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

  14. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer

    NARCIS (Netherlands)

    Petersen, Japke F.; Stuiver, Martijn M.; Timmermans, Adriana J.; Chen, Amy; Zhang, Hongzhen; O'Neill, James P.; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T.; Koch, Wayne; van den Brekel, Michiel W. M.

    2017-01-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442

  15. Predicting Genes Involved in Human Cancer Using Network Contextual Information

    Directory of Open Access Journals (Sweden)

    Rahmani Hossein

    2012-03-01

    Full Text Available Protein-Protein Interaction (PPI networks have been widely used for the task of predicting proteins involved in cancer. Previous research has shown that functional information about the protein for which a prediction is made, proximity to specific other proteins in the PPI network, as well as local network structure are informative features in this respect. In this work, we introduce two new types of input features, reflecting additional information: (1 Functional Context: the functions of proteins interacting with the target protein (rather than the protein itself; and (2 Structural Context: the relative position of the target protein with respect to specific other proteins selected according to a novel ANOVA (analysis of variance based measure. We also introduce a selection strategy to pinpoint the most informative features. Results show that the proposed feature types and feature selection strategy yield informative features. A standard machine learning method (Naive Bayes that uses the features proposed here outperforms the current state-of-the-art methods by more than 5% with respect to F-measure. In addition, manual inspection confirms the biological relevance of the top-ranked features.

  16. Prediction and mastering of wine acidity and tartaric precipitations: the Mextar® software tool

    Directory of Open Access Journals (Sweden)

    Audrey Devatine

    2002-06-01

    The article describes the theoretical background upon which MEXTAR® has been developed and details the computation procedures associated to the simulation of the operations described above. Iterative processes are used to determine the various parameters enounced above. They aim in particular at the verification of the electroneutrality of the sample. However, at first electroneutrality is rarely verified because the wine chemical analytical determination is never exhaustive. Therefore, we introduce the concept of «vinic acid» to compensate the negative ion deficit of usual wine chemical analytical determination. This «vinic acid» is describe as a diacid with a fixed second dissociation constant (pK2 = 5 and a first dissociation constant determined automatically by MEXTAR®. This enables MEXTAR® to simulate accurately experimental tartaric precipitations for several samples. Thanks to the predicting capacity of MEXTAR®, the correlation between the total polyphenol content (IPT in red wines and the difficulty to stabilise high IPT wine with respect to tartaric salts is confirmed. Finally, malolactic fermentations are also well simulated. A validation of MEXTAR® for the acid addition or removal has yet to be done when reliable experimental data are available.

  17. Nomogram to predict rectal toxicity following prostate cancer radiotherapy.

    Directory of Open Access Journals (Sweden)

    Jean-Bernard Delobel

    Full Text Available To identify predictors of acute and late rectal toxicity following prostate cancer radiotherapy (RT, while integrating the potential impact of RT technique, dose escalation, and moderate hypofractionation, thus enabling us to generate a nomogram for individual prediction.In total, 972 patients underwent RT for localized prostate cancer, to a total dose of 70 Gy or 80 Gy, using two different fractionations (2 Gy or 2.5 Gy/day, by means of several RT techniques (3D conformal RT [3DCRT], intensity-modulated RT [IMRT], or image-guided RT [IGRT]. Multivariate analyses were performed to identify predictors of acute and late rectal toxicity. A nomogram was generated based on the logistic regression model used to predict the 3-year rectal toxicity risk, with its accuracy assessed by dividing the cohort into training and validation subgroups.Mean follow-up for the entire cohort was 62 months, ranging from 6 to 235. The rate of acute Grade ≥2 rectal toxicity was 22.2%, decreasing when combining IMRT and IGRT, compared to 3DCRT (RR = 0.4, 95%CI: 0.3-0.6, p<0.01. The 5-year Grade ≥2 risks for rectal bleeding, urgency/tenesmus, diarrhea, and fecal incontinence were 9.9%, 4.5%, 2.8%, and 0.4%, respectively. The 3-year Grade ≥2 risk for overall rectal toxicity increased with total dose (p<0.01, RR = 1.1, 95%CI: 1.0-1.1 and dose per fraction (2Gy vs. 2.5Gy (p = 0.03, RR = 3.3, 95%CI: 1.1-10.0, and decreased when combining IMRT and IGRT (RR = 0.50, 95% CI: 0.3-0.8, p<0.01. Based on these three parameters, a nomogram was generated.Dose escalation and moderate hypofractionation increase late rectal toxicity. IMRT combined with IGRT markedly decreases acute and late rectal toxicity. Performing combined IMRT and IGRT can thus be envisaged for dose escalation and moderate hypofractionation. Our nomogram predicts the 3-year rectal toxicity risk by integrating total dose, fraction dose, and RT technique.

  18. Prediction of cancer incidence in Tyrol/Austria for year of diagnosis 2020.

    Science.gov (United States)

    Oberaigner, Willi; Geiger-Gritsch, Sabine

    2014-10-01

    Prediction of the number of incident cancer cases is very relevant for health planning purposes and allocation of resources. The shift towards elder age groups in central European populations in the next decades is likely to contribute to an increase in cancer incidence for many cancer sites. In Tyrol, cancer incidence data have been registered on a high level of completeness for more than 20 years. We therefore aimed to compute well-founded predictions of cancer incidence for Tyrol for the year 2020 for all frequent cancer sites and for all cancer sites combined. After defining a prediction base range for every cancer site, we extrapolated the age-specific time trends in the prediction base range following a linear model for increasing and a log-linear model for decreasing time trends. The extrapolated time trends were evaluated for the year 2020 applying population figures supplied by Statistics Austria. Compared with the number of annual incident cases for the year 2009 for all cancer sites combined except non-melanoma skin cancer, we predicted an increase of 235 (15 %) and 362 (21 %) for females and males, respectively. For both sexes, more than 90 % of the increase is attributable to the shift toward older age groups in the next decade. The biggest increase in absolute numbers is seen for females in breast cancer (92, 21 %), lung cancer (64, 52 %), colorectal cancer (40, 24 %), melanoma (38, 30 %) and the haematopoietic system (37, 35 %) and for males in prostate cancer (105, 25 %), colorectal cancer (91, 45 %), the haematopoietic system (71, 55 %), bladder cancer (69, 100 %) and melanoma (64, 52 %). The increase in the number of incident cancer cases of 15 % in females and 21 % in males in the next decade is very relevant for planning purposes. However, external factors cause uncertainty in the prediction of some cancer sites (mainly prostate cancer and colorectal cancer) and the prediction intervals are still broad. Therefore

  19. Improving breast cancer outcome prediction by combining multiple data sources

    NARCIS (Netherlands)

    Van Vliet, M.H.

    2010-01-01

    Cancer has recently become the number one cause of death in The Netherlands. Breast cancer is the most prevalent form of cancer among females, with a lifetime risk of 12.8% (i.e. the 1 in 8 rule). As a result, more and more research is devoted to getting a better insight into cancer, and to further

  20. Validation of software components for the prediction of irradiation-induced damage of RPV steel

    International Nuclear Information System (INIS)

    Bergner, Frank; Birkenheuer, Uwe; Ulbricht, Andreas

    2010-04-01

    The modelling of irradiation-induced damage of RPV steels from primary cascades up to the change of mechanical properties bridging length scales from the atomic level up to the macro-scale and time scales up to years contributes essentially to an improved understanding of the phenomenon of neutron embrittlement. In future modelling may become a constituent of the procedure to evaluate RPV safety. The selected two-step approach is based upon the coupling of a rate-theory module aimed at simulating the evolution of the size distribution of defect-solute clusters with a hardening module aimed at predicting the yield stress increase. The scope of the investigation consists in the development and validation of corresponding numerical tools. In order to validate these tools, the output of representative simulations is compared with results from small-angle neutron scattering experiments and tensile tests performed for neutron-irradiated RPV steels. Using the developed rate-theory module it is possible to simulate the evolution of size, concentration and composition of mixed Cu-vacancy clusters over the relevant ranges of size up to 10.000 atoms and time up to tens of years. The connection between the rate-theory model and hardening is based upon both the mean spacing and the strength of obstacles for dislocation glide. As a result of the validation procedure of the numerical tools, we have found that essential trends of the irradiation-induced yield stress increase of Cu-bearing and low-Cu RPV steels are displayed correctly. First ideas on how to take into account the effect of Ni on both cluster evolution and hardening are worked out.

  1. Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction

    Science.gov (United States)

    2017-12-01

    19 NIH Exploiting drivers of androgen receptor signaling negative prostate cancer for precision medicine Goal(s): Identify novel potential drivers...AWARD NUMBER: W81XWH-14-1-0466 TITLE: Clonal evaluation of prostate cancer by ERG/SPINK1 status to improve prognosis prediction PRINCIPAL...Sept 2017 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction 5b

  2. Proposal for future diagnosis and management of vascular tumors by using automatic software for image processing and statistic prediction.

    Science.gov (United States)

    Popescu, M D; Draghici, L; Secheli, I; Secheli, M; Codrescu, M; Draghici, I

    2015-01-01

    Infantile Hemangiomas (IH) are the most frequent tumors of vascular origin, and the differential diagnosis from vascular malformations is difficult to establish. Specific types of IH due to the location, dimensions and fast evolution, can determine important functional and esthetic sequels. To avoid these unfortunate consequences it is necessary to establish the exact appropriate moment to begin the treatment and decide which the most adequate therapeutic procedure is. Based on clinical data collected by a serial clinical observations correlated with imaging data, and processed by a computer-aided diagnosis system (CAD), the study intended to develop a treatment algorithm to accurately predict the best final results, from the esthetical and functional point of view, for a certain type of lesion. The preliminary database was composed of 75 patients divided into 4 groups according to the treatment management they received: medical therapy, sclerotherapy, surgical excision and no treatment. The serial clinical observation was performed each month and all the data was processed by using CAD. The project goal was to create a software that incorporated advanced methods to accurately measure the specific IH lesions, integrated medical information, statistical methods and computational methods to correlate this information with that obtained from the processing of images. Based on these correlations, a prediction mechanism of the evolution of hemangioma, which helped determine the best method of therapeutic intervention to minimize further complications, was established.

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

    International Nuclear Information System (INIS)

    Helal, N.; Sarwat, E.

    2012-01-01

    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.

  4. CuboCube: Student creation of a cancer genetics e-textbook using open-access software for social learning.

    Directory of Open Access Journals (Sweden)

    Puya Seid-Karbasi

    2017-03-01

    Full Text Available Student creation of educational materials has the capacity both to enhance learning and to decrease costs. Three successive honors-style classes of undergraduate students in a cancer genetics class worked with a new software system, CuboCube, to create an e-textbook. CuboCube is an open-source learning materials creation system designed to facilitate e-textbook development, with an ultimate goal of improving the social learning experience for students. Equipped with crowdsourcing capabilities, CuboCube provides intuitive tools for nontechnical and technical authors alike to create content together in a structured manner. The process of e-textbook development revealed both strengths and challenges of the approach, which can inform future efforts. Both the CuboCube platform and the Cancer Genetics E-textbook are freely available to the community.

  5. Pancreatic cancer circulating tumour cells express a cell motility gene signature that predicts survival after surgery

    International Nuclear Information System (INIS)

    Sergeant, Gregory; Eijsden, Rudy van; Roskams, Tania; Van Duppen, Victor; Topal, Baki

    2012-01-01

    Most cancer deaths are caused by metastases, resulting from circulating tumor cells (CTC) that detach from the primary cancer and survive in distant organs. The aim of the present study was to develop a CTC gene signature and to assess its prognostic relevance after surgery for pancreatic ductal adenocarcinoma (PDAC). Negative depletion fluorescence activated cell sorting (FACS) was developed and validated with spiking experiments using cancer cell lines in whole human blood samples. This FACS-based method was used to enrich for CTC from the blood of 10 patients who underwent surgery for PDAC. Total RNA was isolated from 4 subgroup samples, i.e. CTC, haematological cells (G), original tumour (T), and non-tumoural pancreatic control tissue (P). After RNA quality control, samples of 6 patients were eligible for further analysis. Whole genome microarray analysis was performed after double linear amplification of RNA. ‘Ingenuity Pathway Analysis’ software and AmiGO were used for functional data analyses. A CTC gene signature was developed and validated with the nCounter system on expression data of 78 primary PDAC using Cox regression analysis for disease-free (DFS) and overall survival (OS). Using stringent statistical analysis, we retained 8,152 genes to compare expression profiles of CTC vs. other subgroups, and found 1,059 genes to be differentially expressed. The pathway with the highest expression ratio in CTC was p38 mitogen-activated protein kinase (p38 MAPK) signaling, known to be involved in cancer cell migration. In the p38 MAPK pathway, TGF-β1, cPLA2, and MAX were significantly upregulated. In addition, 9 other genes associated with both p38 MAPK signaling and cell motility were overexpressed in CTC. High co-expression of TGF-β1 and our cell motility panel (≥ 4 out of 9 genes for DFS and ≥ 6 out of 9 genes for OS) in primary PDAC was identified as an independent predictor of DFS (p=0.041, HR (95% CI) = 1.885 (1.025 – 3.559)) and OS (p=0.047, HR

  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. Mathematical modeling and computational prediction of cancer drug resistance.

    Science.gov (United States)

    Sun, Xiaoqiang; Hu, Bin

    2017-06-23

    Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of

  8. Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients.

    Directory of Open Access Journals (Sweden)

    Mark E Sherman

    Full Text Available Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown.Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC for 37,939 invasive breast cancers (1996-2007, we estimated 5-year breast cancer risk (<1%; 1-1.66%; ≥1.67% with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions; Breast Cancer Risk Assessment Tool (BCRAT; and BCSC 5-year risk model (BCSC-5. Breast cancer-specific mortality post-diagnosis (range: 1-13 years; median: 5.4-5.6 years was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35-44; 45-54; 55-69; 70-89 years models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years.Of 6,021 deaths, 2,993 (49.7% were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ≥1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR = 0.82 (95% CI = 0.75-0.90; BCRAT: HR = 0.72 (95% CI = 0.65-0.81 and BCSC-5: HR = 0.84 (95% CI = 0.75-0.94. Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55-69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35-44 years.Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering women counseling, it may be useful to note that high

  9. Nanoreporter PET predicts the efficacy of anti-cancer nanotherapy

    NARCIS (Netherlands)

    Pérez-Medina, Carlos; Abdel-Atti, Dalya; Tang, Jun; Zhao, Yiming; Fayad, Zahi A.; Lewis, Jason S.; Mulder, Willem J. M.; Reiner, Thomas

    2016-01-01

    The application of nanoparticle drug formulations, such as nanoliposomal doxorubicin (Doxil), is increasingly integrated in clinical cancer care. Despite nanomedicine's remarkable potential and growth over the last three decades, its clinical benefits for cancer patients vary. Here we report a

  10. Predictive analysis of photodynamic therapy applied to esophagus cancer

    Science.gov (United States)

    Fanjul-Vélez, F.; del Campo-Gutiérrez, M.; Ortega-Quijano, N.; Arce-Diego, J. L.

    2008-04-01

    The use of optical techniques in medicine has revolutionized in many cases the medical praxis, providing new tools for practitioners or improving the existing ones in the fight against diseases. The application of this technology comprises mainly two branches, characterization and treatment of biological tissues. Photodynamic Therapy (PDT) provides a solution for malignant tissue destruction, by means of the inoculation of a photosensitizer and irradiation by an optical source. The key factor of the procedure is the localization of the damage to avoid collateral harmful effects. The volume of tissue destroyed depends on the type of photosensitizer inoculated, both on its reactive characteristics and its distribution inside the tissue, and also on the specific properties of the optical source, that is, the optical power, wavelength and exposition time. In this work, a model for PDT based on the one-dimensional diffusion equation, extensible to 3D, to estimate the optical distribution in tissue, and on photosensitizer parameters to take into account the photobleaching effect is proposed. The application to esophagus cancer allows the selection of the right optical source parameters, like irradiance, wavelength or exposition time, in order to predict the area of tissue destruction.

  11. 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.

  12. Predictive utility of cyclo-oxygenase-2 expression by colon and rectal cancer.

    Science.gov (United States)

    Lobo Prabhu, Kristel C; Vu, Lan; Chan, Simon K; Phang, Terry; Gown, Allen; Jones, Steven J; Wiseman, Sam M

    2014-05-01

    Cyclo-oxygenase-2 (COX-2), an inducible enzyme expressed in areas of inflammation, is a target of interest for colorectal cancer therapy. Currently, the predictive significance of COX-2 in colorectal cancer remains unclear. Tissue microarrays were constructed using 118 colon cancer and 85 rectal cancer specimens; 44 synchronous metastatic colon cancer and 22 rectal cancer lymph nodes were also evaluated. COX-2 expression was assessed by immunohistochemistry. Univariate analysis was used to determine the predictive significance of clinicopathologic variables. Overall survival, disease-specific survival, and disease-free survival were the main outcomes examined. COX-2 was found to be expressed in 93% of colon cancers and 87% of rectal cancers. Decreased COX-2 expression was related to decreased disease-specific survival (P = .016) and decreased disease-free survival (P = .019) in the rectal cancer cohort but not in the colon cancer cohort. COX-2 expression has predictive utility for management of rectal but not colon cancer. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Parameters recorded by software of non-invasive ventilators predict COPD exacerbation: a proof-of-concept study.

    Science.gov (United States)

    Borel, Jean-Christian; Pelletier, Julie; Taleux, Nellie; Briault, Amandine; Arnol, Nathalie; Pison, Christophe; Tamisier, Renaud; Timsit, Jean-François; Pepin, Jean-Louis

    2015-03-01

    To assess whether daily variations in three parameters recorded by non-invasive ventilation (NIV) software (respiratory rate (RR), percentage of respiratory cycles triggered by the patient (%Trigg) and NIV daily use) predict the risk of exacerbation in patients with chronic obstructive pulmonary disease (COPD) treated by home NIV. Patients completed the EXACT-Pro questionnaire daily to detect exacerbations. The 25th and 75th percentiles of each 24 h NIV parameter were calculated and updated daily. For a given day, when the value of any parameter was >75th or 75th, 'low value' <25th). Stratified conditional logistic regressions estimated the risk of exacerbation when ≥2 days (for RR and %Trigg) or ≥3 days (for NIV use) out of five had an 'abnormal value'. Sixty-four patients were included. Twenty-one exacerbations were detected and medically confirmed. The risk of exacerbation was increased when RR (OR 5.6, 95% CI 1.4 to 22.4) and %Trigg (OR 4.0, 95% CI 1.1 to 14.5) were considered as 'high value' on ≥2 days out of five. This proof-of-concept study shows that daily variations in RR and %Trigg are predictors of an exacerbation. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

    , predominantly in cancer cells, or in both stromal and cancer cells. There was no obvious difference between the HER2-positive and HER2-negative tumors in terms of the miR-21 expression patterns and intensities. To explore the possibility that miR-21 expression levels and/or cellular localization could predict...... expression patterns and intensities revealed no association between the miR-21 scores in the cancer cell population (p = 0.69) or the stromal cells population (p = 0.13) and recurrent disease after adjuvant trastuzumab. Thus, our findings show that elevated miR-21 expression does not predict resistance......Trastuzumab is established as standard care for patients with HER2-positive breast cancer both in the adjuvant and metastatic setting. However, 50% of the patients do not respond to the trastuzumab therapy, and therefore new predictive biomarkers are highly warranted. MicroRNAs (miRs) constitute...

  15. Comparing Visually Assessed BI-RADS Breast Density and Automated Volumetric Breast Density Software: A Cross-Sectional Study in a Breast Cancer Screening Setting.

    Science.gov (United States)

    van der Waal, Daniëlle; den Heeten, Gerard J; Pijnappel, Ruud M; Schuur, Klaas H; Timmers, Johanna M H; Verbeek, André L M; Broeders, Mireille J M

    2015-01-01

    The objective of this study is to compare different methods for measuring breast density, both visual assessments and automated volumetric density, in a breast cancer screening setting. These measures could potentially be implemented in future screening programmes, in the context of personalised screening or screening evaluation. Digital mammographic exams (N = 992) of women participating in the Dutch breast cancer screening programme (age 50-75y) in 2013 were included. Breast density was measured in three different ways: BI-RADS density (5th edition) and with two commercially available automated software programs (Quantra and Volpara volumetric density). BI-RADS density (ordinal scale) was assessed by three radiologists. Quantra (v1.3) and Volpara (v1.5.0) provide continuous estimates. Different comparison methods were used, including Bland-Altman plots and correlation coefficients (e.g., intraclass correlation coefficient [ICC]). Based on the BI-RADS classification, 40.8% of the women had 'heterogeneously or extremely dense' breasts. The median volumetric percent density was 12.1% (IQR: 9.6-16.5) for Quantra, which was higher than the Volpara estimate (median 6.6%, IQR: 4.4-10.9). The mean difference between Quantra and Volpara was 5.19% (95% CI: 5.04-5.34) (ICC: 0.64). There was a clear increase in volumetric percent dense volume as BI-RADS density increased. The highest accuracy for predicting the presence of BI-RADS c+d (heterogeneously or extremely dense) was observed with a cut-off value of 8.0% for Volpara and 13.8% for Quantra. Although there was no perfect agreement, there appeared to be a strong association between all three measures. Both volumetric density measures seem to be usable in breast cancer screening programmes, provided that the required data flow can be realized.

  16. The Neutrophil-Platelet Score (NPS Predicts Survival in Primary Operable Colorectal Cancer and a Variety of Common Cancers.

    Directory of Open Access Journals (Sweden)

    David G Watt

    Full Text Available Recent in-vitro studies have suggested that a critical checkpoint early in the inflammatory process involves the interaction between neutrophils and platelets. This confirms the importance of the innate immune system in the elaboration of the systemic inflammatory response. The aim of the present study was to examine whether a combination of the neutrophil and platelet counts were predictive of survival in patients with cancer.Patients with histologically proven colorectal cancer who underwent potentially curative resection at a single centre between March 1999 and May 2013 (n = 796 and patients with cancer from the Glasgow Inflammation Outcome Study, who had a blood sample taken between January 2000 and December 2007 (n = 9649 were included in the analysis.In the colorectal cancer cohort, there were 173 cancer and 135 non-cancer deaths. In patients undergoing elective surgery, cancer-specific survival (CSS at 5 years ranged from 97% in patients with TNM I disease and NPS = 0 to 57% in patients with TNM III disease and NPS = 2 (p = 0.019 and in patients undergoing elective surgery for node-negative colon cancer from 98% (TNM I, NPS = 0 to 65% (TNM II, NPS = 2 (p = 0.004. In those with a variety of common cancers there were 5218 cancer and 929 non-cancer deaths. On multivariate analysis, adjusting for age and sex and stratified by tumour site, incremental increase in the NPS was significantly associated with poorer CSS (p<0.001.The neutrophil-platelet score predicted survival in a variety of common cancers and highlights the importance of the innate immune system in patients with cancer.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-09-27

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

  18. Comparison of Automated Atlas Based Segmentation Software for postoperative prostate cancer radiotherapy

    Directory of Open Access Journals (Sweden)

    Grégory Delpon

    2016-08-01

    Full Text Available Automated atlas-based segmentation algorithms present the potential to reduce the variability in volume delineation. Several vendors offer software that are mainly used for cranial, head and neck and prostate cases. The present study will compare the contours produced by a radiation oncologist to the contours computed by different automated atlas-based segmentation algorithms for prostate bed cases, including femoral heads, bladder and rectum. Contour comparison was evaluated by different metrics such as volume ratio, Dice coefficient and Hausdorff distance. Results depended on the volume of interest and showed some discrepancies between the different software. Automatic contours could be a good starting point for the delineation of organs since efficient editing tools are provided by different vendors. It should become an important help in the next few years for organ at risk delineation.

  19. The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer.

    Science.gov (United States)

    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

    2015-02-01

    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 Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients' actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: -1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74-0.81) and 0.73 (95% CI: 0.68-0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings.

  20. Predicted vitamin D status and colon cancer recurrence and mortality in CALGB 89803 (Alliance).

    Science.gov (United States)

    Fuchs, M A; Yuan, C; Sato, K; Niedzwiecki, D; Ye, X; Saltz, L B; Mayer, R J; Mowat, R B; Whittom, R; Hantel, A; Benson, A; Atienza, D; Messino, M; Kindler, H; Venook, A; Innocenti, F; Warren, R S; Bertagnolli, M M; Ogino, S; Giovannucci, E L; Horvath, E; Meyerhardt, J A; Ng, K

    2017-06-01

    Observational studies suggest that higher levels of 25-hydroxyvitamin D3 (25(OH)D) are associated with a reduced risk of colorectal cancer and improved survival of colorectal cancer patients. However, the influence of vitamin D status on cancer recurrence and survival of patients with stage III colon cancer is unknown. We prospectively examined the influence of post-diagnosis predicted plasma 25(OH)D on outcome among 1016 patients with stage III colon cancer who were enrolled in a National Cancer Institute-sponsored adjuvant therapy trial (CALGB 89803). Predicted 25(OH)D scores were computed using validated regression models. We examined the influence of predicted 25(OH)D scores on cancer recurrence and mortality (disease-free survival; DFS) using Cox proportional hazards. Patients in the highest quintile of predicted 25(OH)D score had an adjusted hazard ratio (HR) for colon cancer recurrence or mortality (DFS) of 0.62 (95% confidence interval [CI], 0.44-0.86), compared with those in the lowest quintile (Ptrend = 0.005). Higher predicted 25(OH)D score was also associated with a significant improvement in recurrence-free survival and overall survival (Ptrend = 0.01 and 0.0004, respectively). The benefit associated with higher predicted 25(OH)D score appeared consistent across predictors of cancer outcome and strata of molecular tumor characteristics, including microsatellite instability and KRAS, BRAF, PIK3CA, and TP53 mutation status. Higher predicted 25(OH)D levels after a diagnosis of stage III colon cancer may be associated with decreased recurrence and improved survival. Clinical trials assessing the benefit of vitamin D supplementation in the adjuvant setting are warranted. NCT00003835. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  1. Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

    Science.gov (United States)

    Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G M; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M

    2014-01-01

    In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53) to 0.63 (95% CI, 0.60-0.66). The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

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

    Science.gov (United States)

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

    2016-01-01

    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.

  3. Integration of Multi-Modal Biomedical Data to Predict Cancer Grade and Patient Survival.

    Science.gov (United States)

    Phan, John H; Hoffman, Ryan; Kothari, Sonal; Wu, Po-Yen; Wang, May D

    2016-02-01

    The Big Data era in Biomedical research has resulted in large-cohort data repositories such as The Cancer Genome Atlas (TCGA). These repositories routinely contain hundreds of matched patient samples for genomic, proteomic, imaging, and clinical data modalities, enabling holistic and multi-modal integrative analysis of human disease. Using TCGA renal and ovarian cancer data, we conducted a novel investigation of multi-modal data integration by combining histopathological image and RNA-seq data. We compared the performances of two integrative prediction methods: majority vote and stacked generalization. Results indicate that integration of multiple data modalities improves prediction of cancer grade and outcome. Specifically, stacked generalization, a method that integrates multiple data modalities to produce a single prediction result, outperforms both single-data-modality prediction and majority vote. Moreover, stacked generalization reveals the contribution of each data modality (and specific features within each data modality) to the final prediction result and may provide biological insights to explain prediction performance.

  4. Usefulness of Clinical Prediction Rules, D-dimer, and Arterial Blood Gas Analysis to Predict Pulmonary Embolism in Cancer Patients

    Directory of Open Access Journals (Sweden)

    Shazia Awan

    2017-03-01

    Full Text Available Objectives: Pulmonary embolism (PE is seven times more common in cancer patients than non-cancer patients. Since the existing clinical prediction rules (CPRs were validated predominantly in a non-cancer population, we decided to look at the utility of arterial blood gas (ABG analysis and D-dimer in predicting PE in cancer patients. Methods: Electronic medical records were reviewed between December 2005 and November 2010. A total of 177 computed tomography pulmonary angiograms (CTPAs were performed. We selected 104 individuals based on completeness of laboratory and clinical data. Patients were divided into two groups, CTPA positive (patients with PE and CTPA negative (PE excluded. Wells score, Geneva score, and modified Geneva score were calculated for each patient. Primary outcomes of interest were the sensitivities, specificities, positive, and negative predictive values for all three CPRs. Results: Of the total of 104 individuals who had CTPAs, 33 (31.7% were positive for PE and 71 (68.3% were negative. There was no difference in basic demographics between the two groups. Laboratory parameters were compared and partial pressure of oxygen was significantly lower in patients with PE (68.1 mmHg vs. 71 mmHg, p = 0.030. Clinical prediction rules showed good sensitivities (88−100% and negative predictive values (93−100%. An alveolar-arterial (A-a gradient > 20 had 100% sensitivity and negative predictive values. Conclusions: CPRs and a low A-a gradient were useful in excluding PE in cancer patients. There is a need for prospective trials to validate these results.

  5. Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk.

    Science.gov (United States)

    Glynn, Robert J; Colditz, Graham A; Tamimi, Rulla M; Chen, Wendy Y; Hankinson, Susan E; Willett, Walter W; Rosner, Bernard

    2017-08-01

    A breast cancer risk prediction rule previously developed by Rosner and Colditz has reasonable predictive ability. We developed a re-fitted version of this model, based on more than twice as many cases now including women up to age 85, and further extended it to a model that distinguished risk factor prediction of tumors with different estrogen/progesterone receptor status. We compared the calibration and discriminatory ability of the original, the re-fitted, and the type-specific models. Evaluation used data from the Nurses' Health Study during the period 1980-2008, when 4384 incident invasive breast cancers occurred over 1.5 million person-years. Model development used two-thirds of study subjects and validation used one-third. Predicted risks in the validation sample from the original and re-fitted models were highly correlated (ρ = 0.93), but several parameters, notably those related to use of menopausal hormone therapy and age, had different estimates. The re-fitted model was well-calibrated and had an overall C-statistic of 0.65. The extended, type-specific model identified several risk factors with varying associations with occurrence of tumors of different receptor status. However, this extended model relative to the prediction of any breast cancer did not meaningfully reclassify women who developed breast cancer to higher risk categories, nor women remaining cancer free to lower risk categories. The re-fitted Rosner-Colditz model has applicability to risk prediction in women up to age 85, and its discrimination is not improved by consideration of varying associations across tumor subtypes.

  6. 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.

  7. Nonvisible tumors on multiparametric magnetic resonance imaging does not predict low-risk prostate cancer

    Directory of Open Access Journals (Sweden)

    Seung Hwan Lee

    2015-12-01

    Conclusions: Even though cancer foci were not visualized by postbiopsy MRI, the pathological tumor volumes and extent of GS upgrading were relatively high. Therefore, nonvisible tumors by multiparametric MRI do not appear to be predictive of low-risk PCA.

  8. 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...

  9. 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.

  10. Predicting the Toxicity of Adjuvant Breast Cancer Drug Combination Therapy

    Science.gov (United States)

    2013-03-01

    Neratinib Versus Lapatinib Plus Capecitabine For ErbB2 Positive Advanced Breast Cancer Active, not recruiting No Results Available YES neratinib -9...Drug: Neratinib |Drug: Lapatinib|Drug: Capecitabine Efficacy and Safety of BMS-690514 in Combination With Letrozole to Treat Metastatic Breast Cancer

  11. 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…

  12. Predictive factors of unfavorable prostate cancer in patients who underwent prostatectomy but eligible for active surveillance

    Directory of Open Access Journals (Sweden)

    Seol Ho Choo

    2014-06-01

    Conclusions: A significant proportion of patients who were candidates for active surveillance had unfavorable prostate cancer. Age, PSA density, and two positive cores were independent significant predictive factors for unfavorable prostate cancer. These factors should be considered when performing active surveillance.

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

    DEFF Research Database (Denmark)

    Espersen, Maiken Lise Marcker; Linnemann, Dorte; Christensen, Ib Jarle

    2016-01-01

    The aim 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...

  14. Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals

    DEFF Research Database (Denmark)

    Smith, Todd; Muller, David C; Moons, Karel G M

    2018-01-01

    in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted......-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained....

  15. Update on breast cancer risk prediction and prevention.

    Science.gov (United States)

    Sestak, Ivana; Cuzick, Jack

    2015-02-01

    Breast cancer is the most common cancer in women worldwide. This review will focus on current prevention strategies for women at high risk. The identification of women who are at high risk of developing breast cancer is key to breast cancer prevention. Recent findings have shown that the inclusion of breast density and a panel of low-penetrance genetic polymorphisms can improve risk estimation compared with previous models. Preventive therapy with aromatase inhibitors has produced large reductions in breast cancer incidence in postmenopausal women. Tamoxifen confers long-term protection and is the only proven preventive treatment for premenopausal women. Several other agents, including metformin, bisphosphonates, aspirin and statins, have been found to be effective in nonrandomized settings. There are many options for the prevention of oestrogen-positive breast cancer, in postmenopausal women who can be given a selective oestrogen receptor modulator or an aromatase inhibitor. It still remains unclear how to prevent oestrogen-negative breast cancer, which occurs more often in premenopausal women. Identification of women at high risk of the disease is crucial, and the inclusion of breast density and a panel of genetic polymorphisms, which individually have low penetrance, can improve risk assessment.

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

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  17. 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.

  18. Prediction of rehabilitation needs after treatment of cervical cancer

    DEFF Research Database (Denmark)

    Mikkelsen, Tina Broby; Sørensen, Bente; Dieperink, Karin B

    2017-01-01

    PURPOSE: Women treated for cervical cancer with radiotherapy and chemotherapy have reported serious bowel, vaginal, and sexual late effects. The purpose of this study was to describe late adverse effects, health-related quality of life, and self-efficacy in a representative Danish cervical cancer...... population in order to describe rehabilitation needs. METHODS: Women, mean age 55 years, treated for cervical cancer from January 2010 to July 2013, who were alive and without known relapse/metastases were included in this cross-sectional study. EORTC QLQ C30 and CX24 and self-efficacy questionnaires were......: This study found that young, obese survivors with locally advanced cervical cancer and survivors who received chemotherapy may have a serious risk of developing late adverse effects; thus, rehabilitation should target these needs....

  19. 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)

    Farkas, J.; Andrassy, E.; Meszaros, L.; Beczner, J.; Polyak-Feher, K.; Gaal, O.; Lebovics, V.K.; Lugasi, A.

    2009-01-01

    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)

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

    International Nuclear Information System (INIS)

    Greenham, Stuart; Dean, Jenna; Fu, Cheuk Kuen Kenneth; Goman, Joanne; Mulligan, Jeremy; Tune, Deanna; Sampson, David; Westhuyzen, Justin; McKay, Michael

    2014-01-01

    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

  1. A deep learning-based multi-model ensemble method for cancer prediction.

    Science.gov (United States)

    Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

    2018-01-01

    Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Applying a new mammographic imaging marker to predict breast cancer risk

    Science.gov (United States)

    Aghaei, Faranak; Danala, Gopichandh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

    2018-02-01

    Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the "current" and "prior" screenings with a time interval from 365 to 600 days. All "prior" images were originally interpreted negative. In "current" screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p cancer detection in the "current" screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

  3. Utilizing Data Mining for Predictive Modeling of Colorectal Cancer using Electronic Medical Records

    NARCIS (Netherlands)

    Hoogendoorn, M.; Moons, L.G.; Numans, M.E.; Sips, R.J.

    2014-01-01

    Colorectal cancer (CRC) is a relatively common cause of death around the globe. Predictive models for the development of CRC could be highly valuable and could facilitate an early diagnosis and increased survival rates. Currently available predictive models are improving, but do not fully utilize

  4. FERAL : Network-based classifier with application to breast cancer outcome prediction

    NARCIS (Netherlands)

    Allahyar, A.; De Ridder, J.

    2015-01-01

    Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed.

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

    DEFF Research Database (Denmark)

    Hagmar, L; Bonassi, S; Strömberg, U

    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......, occupational exposures and smoking, will be assessed in a case-referent study within the study base....

  6. Predicting reattendance at a high-risk breast cancer clinic.

    Science.gov (United States)

    Ormseth, Sarah R; Wellisch, David K; Aréchiga, Adam E; Draper, Taylor L

    2015-10-01

    The research about follow-up patterns of women attending high-risk breast-cancer clinics is sparse. This study sought to profile daughters of breast-cancer patients who are likely to return versus those unlikely to return for follow-up care in a high-risk clinic. Our investigation included 131 patients attending the UCLA Revlon Breast Center High Risk Clinic. Predictor variables included age, computed breast-cancer risk, participants' perceived personal risk, clinically significant depressive symptomatology (CES-D score ≥ 16), current level of anxiety (State-Trait Anxiety Inventory), and survival status of participants' mothers (survived or passed away from breast cancer). A greater likelihood of reattendance was associated with older age (adjusted odds ratio [AOR] = 1.07, p = 0.004), computed breast-cancer risk (AOR = 1.10, p = 0.017), absence of depressive symptomatology (AOR = 0.25, p = 0.009), past psychiatric diagnosis (AOR = 3.14, p = 0.029), and maternal loss to breast cancer (AOR = 2.59, p = 0.034). Also, an interaction was found between mother's survival and perceived risk (p = 0.019), such that reattendance was associated with higher perceived risk among participants whose mothers survived (AOR = 1.04, p = 0.002), but not those whose mothers died (AOR = 0.99, p = 0.685). Furthermore, a nonlinear inverted "U" relationship was observed between state anxiety and reattendance (p = 0.037); participants with moderate anxiety were more likely to reattend than those with low or high anxiety levels. Demographic, medical, and psychosocial factors were found to be independently associated with reattendance to a high-risk breast-cancer clinic. Explication of the profiles of women who may or may not reattend may serve to inform the development and implementation of interventions to increase the likelihood of follow-up care.

  7. Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry.

    Science.gov (United States)

    Wen, Wanqing; Shu, Xiao-Ou; Guo, Xingyi; Cai, Qiuyin; Long, Jirong; Bolla, Manjeet K; Michailidou, Kyriaki; Dennis, Joe; Wang, Qin; Gao, Yu-Tang; Zheng, Ying; Dunning, Alison M; García-Closas, Montserrat; Brennan, Paul; Chen, Shou-Tung; Choi, Ji-Yeob; Hartman, Mikael; Ito, Hidemi; Lophatananon, Artitaya; Matsuo, Keitaro; Miao, Hui; Muir, Kenneth; Sangrajrang, Suleeporn; Shen, Chen-Yang; Teo, Soo H; Tseng, Chiu-Chen; Wu, Anna H; Yip, Cheng Har; Simard, Jacques; Pharoah, Paul D P; Hall, Per; Kang, Daehee; Xiang, Yongbing; Easton, Douglas F; Zheng, Wei

    2016-12-08

    Approximately 100 common breast cancer susceptibility alleles have been identified in genome-wide association studies (GWAS). The utility of these variants in breast cancer risk prediction models has not been evaluated adequately in women of Asian ancestry. We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk. We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P women in the middle quintile of the PRS, women in the top 1% group had a 2.70-fold elevated risk of breast cancer (95% CI: 2.15-3.40). The risk prediction model with the PRS had an area under the receiver operating characteristic curve of 0.606. The lifetime risk of breast cancer for Shanghai Chinese women in the lowest and highest 1% of the PRS was 1.35% and 10.06%, respectively. Approximately one-half of GWAS-identified breast cancer risk variants can be directly replicated in East Asian women. Collectively, common genetic variants are important predictors for breast cancer risk. Using common genetic variants for breast cancer could help identify women at high risk of breast cancer.

  8. Predictive Factors and Treatment Outcomes of Tuberculous Pleural Effusion in Patients With Cancer and Pleural Effusion.

    Science.gov (United States)

    Lee, Jaehee; Lee, Yong Dae; Lim, Jae Kwang; Lee, Deok Heon; Yoo, Seung Soo; Lee, Shin Yup; Cha, Seung Ick; Park, Jae Yong; Kim, Chang Ho

    2017-08-01

    Patients with cancer are at an increased risk of tuberculosis. As pleural effusion has great clinical significance in patients with cancer, the differential diagnosis between tuberculous pleural effusion (TPE) and malignant pleural effusion (MPE) is important. However, the predictive factors and treatment outcomes of TPE in patients with cancer have rarely been studied. Confirmed TPE cases identified at cancer diagnosis and during anticancer management from 2008-2015 were retrospectively investigated. Patients in the study included coexisting TPE and cancer (n = 20), MPE (n = 40) and TPE without cancer (n = 40). Control groups were patients with MPE, and patients with TPE without cancer. Clinical, laboratory and pleural fluid characteristics were compared among groups. Treatment outcomes were compared between patients with TPE with and without cancer. In the final analysis, serum C-reactive protein (S-CRP) ≥3.0mg/dL and pleural fluid adenosine deaminase (ADA) ≥40U/L were independent predictors for identifying TPE in patients with cancer having pleural effusion. The combination of S-CRP with pleural fluid ADA using an "or" rule achieved a sensitivity of 100%, whereas both parameters combined in an "and" rule had a specificity of 98%. Treatment outcomes were not different between the TPE groups with and without cancer. S-CRP and pleural fluid ADA levels may be helpful for predicting TPE in patients with cancer with pleural effusion. The combination of these biomarkers provides better information for distinguishing between TPE and MPE in these patients. Treatment outcomes of TPE in patients with cancer are comparable to those in patients without cancer. Copyright © 2017 Southern Society for Clinical Investigation. Published by Elsevier Inc. All rights reserved.

  9. Nonlinear joint models for individual dynamic prediction of risk of death using Hamiltonian Monte Carlo: application to metastatic prostate cancer

    Directory of Open Access Journals (Sweden)

    Solène Desmée

    2017-07-01

    Full Text Available Abstract Background Joint models of longitudinal and time-to-event data are increasingly used to perform individual dynamic prediction of a risk of event. However the difficulty to perform inference in nonlinear models and to calculate the distribution of individual parameters has long limited this approach to linear mixed-effect models for the longitudinal part. Here we use a Bayesian algorithm and a nonlinear joint model to calculate individual dynamic predictions. We apply this approach to predict the risk of death in metastatic castration-resistant prostate cancer (mCRPC patients with frequent Prostate-Specific Antigen (PSA measurements. Methods A joint model is built using a large population of 400 mCRPC patients where PSA kinetics is described by a biexponential function and the hazard function is a PSA-dependent function. Using Hamiltonian Monte Carlo algorithm implemented in Stan software and the estimated population parameters in this population as priors, the a posteriori distribution of the hazard function is computed for a new patient knowing his PSA measurements until a given landmark time. Time-dependent area under the ROC curve (AUC and Brier score are derived to assess discrimination and calibration of the model predictions, first on 200 simulated patients and then on 196 real patients that are not included to build the model. Results Satisfying coverage probabilities of Monte Carlo prediction intervals are obtained for longitudinal and hazard functions. Individual dynamic predictions provide good predictive performances for landmark times larger than 12 months and horizon time of up to 18 months for both simulated and real data. Conclusions As nonlinear joint models can characterize the kinetics of biomarkers and their link with a time-to-event, this approach could be useful to improve patient’s follow-up and the early detection of most at risk patients.

  10. Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

    Science.gov (United States)

    Wang, Shengfeng; Ogundiran, Temidayo O; Ademola, Adeyinka; Oluwasola, Olayiwola A; Adeoye, Adewunmi O; Sofoluwe, Adenike; Morhason-Bello, Imran; Odedina, Stella O; Agwai, Imaria; Adebamowo, Clement; Obajimi, Millicent; Ojengbede, Oladosu; Olopade, Olufunmilayo I; Huo, Dezheng

    2018-04-20

    Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aim to develop a model for absolute breast cancer risk prediction for Nigerian women. A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998~2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model. The NBCS model included age, age at menarche, parity, duration of breast feeding, family history of breast cancer, height, body mass index, benign breast diseases and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model (area under ROC curve [AUC]=0.703, 95% confidence interval [CI]: 0.687-0.719) was better than the Black Women's Health Study (BWHS) model (AUC=0.605, 95% CI: 0.586-0.624), Gail model for White population (AUC=0.551, 95% CI: 0.531-0.571), and Gail model for Black population (AUC=0.545, 95% CI: 0.525-0.565). Compared to the BWHS, two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45% and 14.19%, respectively. We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in SSA populations. Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high-risk for breast cancer screening. Copyright ©2018, American Association for Cancer Research.

  11. 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.

  12. Quantifying predictive capability of electronic health records for the most harmful breast cancer

    Science.gov (United States)

    Wu, Yirong; Fan, Jun; Peissig, Peggy; Berg, Richard; Tafti, Ahmad Pahlavan; Yin, Jie; Yuan, Ming; Page, David; Cox, Jennifer; Burnside, Elizabeth S.

    2018-03-01

    Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction. We identified 794 subjects who had breast cancer with primary non-benign tumors with their earliest diagnosis on or after 1/1/2004 from an existing personalized medicine data repository, including 395 "most harmful" breast cancer cases and 399 "least harmful" breast cancer cases. For these subjects, we collected EHR data comprised of 6 components: demographics, diagnoses, symptoms, procedures, medications, and laboratory results. We developed two regularized prediction models, Ridge Logistic Regression (Ridge-LR) and Lasso Logistic Regression (Lasso-LR), to predict the "most harmful" breast cancer one year in advance. The area under the ROC curve (AUC) was used to assess model performance. We observed that the AUCs of Ridge-LR and Lasso-LR models were 0.818 and 0.839 respectively. For both the Ridge-LR and LassoLR models, the predictive performance of the whole EHR variables was significantly higher than that of each individual component (pbreast cancer, providing the possibility to personalize care for those women at the highest risk in clinical practice.

  13. Predicting physical activity and outcome expectations in cancer survivors: an application of Self-Determination Theory.

    Science.gov (United States)

    Wilson, Philip M; Blanchard, Chris M; Nehl, Eric; Baker, Frank

    2006-07-01

    The purpose of this study was to examine the contributions of autonomous and controlled motives drawn from Self-Determination Theory (SDT; Intrinsic Motivation and Self-determination in Human Behavior. Plenum Press: New York, 1985; Handbook of Self-determination Research. University of Rochester Press: New York, 2002) towards predicting physical activity behaviours and outcome expectations in adult cancer survivors. Participants were cancer-survivors (N=220) and a non-cancer comparison cohort (N=220) who completed an adapted version of the Treatment Self-Regulation Questionnaire modified for physical activity behaviour (TSRQ-PA), an assessment of the number of minutes engaged in moderate-to-vigorous physical activity (MVPA) weekly, and the anticipated outcomes expected from regular physical activity (OE). Simultaneous multiple regression analyses indicated that autonomous motives was the dominant predictor of OEs across both cancer and non-cancer cohorts (R(2adj)=0.29-0.43), while MVPA was predicted by autonomous (beta's ranged from 0.21 to 0.34) and controlled (beta's ranged from -0.04 to -0.23) motives after controlling for demographic considerations. Cancer status (cancer versus no cancer) did not moderate the motivation-physical activity relationship. Collectively, these findings suggest that the distinction between autonomous and controlled motives is useful and compliments a growing body of evidence supporting SDT as a framework for understanding motivational processes in physical activity contexts with cancer survivors.

  14. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    International Nuclear Information System (INIS)

    Hao, Ke; Zhong, Hua; Greenawalt, Danielle; Ferguson, Mark D; Ng, Irene O; Sham, Pak C; Poon, Ronnie T; Molony, Cliona; Schadt, Eric E; Dai, Hongyue; Luk, John M; Lamb, John; Zhang, Chunsheng; Xie, Tao; Wang, Kai; Zhang, Bin; Chudin, Eugene; Lee, Nikki P; Mao, Mao

    2011-01-01

    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

  15. Risk prediction model for colorectal cancer: National Health Insurance Corporation study, Korea.

    Directory of Open Access Journals (Sweden)

    Aesun Shin

    Full Text Available 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 population of 846,559 men and 479,449 women who participated in health examinations by the National Health Insurance Corporation. Examinees were 30-80 years old and free of cancer in the baseline years of 1996 and 1997. An independent population of 547,874 men and 415,875 women who participated in 1998 and 1999 examinations was used to validate the model. Model validation was done by evaluating its performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow-type chi-square statistics. RESULTS: Age, body mass index, serum cholesterol, family history of cancer, and alcohol consumption were included in all models for men, whereas age, height, and meat intake frequency were included in all models for women. Models showed moderately good discrimination ability with C-statistics between 0.69 and 0.78. The C-statistics were generally higher in the models for men, whereas the calibration abilities were generally better in the models for women. CONCLUSIONS: Colorectal cancer risk prediction models were developed from large-scale, population-based data. Those models can be used for identifying high risk groups and developing preventive intervention strategies for colorectal cancer.

  16. Risk prediction model for colorectal cancer: National Health Insurance Corporation study, Korea.

    Science.gov (United States)

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

    2014-01-01

    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. 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 population of 846,559 men and 479,449 women who participated in health examinations by the National Health Insurance Corporation. Examinees were 30-80 years old and free of cancer in the baseline years of 1996 and 1997. An independent population of 547,874 men and 415,875 women who participated in 1998 and 1999 examinations was used to validate the model. Model validation was done by evaluating its performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow-type chi-square statistics. Age, body mass index, serum cholesterol, family history of cancer, and alcohol consumption were included in all models for men, whereas age, height, and meat intake frequency were included in all models for women. Models showed moderately good discrimination ability with C-statistics between 0.69 and 0.78. The C-statistics were generally higher in the models for men, whereas the calibration abilities were generally better in the models for women. Colorectal cancer risk prediction models were developed from large-scale, population-based data. Those models can be used for identifying high risk groups and developing preventive intervention strategies for colorectal cancer.

  17. Predicting, preventing and managing persistent pain after breast cancer surgery:

    DEFF Research Database (Denmark)

    Schreiber, Kristin L; Kehlet, Henrik; Belfer, Inna

    2014-01-01

    Persistent pain after breast cancer surgery (PPBCS) is increasingly recognized as a potential problem facing a sizeable subset of the millions of women who undergo surgery as part of their treatment of breast cancer. Importantly, an increasing number of studies suggest that individual variation...... in psychosocial factors such as catastrophizing, anxiety, depression, somatization and sleep quality play an important role in shaping an individual's risk of developing PPBCS. This review presents evidence for the importance of these factors and puts them within the context of other surgical, medical...

  18. Prediction of Aggressive Human Prostate Cancer by Cathepsin B

    Science.gov (United States)

    2008-03-01

    Cancer Res 2004;10(12 Pt 1):4118-4124. 28. Munoz E, Gomez F, Paz JI, Casado I, Silva JM, Corcuera MT, Alonso MJ. Ki-67 immunolabeling in pre...detected prostate cancer. J Pathol 2002;197(2):148-154. 34. Claudio PP, Zamparelli A, Garcia FU, Claudio L, Ammirati G, Farina A, Bovicelli A, Russo G...JA. Distinct roles for cysteine cathepsin genes in multistage tumorigenesis. Genes Dev 2006;20(5):543-556. 47. Fernandez PL, Farre X, Nadal A

  19. An etiologic prediction model incorporating biomarkers to predict the bladder cancer risk associated with occupational exposure to aromatic amines: a pilot study

    OpenAIRE

    Mastrangelo, Giuseppe; Carta, Angela; Arici, Cecilia; Pavanello, Sofia; Porru, Stefano

    2017-01-01

    Background No etiological prediction model incorporating biomarkers is available to predict bladder cancer risk associated with occupational exposure to aromatic amines. Methods Cases were 199 bladder cancer patients. Clinical, laboratory and genetic data were predictors in logistic regression models (full and short) in which the dependent variable was 1 for 15 patients with aromatic amines related bladder cancer and 0 otherwise. The receiver operating characteristics approach was adopted; th...

  20. 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.

  1. Quantitative prediction of oral cancer risk in patients with oral leukoplakia.

    Science.gov (United States)

    Liu, Yao; Li, Yicheng; Fu, Yue; Liu, Tong; Liu, Xiaoyong; Zhang, Xinyan; Fu, Jie; Guan, Xiaobing; Chen, Tong; Chen, Xiaoxin; Sun, Zheng

    2017-07-11

    Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma. We have developed an oral cancer risk index using DNA index value to quantitatively assess cancer risk in patients with oral leukoplakia, but with limited success. In order to improve the performance of the risk index, we collected exfoliative cytology, histopathology, and clinical follow-up data from two independent cohorts of normal, leukoplakia and cancer subjects (training set and validation set). Peaks were defined on the basis of first derivatives with positives, and modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Random forest was found to be the best model with high sensitivity (100%) and specificity (99.2%). Using the Peaks-Random Forest model, we constructed an index (OCRI2) as a quantitative measurement of cancer risk. Among 11 leukoplakia patients with an OCRI2 over 0.5, 4 (36.4%) developed cancer during follow-up (23 ± 20 months), whereas 3 (5.3%) of 57 leukoplakia patients with an OCRI2 less than 0.5 developed cancer (32 ± 31 months). OCRI2 is better than other methods in predicting oral squamous cell carcinoma during follow-up. In conclusion, we have developed an exfoliative cytology-based method for quantitative prediction of cancer risk in patients with oral leukoplakia.

  2. Cognitive and social processes predicting partner psychological adaptation to early stage breast cancer.

    Science.gov (United States)

    Manne, Sharon; Ostroff, Jamie; Fox, Kevin; Grana, Generosa; Winkel, Gary

    2009-02-01

    The diagnosis and subsequent treatment for early stage breast cancer is stressful for partners. Little is known about the role of cognitive and social processes predicting the longitudinal course of partners' psychosocial adaptation. This study evaluated the role of cognitive and social processing in partner psychological adaptation to early stage breast cancer, evaluating both main and moderator effect models. Moderating effects for meaning making, acceptance, and positive reappraisal on the predictive association of searching for meaning, emotional processing, and emotional expression on partner psychological distress were examined. Partners of women diagnosed with early stage breast cancer were evaluated shortly after the ill partner's diagnosis (N=253), 9 (N=167), and 18 months (N=149) later. Partners completed measures of emotional expression, emotional processing, acceptance, meaning making, and general and cancer-specific distress at all time points. Lower satisfaction with partner support predicted greater global distress, and greater use of positive reappraisal was associated with greater distress. The predicted moderator effects for found meaning on the associations between the search for meaning and cancer-specific distress were found and similar moderating effects for positive reappraisal on the associations between emotional expression and global distress and for acceptance on the association between emotional processing and cancer-specific distress were found. Results indicate several cognitive-social processes directly predict partner distress. However, moderator effect models in which the effects of partners' processing depends upon whether these efforts result in changes in perceptions of the cancer experience may add to the understanding of partners' adaptation to cancer.

  3. Smoking status predicts cancer patients' quality of life over time

    Directory of Open Access Journals (Sweden)

    Ursula Martinez

    2018-03-01

    These results extend previous findings showing that QOL improves in cancer patients who quit smoking. Specifically, patients who quit smoking experience a greater reduction in depression and pain levels at all time points, and the reduction increases over time. In the case of fatigue, the results suggest that patients experience the greatest improvement with longer (≥ 4 months abstinence.

  4. Genetically Predicted Body Mass Index and Breast Cancer Risk

    DEFF Research Database (Denmark)

    Guo, Yan; Warren Andersen, Shaneda; Shu, Xiao-Ou

    2016-01-01

    BACKGROUND: Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or enviro...

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

    DEFF Research Database (Denmark)

    Josa, Valeria; Krzystanek, Marcin; Vass, Tamas

    2015-01-01

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

  6. Validation of an online risk calculator for the prediction of anastomotic leak after colon cancer surgery and preliminary exploration of artificial intelligence-based analytics.

    Science.gov (United States)

    Sammour, T; Cohen, L; Karunatillake, A I; Lewis, M; Lawrence, M J; Hunter, A; Moore, J W; Thomas, M L

    2017-11-01

    Recently published data support the use of a web-based risk calculator ( www.anastomoticleak.com ) for the prediction of anastomotic leak after colectomy. The aim of this study was to externally validate this calculator on a larger dataset. Consecutive adult patients undergoing elective or emergency colectomy for colon cancer at a single institution over a 9-year period were identified using the Binational Colorectal Cancer Audit database. Patients with a rectosigmoid cancer, an R2 resection, or a diverting ostomy were excluded. The primary outcome was anastomotic leak within 90 days as defined by previously published criteria. Area under receiver operating characteristic curve (AUROC) was derived and compared with that of the American College of Surgeons National Surgical Quality Improvement Program ® (ACS NSQIP) calculator and the colon leakage score (CLS) calculator for left colectomy. Commercially available artificial intelligence-based analytics software was used to further interrogate the prediction algorithm. A total of 626 patients were identified. Four hundred and fifty-six patients met the inclusion criteria, and 402 had complete data available for all the calculator variables (126 had a left colectomy). Laparoscopic surgery was performed in 39.6% and emergency surgery in 14.7%. The anastomotic leak rate was 7.2%, with 31.0% requiring reoperation. The anastomoticleak.com calculator was significantly predictive of leak and performed better than the ACS NSQIP calculator (AUROC 0.73 vs 0.58) and the CLS calculator (AUROC 0.96 vs 0.80) for left colectomy. Artificial intelligence-predictive analysis supported these findings and identified an improved prediction model. The anastomotic leak risk calculator is significantly predictive of anastomotic leak after colon cancer resection. Wider investigation of artificial intelligence-based analytics for risk prediction is warranted.

  7. Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

    Directory of Open Access Journals (Sweden)

    Hui Miao

    Full Text Available BACKGROUND: In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. MATERIALS AND METHODS: We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic. RESULTS: We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53 to 0.63 (95% CI, 0.60-0.66. CONCLUSION: The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

  8. Prognostic and predictive potential molecular biomarkers in colon cancer.

    Science.gov (United States)

    Nastase, A; Pâslaru, L; Niculescu, A M; Ionescu, M; Dumitraşcu, T; Herlea, V; Dima, S; Gheorghe, C; Lazar, V; Popescu, I

    2011-01-01

    An important objective in nowadays research is the discovery of new biomarkers that can detect colon tumours in early stages and indicate with accuracy the status of the disease. The aim of our study was to identify potential biomarkers for colon cancer onset and progression. We assessed gene expression profiles of a list of 10 candidate genes (MMP-1, MMP-3, MMP-7, DEFA 1, DEFA-5, DEFA-6, IL-8, CXCL-1, SPP-1, CTHRC-1) by quantitative real time PCR in triplets of colonic mucosa (normal, adenoma, tumoral tissue) collected from the same patient during surgery for a group of 20 patients. Additionally we performed immunohistochemistry for DEFA1-3 and SPP1. We remarked that DEFA5 and DEFA6 are key factors in adenoma formation (p<0.05). MMP7 is important in the transition from a benign to a malignant status (p <0.01) and further in metastasis being a prognostic indicator for tumor transformation and for the metastatic potential of cancer cells. IL8, irrespective of tumor stage, has a high mRNA level in adenocarcinoma (p< 0.05). The level of expression for SPP1 is correlated with tumor level. We suggest that high levels of DEFAS, DEFA6 (key elements in adenoma formation), MMP7 (marker of colon cancer onset and progression to metastasis), SPP1 (marker of progression) and IL8 could be used to diagnose an early stage colon cancer and to evaluate the prognostic of progression for colon tumors. Further, if DEFA5 and DEFA6 level of expression are low but MMP7, SPP1 and IL8 level are high we could point out that the transition from adenoma to adenocarcinoma had already occurred. Thus, DEFA5, DEFA6, MMP7, IL8 and SPP1 consist in a valuable panel of biomarkers, whose detection can be used in early detection and progressive disease and also in prognostic of colon cancer.

  9. Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models

    Science.gov (United States)

    Nilsaz-Dezfouli, Hamid; Abu-Bakar, Mohd Rizam; Arasan, Jayanthi; Adam, Mohd Bakri; Pourhoseingholi, Mohamad Amin

    2017-01-01

    In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. PMID:28469384

  10. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

    Science.gov (United States)

    Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

    2018-05-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

  11. Cancer-related symptoms predict psychological wellbeing among prostate cancer survivors: results from the PiCTure study.

    Science.gov (United States)

    Sharp, Linda; O'Leary, Eamonn; Kinnear, Heather; Gavin, Anna; Drummond, Frances J

    2016-03-01

    Prostate cancer treatments are associated with a range of symptoms and physical side-effects. Cancer can also adversely impact on psychological wellbeing. Because many prostate cancer-related symptoms and side-effects are potentially modifiable, we investigated associations between symptoms and psychological wellbeing among prostate cancer survivors. Postal questionnaires were distributed to men diagnosed with prostate cancer 2-18 years previously identified through cancer registries. General and prostate cancer-specific symptoms were assessed using the EORTC QLQ-C30 and QLQ-PR25, with higher symptom scores indicating more/worse symptomatology. Psychological wellbeing was assessed by the DASS-21. Associations between symptoms and each outcome were investigated using multivariate logistic regression, controlling for socio-demographic and clinical factors. A total 3348 men participated (response rate = 54%). Seventeen percent (95%CI 15.2%-17.9%), 16% (95%CI 15.1%-17.8%) and 11% (95%CI 9.5%-11.8%) of survivors scored in the range for depression, anxiety and distress on the DASS scales, respectively. In multivariate models, risk of depression on the DASS scale was significantly higher in men with higher urinary and androgen deprivation therapy (ADT)-related symptoms, and higher scores for fatigue, insomnia and financial difficulties. Risk of anxiety on the DASS scale was higher in men with higher scores for urinary, bowel and ADT-related symptoms and fatigue, dyspnoea and financial difficulties. Risk of distress on the DASS scale was positively associated with urinary, bowel and ADT-related symptoms, fatigue, insomnia and financial difficulties. Cancer-related symptoms significantly predict psychological wellbeing among prostate cancer survivors. Greater use of interventions and medications and to alleviate symptoms might improve psychological wellbeing of prostate cancer survivors. Copyright © 2015 John Wiley & Sons, Ltd.

  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.

  13. A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound.

    Science.gov (United States)

    Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J

    2017-04-12

    Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival.

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

    Science.gov (United States)

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

    2008-01-01

    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 clinical information for 1978 patients treated by two high-volume surgeons from our institution. The nomogram was externally validated on an independent cohort of 1545 patients with a concordance index of 0.79 and was well calibrated with respect to observed outcome. The inclusion of the number of positive and negative biopsy cores enhanced the predictive accuracy of the model. Thus, a new preoperative nomogram provides robust predictions of prostate cancer recurrence up to 10 years after radical prostatectomy. PMID:16705126

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

    Science.gov (United States)

    Bodrato, Marco; Vione, Davide

    2014-04-01

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

  16. Evaluation of atlas-based autosegmentation with ABAS software for head-and-neck cancer

    International Nuclear Information System (INIS)

    Zhang Xiuchun; Hu Cairong; Chen Chuanben; Cai Yongjun

    2011-01-01

    Objective: To evaluate the autocontouring accuracy using the atlas-based autosegmentation of CT images for head-and-neck cancer. Methods: Ten head and neck patients with contours were selected. Two groups of autocontouring atlas were tested, the first group was for patients with own atlas, for the second group we tested the autocontouring of eight patients with other two patients atlas. Dice similarity coefficient (DSC) and overlap index (OI) were introduced to evaluate the autocontours, and the discrepancy between the two groups was evaluated through paired t-test. Results: Both the DSC and OI of all the organs in the first group were >0.80, the result of mandible was the highest (>0.91), the DSC of the gross tumor volume (GTV) was the lowest (0.81), the OI of the GTV was 0.82, and the DSC and OI of the clinical target volume (node) were 0.82 and 0, 79, respectively. Only the risk organ was delineated in the second group, and spinal cord and brain stem were combined to analyze. All the DSC was about 0.70, and the DSC and OI of mandible were higher than the others, which was due to its bone anatomy. The accuracy in the second group was significantly lower than that of the first group (t =3.24 - 8.26, P =0.014 -0.000), except the right parotid (t=2.08, P=0.075). Conclusions: Automatic segmentation generates contours of sufficient accuracy for adaptive planning intensity-modulated radiotherapy (IMRT) to accommodate anatomic changes during treatment. For convention planning IMRT normal structure auto-contouring,it need to select more standard atlas in order to acquire a satisfied autocontours. (authors)

  17. Role of alexithymia in predicting psychological symptoms in patients with breast and prostate cancer

    Directory of Open Access Journals (Sweden)

    M. Mowlaie

    2015-12-01

    Full Text Available Background: Identifying the psychological factors involved in psychological problems of patients with cancer is very important. Objective: The aim of this study was to determine the role of alexithymia in predicting psychological symptoms in patients with cancer. Methods: This cross sectional study was conducted in 102 patients with cancer selected by convenience sampling method in Ardabil during 2014. The measurement tools were the Persian version of Toronto Alexithymia Scale (TAS-20 and the Hopkins Symptom Checklist-25 (SCL-25. Data were analyzed using Pearson's correlation coefficient and regression analysis. Findings: There was significantly positive correlation between alexithymia and all psychological symptoms. In regression analysis, alexithymia was predictor of all psychological symptoms. Conclusion: With regards to the results, it seems that alexithymia is able to predict psychological symptoms. Therefore, paying more attention to psychological determinants in patients with cancer and providing appropriate treatment strategies can be effective to alleviate the mental suffering.

  18. A novel industry grade dataset for fault prediction based on model-driven developed automotive embedded software

    NARCIS (Netherlands)

    Altinger, H.; Siegl, S.; Dajsuren, Y.; Wotawa, F.

    2015-01-01

    In this paper, we present a novel industry dataset on static software and change metrics for Matlab/Simulink models and their corresponding auto-generated C source code. The data set comprises data of three automotive projects developed and tested accordingly to industry standards and restrictive

  19. Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error

    OpenAIRE

    Shipitsin, M; Small, C; Choudhury, S; Giladi, E; Friedlander, S; Nardone, J; Hussain, S; Hurley, A D; Ernst, C; Huang, Y E; Chang, H; Nifong, T P; Rimm, D L; Dunyak, J; Loda, M

    2014-01-01

    Background: Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation. Methods: Prostatectom...

  20. Hopefulness predicts resilience after hereditary colorectal cancer genetic testing: a prospective outcome trajectories study

    OpenAIRE

    Chu Annie TW; Bonanno George A; Ho Judy WC; Ho Samuel MY; Chan Emily MS

    2010-01-01

    Abstract Background - Genetic testing for hereditary colorectal cancer (HCRC) had significant psychological consequences for test recipients. This prospective longitudinal study investigated the factors that predict psychological resilience in adults undergoing genetic testing for HCRC. Methods - A longitudinal study was carried out from April 2003 to August 2006 on Hong Kong Chinese HCRC family members who were recruited and offered genetic testing by the Hereditary Gastrointestinal Cancer R...

  1. Predicted cancer risks induced by computed tomography examinations during childhood, by a quantitative risk assessment approach.

    Science.gov (United States)

    Journy, Neige; Ancelet, Sophie; Rehel, Jean-Luc; Mezzarobba, Myriam; Aubert, Bernard; Laurier, Dominique; Bernier, Marie-Odile

    2014-03-01

    The potential adverse effects associated with exposure to ionizing radiation from computed tomography (CT) in pediatrics must be characterized in relation to their expected clinical benefits. Additional epidemiological data are, however, still awaited for providing a lifelong overview of potential cancer risks. This paper gives predictions of potential lifetime risks of cancer incidence that would be induced by CT examinations during childhood in French routine practices in pediatrics. Organ doses were estimated from standard radiological protocols in 15 hospitals. Excess risks of leukemia, brain/central nervous system, breast and thyroid cancers were predicted from dose-response models estimated in the Japanese atomic bomb survivors' dataset and studies of medical exposures. Uncertainty in predictions was quantified using Monte Carlo simulations. This approach predicts that 100,000 skull/brain scans in 5-year-old children would result in eight (90 % uncertainty interval (UI) 1-55) brain/CNS cancers and four (90 % UI 1-14) cases of leukemia and that 100,000 chest scans would lead to 31 (90 % UI 9-101) thyroid cancers, 55 (90 % UI 20-158) breast cancers, and one (90 % UI risks without exposure). Compared to background risks, radiation-induced risks would be low for individuals throughout life, but relative risks would be highest in the first decades of life. Heterogeneity in the radiological protocols across the hospitals implies that 5-10 % of CT examinations would be related to risks 1.4-3.6 times higher than those for the median doses. Overall excess relative risks in exposed populations would be 1-10 % depending on the site of cancer and the duration of follow-up. The results emphasize the potential risks of cancer specifically from standard CT examinations in pediatrics and underline the necessity of optimization of radiological protocols.

  2. 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

    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...... with different characteristics and risk, expression-based classification specifically developed in low-risk patients have higher predictive power in this group.......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...

  3. Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients.

    Science.gov (United States)

    Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

    2017-09-29

    Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients.

  4. Identification of a robust gene signature that predicts breast cancer outcome in independent data sets

    International Nuclear Information System (INIS)

    Korkola, James E; Waldman, Frederic M; Blaveri, Ekaterina; DeVries, Sandy; Moore, Dan H II; Hwang, E Shelley; Chen, Yunn-Yi; Estep, Anne LH; Chew, Karen L; Jensen, Ronald H

    2007-01-01

    Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients. We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets. We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62–65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors. This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients

  5. 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)

    Winkler Wille, Mathilde M.; Dirksen, Asger; Riel, Sarah J. van; Jacobs, Colin; Scholten, Ernst T.; Ginneken, Bram van; Saghir, Zaigham; Pedersen, Jesper Holst; Hohwue Thomsen, Laura; Skovgaard, Lene T.

    2015-01-01

    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.)

  6. 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.)

  7. Predicting Forearm Physical Exposures During Computer Work Using Self-Reports, Software-Recorded Computer Usage Patterns, and Anthropometric and Workstation Measurements.

    Science.gov (United States)

    Huysmans, Maaike A; Eijckelhof, Belinda H W; Garza, Jennifer L Bruno; Coenen, Pieter; Blatter, Birgitte M; Johnson, Peter W; van Dieën, Jaap H; van der Beek, Allard J; Dennerlein, Jack T

    2017-12-15

    Alternative techniques to assess physical exposures, such as prediction models, could facilitate more efficient epidemiological assessments in future large cohort studies examining physical exposures in relation to work-related musculoskeletal symptoms. The aim of this study was to evaluate two types of models that predict arm-wrist-hand physical exposures (i.e. muscle activity, wrist postures and kinematics, and keyboard and mouse forces) during computer use, which only differed with respect to the candidate predicting variables; (i) a full set of predicting variables, including self-reported factors, software-recorded computer usage patterns, and worksite measurements of anthropometrics and workstation set-up (full models); and (ii) a practical set of predicting variables, only including the self-reported factors and software-recorded computer usage patterns, that are relatively easy to assess (practical models). Prediction models were build using data from a field study among 117 office workers who were symptom-free at the time of measurement. Arm-wrist-hand physical exposures were measured for approximately two hours while workers performed their own computer work. Each worker's anthropometry and workstation set-up were measured by an experimenter, computer usage patterns were recorded using software and self-reported factors (including individual factors, job characteristics, computer work behaviours, psychosocial factors, workstation set-up characteristics, and leisure-time activities) were collected by an online questionnaire. We determined the predictive quality of the models in terms of R2 and root mean squared (RMS) values and exposure classification agreement to low-, medium-, and high-exposure categories (in the practical model only). The full models had R2 values that ranged from 0.16 to 0.80, whereas for the practical models values ranged from 0.05 to 0.43. Interquartile ranges were not that different for the two models, indicating that only for some

  8. Predictive values of upper gastrointestinal cancer alarm symptoms in the general population - a nationwide cohort study

    DEFF Research Database (Denmark)

    Rasmussen, Sanne; Haastrup, Peter Fentz; Balasubramaniam, Kirubakaran

    2018-01-01

    BACKGROUND: Survival rates for upper gastrointestinal (GI) cancer are poor since many are diagnosed at advanced stages. Fast track endoscopy has been introduced to prompt diagnosis for patients with alarm symptoms that could be indicative of upper GI cancer. However, these symptoms may represent...... to complete a survey comprising of questions on several symptom experiences, including alarm symptoms for upper GI cancer within the past four weeks. The participants were asked about specific symptoms (repeated vomiting, difficulty swallowing, signs of upper GI bleeding or persistent and recent......-onset abdominal pain) and non-specific symptoms (nausea, weight loss, loss of appetite, feeling unwell and tiredness). We obtained information on upper GI cancer diagnosed in a 12-month period after completing the questionnaire from the Danish Cancer Registry. We calculated positive predictive values and positive...

  9. Predicting survival time in noncurative patients with advanced cancer: a prospective study in China.

    Science.gov (United States)

    Cui, Jing; Zhou, Lingjun; Wee, B; Shen, Fengping; Ma, Xiuqiang; Zhao, Jijun

    2014-05-01

    Accurate prediction of prognosis for cancer patients is important for good clinical decision making in therapeutic and care strategies. The application of prognostic tools and indicators could improve prediction accuracy. This study aimed to develop a new prognostic scale to predict survival time of advanced cancer patients in China. We prospectively collected items that we anticipated might influence survival time of advanced cancer patients. Participants were recruited from 12 hospitals in Shanghai, China. We collected data including demographic information, clinical symptoms and signs, and biochemical test results. Log-rank tests, Cox regression, and linear regression were performed to develop a prognostic scale. Three hundred twenty patients with advanced cancer were recruited. Fourteen prognostic factors were included in the prognostic scale: Karnofsky Performance Scale (KPS) score, pain, ascites, hydrothorax, edema, delirium, cachexia, white blood cell (WBC) count, hemoglobin, sodium, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) values. The score was calculated by summing the partial scores, ranging from 0 to 30. When using the cutoff points of 7-day, 30-day, 90-day, and 180-day survival time, the scores were calculated as 12, 10, 8, and 6, respectively. We propose a new prognostic scale including KPS, pain, ascites, hydrothorax, edema, delirium, cachexia, WBC count, hemoglobin, sodium, total bilirubin, direct bilirubin, AST, and ALP values, which may help guide physicians in predicting the likely survival time of cancer patients more accurately. More studies are needed to validate this scale in the future.

  10. Comparative Risk Predictions of Second Cancers After Carbon-Ion Therapy Versus Proton Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Eley, John G., E-mail: jeley@som.umaryland.edu [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); University of Texas Graduate School of Biomedical Sciences, Houston, Texas (United States); Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland (United States); Friedrich, Thomas [GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt (Germany); Homann, Kenneth L.; Howell, Rebecca M. [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); University of Texas Graduate School of Biomedical Sciences, Houston, Texas (United States); Scholz, Michael; Durante, Marco [GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt (Germany); Newhauser, Wayne D. [Department of Physics and Astronomy, Louisiana State University and Agricultural and Mechanical College, Baton Rouge, Louisiana (United States); Mary Bird Perkins Cancer Center, Baton Rouge, Louisiana (United States)

    2016-05-01

    Purpose: This work proposes a theoretical framework that enables comparative risk predictions for second cancer incidence after particle beam therapy for different ion species for individual patients, accounting for differences in relative biological effectiveness (RBE) for the competing processes of tumor initiation and cell inactivation. Our working hypothesis was that use of carbon-ion therapy instead of proton therapy would show a difference in the predicted risk of second cancer incidence in the breast for a sample of Hodgkin lymphoma (HL) patients. Methods and Materials: We generated biologic treatment plans and calculated relative predicted risks of second cancer in the breast by using two proposed methods: a full model derived from the linear quadratic model and a simpler linear-no-threshold model. Results: For our reference calculation, we found the predicted risk of breast cancer incidence for carbon-ion plans-to-proton plan ratio, , to be 0.75 ± 0.07 but not significantly smaller than 1 (P=.180). Conclusions: Our findings suggest that second cancer risks are, on average, comparable between proton therapy and carbon-ion therapy.

  11. Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error.

    Science.gov (United States)

    Shipitsin, M; Small, C; Choudhury, S; Giladi, E; Friedlander, S; Nardone, J; Hussain, S; Hurley, A D; Ernst, C; Huang, Y E; Chang, H; Nifong, T P; Rimm, D L; Dunyak, J; Loda, M; Berman, D M; Blume-Jensen, P

    2014-09-09

    Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation. Prostatectomy samples from a large patient cohort with long follow-up were blindly assessed by expert pathologists who identified the tissue regions with the highest and lowest Gleason grade from each patient. To simulate biopsy-sampling error, a core from a high- and a low-Gleason area from each patient sample was used to generate a 'high' and a 'low' tumour microarray, respectively. Using a quantitative proteomics approach, we identified from 160 candidates 12 biomarkers that predicted prostate cancer aggressiveness (surgical Gleason and TNM stage) and lethal outcome robustly in both high- and low-Gleason areas. Conversely, a previously reported lethal outcome-predictive marker signature for prostatectomy tissue was unable to perform under circumstances of maximal sampling error. Our results have important implications for cancer biomarker discovery in general and development of a sampling error-resistant clinical biopsy test for prediction of prostate cancer aggressiveness.

  12. Robust prediction of anti-cancer drug sensitivity and sensitivity-specific biomarker.

    Directory of Open Access Journals (Sweden)

    Heewon Park

    Full Text Available The personal genomics era has attracted a large amount of attention for anti-cancer therapy by patient-specific analysis. Patient-specific analysis enables discovery of individual genomic characteristics for each patient, and thus we can effectively predict individual genetic risk of disease and perform personalized anti-cancer therapy. Although the existing methods for patient-specific analysis have successfully uncovered crucial biomarkers, their performance takes a sudden turn for the worst in the presence of outliers, since the methods are based on non-robust manners. In practice, clinical and genomic alterations datasets usually contain outliers from various sources (e.g., experiment error, coding error, etc. and the outliers may significantly affect the result of patient-specific analysis. We propose a robust methodology for patient-specific analysis in line with the NetwrokProfiler. In the proposed method, outliers in high dimensional gene expression levels and drug response datasets are simultaneously controlled by robust Mahalanobis distance in robust principal component space. Thus, we can effectively perform for predicting anti-cancer drug sensitivity and identifying sensitivity-specific biomarkers for individual patients. We observe through Monte Carlo simulations that the proposed robust method produces outstanding performances for predicting response variable in the presence of outliers. We also apply the proposed methodology to the Sanger dataset in order to uncover cancer biomarkers and predict anti-cancer drug sensitivity, and show the effectiveness of our method.

  13. DNA Mismatch Repair Deficiency in Rectal Cancer: Benchmarking Its Impact on Prognosis, Neoadjuvant Response Prediction, and Clinical Cancer Genetics.

    Science.gov (United States)

    de Rosa, Nicole; Rodriguez-Bigas, Miguel A; Chang, George J; Veerapong, Jula; Borras, Ester; Krishnan, Sunil; Bednarski, Brian; Messick, Craig A; Skibber, John M; Feig, Barry W; Lynch, Patrick M; Vilar, Eduardo; You, Y Nancy

    2016-09-01

    DNA mismatch repair deficiency (dMMR) hallmarks consensus molecular subtype 1 of colorectal cancer. It is being routinely tested, but little is known about dMMR rectal cancers. The efficacy of novel treatment strategies cannot be established without benchmarking the outcomes of dMMR rectal cancer with current therapy. We aimed to delineate the impact of dMMR on prognosis, the predicted response to fluoropyrimidine-based neoadjuvant therapy, and implications of germline alterations in the MMR genes in rectal cancer. Between 1992 and 2012, 62 patients with dMMR rectal cancers underwent multimodality therapy. Oncologic treatment and outcomes as well as clinical genetics work-up were examined. Overall and rectal cancer-specific survival were calculated by the Kaplan-Meier method. The median age at diagnosis was 41 years. MMR deficiency was most commonly due to alterations in MSH2 (53%) or MSH6 (23%). After a median follow-up of 6.8 years, the 5-year rectal cancer-specific survival was 100% for stage I and II, 85.1% for stage III, and 60.0% for stage IV disease. Fluoropyrimidine-based neoadjuvant chemoradiation was associated with a complete pathologic response rate of 27.6%. The extent of surgical resection was influenced by synchronous colonic disease at presentation, tumor height, clinical stage, and pelvic radiation. An informed decision for a limited resection focusing on proctectomy did not compromise overall survival. Five of the 11 (45.5%) deaths during follow-up were due to extracolorectal malignancies. dMMR rectal cancer had excellent prognosis and pathologic response with current multimodality therapy including an individualized surgical treatment plan. Identification of a dMMR rectal cancer should trigger germline testing, followed by lifelong surveillance for both colorectal and extracolorectal malignancies. We herein provide genotype-specific outcome benchmarks for comparison with novel interventions. © 2016 by American Society of Clinical Oncology.

  14. 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...

  15. Identification of cancer cytotoxic modulators of PDE3A by predictive chemogenomics

    Science.gov (United States)

    de Waal, Luc; Lewis, Timothy A.; Rees, Matthew G.; Tsherniak, Aviad; Wu, Xiaoyun; Choi, Peter S.; Gechijian, Lara; Hartigan, Christina; Faloon, Patrick W.; Hickey, Mark J.; Tolliday, Nicola; Carr, Steven A.; Clemons, Paul A.; Munoz, Benito; Wagner, Bridget K.; Shamji, Alykhan F.; Koehler, Angela N.; Schenone, Monica; Burgin, Alex B.; Schreiber, Stuart L.; Greulich, Heidi; Meyerson, Matthew

    2015-01-01

    High cancer death rates indicate the need for new anti-cancer therapeutic agents. Approaches to discover new cancer drugs include target-based drug discovery and phenotypic screening. Here, we identified phosphodiesterase 3A modulators as cell-selective cancer cytotoxic compounds by phenotypic compound library screening and target deconvolution by predictive chemogenomics. We found that sensitivity to 6-(4-(diethylamino)-3-nitrophenyl)-5-methyl-4,5-dihydropyridazin-3(2H)-one, or DNMDP, across 766 cancer cell lines correlates with expression of the phosphodiesterase 3A gene, PDE3A. Like DNMDP, a subset of known PDE3A inhibitors kill selected cancer cells while others do not. Furthermore, PDE3A depletion leads to DNMDP resistance. We demonstrated that DNMDP binding to PDE3A promotes an interaction between PDE3A and Schlafen 12 (SLFN12), suggesting a neomorphic activity. Co-expression of SLFN12 with PDE3A correlates with DNMDP sensitivity, while depletion of SLFN12 results in decreased DNMDP sensitivity. Our results implicate PDE3A modulators as candidate cancer therapeutic agents and demonstrate the power of predictive chemogenomics in small-molecule discovery. PMID:26656089

  16. Application of CT texture analysis in predicting histopathological characteristics of gastric cancers

    International Nuclear Information System (INIS)

    Liu, Shunli; Liu, Song; Ji, Changfeng; Zheng, Huanhuan; Pan, Xia; Zhang, Yujuan; He, Jian; Zhou, Zhengyang; Guan, Wenxian; Chen, Ling; Guan, Yue; Li, Weifeng; Ge, Yun

    2017-01-01

    To explore the application of computed tomography (CT) texture analysis in predicting histopathological features of gastric cancers. Preoperative contrast-enhanced CT images and postoperative histopathological features of 107 patients (82 men, 25 women) with gastric cancers were retrospectively reviewed. CT texture analysis generated: (1) mean attenuation, (2) standard deviation, (3) max frequency, (4) mode, (5) minimum attenuation, (6) maximum attenuation, (7) the fifth, 10th, 25th, 50th, 75th and 90th percentiles, and (8) entropy. Correlations between CT texture parameters and histopathological features were analysed. Mean attenuation, maximum attenuation, all percentiles and mode derived from portal venous CT images correlated significantly with differentiation degree and Lauren classification of gastric cancers (r, -0.231 ∝-0.324, 0.228 ∝ 0.321, respectively). Standard deviation and entropy derived from arterial CT images also correlated significantly with Lauren classification of gastric cancers (r = -0.265, -0.222, respectively). In arterial phase analysis, standard deviation and entropy were significantly lower in gastric cancers with than those without vascular invasion; however, minimum attenuation was significantly higher in gastric cancers with than those without vascular invasion. CT texture analysis held great potential in predicting differentiation degree, Lauren classification and vascular invasion status of gastric cancers. (orig.)

  17. Application of CT texture analysis in predicting histopathological characteristics of gastric cancers

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Shunli; Liu, Song; Ji, Changfeng; Zheng, Huanhuan; Pan, Xia; Zhang, Yujuan; He, Jian; Zhou, Zhengyang [The Affiliated Hospital of Nanjing University Medical School, Department of Radiology, Nanjing Drum Tower Hospital, Nanjing, Jiangsu Province (China); Guan, Wenxian [The Affiliated Hospital of Nanjing University Medical School, Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, Nanjing (China); Chen, Ling [The Affiliated Hospital of Nanjing University Medical School, Department of Pathology, Nanjing Drum Tower Hospital, Nanjing, Jiangsu Province (China); Guan, Yue; Li, Weifeng; Ge, Yun [Nanjing University, School of Electronic Science and Engineering, Nanjing (China)

    2017-12-15

    To explore the application of computed tomography (CT) texture analysis in predicting histopathological features of gastric cancers. Preoperative contrast-enhanced CT images and postoperative histopathological features of 107 patients (82 men, 25 women) with gastric cancers were retrospectively reviewed. CT texture analysis generated: (1) mean attenuation, (2) standard deviation, (3) max frequency, (4) mode, (5) minimum attenuation, (6) maximum attenuation, (7) the fifth, 10th, 25th, 50th, 75th and 90th percentiles, and (8) entropy. Correlations between CT texture parameters and histopathological features were analysed. Mean attenuation, maximum attenuation, all percentiles and mode derived from portal venous CT images correlated significantly with differentiation degree and Lauren classification of gastric cancers (r, -0.231 ∝-0.324, 0.228 ∝ 0.321, respectively). Standard deviation and entropy derived from arterial CT images also correlated significantly with Lauren classification of gastric cancers (r = -0.265, -0.222, respectively). In arterial phase analysis, standard deviation and entropy were significantly lower in gastric cancers with than those without vascular invasion; however, minimum attenuation was significantly higher in gastric cancers with than those without vascular invasion. CT texture analysis held great potential in predicting differentiation degree, Lauren classification and vascular invasion status of gastric cancers. (orig.)

  18. Prediction of Early Response to Chemotherapy in Lung Cancer by Using Diffusion-Weighted MR Imaging

    Directory of Open Access Journals (Sweden)

    Jing Yu

    2014-01-01

    Full Text Available Purpose. To determine whether change of apparent diffusion coefficient (ADC value could predict early response to chemotherapy in lung cancer. Materials and Methods. Twenty-five patients with advanced non-small cell lung cancer underwent chest MR imaging including DWI before and at the end of the first cycle of chemotherapy. The tumor’s mean ADC value and diameters on MR images were calculated and compared. The grouping reference was based on serial CT scans according to Response Evaluation Criteria in Solid Tumors. Logistic regression was applied to assess treatment response prediction ability of ADC value and diameters. Results. The change of ADC value in partial response group was higher than that in stable disease group (P=0.004. ROC curve showed that ADC value could predict treatment response with 100% sensitivity, 64.71% specificity, 57.14% positive predictive value, 100% negative predictive value, and 82.7% accuracy. The area under the curve for combination of ADC value and longest diameter change was higher than any parameter alone (P≤0.01. Conclusions. The change of ADC value may be a sensitive indicator to predict early response to chemotherapy in lung cancer. Prediction ability could be improved by combining the change of ADC value and longest diameter.

  19. Using recurrent neural networks to predict colorectal cancer among patients

    NARCIS (Netherlands)

    Amirkhan, Ryan; Hoogendoorn, Mark; Numans, Mattijs E.; Moons, Leon

    2018-01-01

    Development of predictive models from Electronic Medical Records (EMRs) is a far from trivial task. Especially the temporal nature of health records is an aspect that is often ignored yet of utmost importance. Additionally, data is extremely sparse. Previous research has shown that the

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

    OpenAIRE

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

    2005-01-01

    Submitted on behalf of EDAA (http://www.edaa.com/); International audience; 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 ...

  1. MOLECULAR BIOLOGICAL FACTORS IN THE PREDICTION OF PROSTATE CANCER

    Directory of Open Access Journals (Sweden)

    S. V. Vtorushin

    2017-01-01

    Full Text Available Purpose: to review the available data on molecular-genetic diagnostic and prognostic markers in prostate cancer. Material and methods. The following electronic databases were used for our systematic review: Medline, Cochrane Library and Elibrary. Of 540 studies, 61 were used for our systematic review. Results. There are currently a variety of both prognostic and diagnostic markers used for diagnosis and treatment of prostate cancer. The review presents the classification of markers depending on the method and medium in which they were identified. The molecular mechanisms of participation of the different genes and proteins in the pathogenesis and progression of prostate carcinoma were analyzed and the potential importance of their use in clinical practice was provided. Conclusion. Many of the existing markers can be used for screening and early detection of tumors, and they have been proved to have a prognostic value. However, contradictory findings with regard to certain proteins and genes require further study, their validation with the subsequent implementation into clinical practice.

  2. 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. 

  3. Early prostate cancer antigen expression in predicting presence of prostate cancer in men with histologically negative biopsies.

    Science.gov (United States)

    Hansel, D E; DeMarzo, A M; Platz, E A; Jadallah, S; Hicks, J; Epstein, J I; Partin, A W; Netto, G J

    2007-05-01

    Early prostate cancer antigen is a nuclear matrix protein that was recently shown to be expressed in prostate adenocarcinoma and adjacent benign tissue. Previous studies have demonstrated early prostate cancer antigen expression in benign prostate tissue up to 5 years before a diagnosis of prostate carcinoma, suggesting that early prostate cancer antigen could be used as a potential predictive marker. We evaluated early prostate cancer antigen expression by immunohistochemistry using a polyclonal antibody (Onconome Inc., Seattle, Washington) on benign biopsies from 98 patients. Biopsies were obtained from 4 groups that included 39 patients with first time negative biopsy (group 1), 24 patients with persistently negative biopsies (group 2), 8 patients with initially negative biopsies who were subsequently diagnosed with prostate carcinoma (group 3) and negative biopsies obtained from 27 cases where other concurrent biopsies contained prostate carcinoma (group 4). Early prostate cancer antigen staining was assessed by 2 of the authors who were blind to the group of the examined sections. Staining intensity (range 0 to 3) and extent (range 1 to 3) scores were assigned. The presence of intensity 3 staining in any of the blocks of a biopsy specimen was considered as positive for early prostate cancer antigen for the primary outcome in the statistical analysis. In addition, as secondary outcomes we evaluated the data using the proportion of blocks with intensity 3 early prostate cancer antigen staining, the mean of the product of staining intensity and staining extent of all blocks within a biopsy, and the mean of the product of intensity 3 staining and extent. Primary outcome analysis revealed the proportion of early prostate cancer antigen positivity to be highest in group 3 (6 of 8, 75%) and lowest in group 2 (7 of 24, 29%, p=0.04 for differences among groups). A relatively higher than expected proportion of early prostate cancer antigen positivity was present in

  4. IMS2 – An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath

    Directory of Open Access Journals (Sweden)

    Baumbach Jan

    2007-12-01

    Full Text Available IMS2 is an Integrated Medical Software system for the analysis of Ion Mobility Spectrometry (IMS data. It assists medical staff with the following IMS data processing steps: acquisition, visualization, classification, and annotation. IMS2 provides data analysis and interpretation features on the one hand, and also helps to improve the classification by increasing the number of the pre-classified datasets on the other hand. It is designed to facilitate early detection of lung cancer, one of the most common cancer types with one million deaths each year around the world.

  5. 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

  6. Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

    Science.gov (United States)

    Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

    2017-07-01

    To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

  7. CAsubtype: An R Package to Identify Gene Sets Predictive of Cancer Subtypes and Clinical Outcomes.

    Science.gov (United States)

    Kong, Hualei; Tong, Pan; Zhao, Xiaodong; Sun, Jielin; Li, Hua

    2018-03-01

    In the past decade, molecular classification of cancer has gained high popularity owing to its high predictive power on clinical outcomes as compared with traditional methods commonly used in clinical practice. In particular, using gene expression profiles, recent studies have successfully identified a number of gene sets for the delineation of cancer subtypes that are associated with distinct prognosis. However, identification of such gene sets remains a laborious task due to the lack of tools with flexibility, integration and ease of use. To reduce the burden, we have developed an R package, CAsubtype, to efficiently identify gene sets predictive of cancer subtypes and clinical outcomes. By integrating more than 13,000 annotated gene sets, CAsubtype provides a comprehensive repertoire of candidates for new cancer subtype identification. For easy data access, CAsubtype further includes the gene expression and clinical data of more than 2000 cancer patients from TCGA. CAsubtype first employs principal component analysis to identify gene sets (from user-provided or package-integrated ones) with robust principal components representing significantly large variation between cancer samples. Based on these principal components, CAsubtype visualizes the sample distribution in low-dimensional space for better understanding of the distinction between samples and classifies samples into subgroups with prevalent clustering algorithms. Finally, CAsubtype performs survival analysis to compare the clinical outcomes between the identified subgroups, assessing their clinical value as potentially novel cancer subtypes. In conclusion, CAsubtype is a flexible and well-integrated tool in the R environment to identify gene sets for cancer subtype identification and clinical outcome prediction. Its simple R commands and comprehensive data sets enable efficient examination of the clinical value of any given gene set, thus facilitating hypothesis generating and testing in biological and

  8. Identification of a claudin-4 and E-cadherin score to predict prognosis in breast cancer.

    Science.gov (United States)

    Szasz, Attila M; Nemeth, Zsuzsanna; Gyorffy, Balazs; Micsinai, Mariann; Krenacs, Tibor; Baranyai, Zsolt; Harsanyi, Laszlo; Kiss, Andras; Schaff, Zsuzsa; Tokes, Anna-Maria; Kulka, Janina

    2011-12-01

    The elevated expression of claudins (CLDN) and E-cadherin (CDH-1) was found to correlate with poor prognostic features. Our aim was to perform a comprehensive analysis to assess their potential to predict prognosis in breast cancer. The expression of CLDN-1, -3-5, -7, -8, -10, -15, -18, and E-cadherin at the mRNA level was evaluated in correlation with survival in datasets containing expression measurements of 1809 breast cancer patients. The breast cancer tissues of 197 patients were evaluated with tissue microarray technique and immunohistochemical method for CLDN-1-5, -7, and E-cadherin protein expression. An additional validation set of 387 patients was used to test the accuracy of the resulting prognostic score. Based on the bioinformatic screening of publicly-available datasets, the metagene of CLDN-3, -4, -7, and E-cadherin was shown to have the most powerful predictive power in the survival analyses. An immunohistochemical protein profile consisting of CLDN-2, -4, and E-cadherin was able to predict outcome in the most effective manner in the training set. Combining the overlapping members of the above two methods resulted in the claudin-4 and E-cadherin score (CURIO), which was able to accurately predict relapse-free survival in the validation cohort (P = 0.029). The multivariate analysis, including clinicopathological variables and the CURIO, showed that the latter kept its predictive power (P = 0.040). Furthermore, the CURIO was able to further refine prognosis, separating good versus poor prognosis subgroups in luminal A, luminal B, and triple-negative breast cancer intrinsic subtypes. In breast cancer, the CURIO provides additional prognostic information besides the routinely utilized diagnostic approaches and factors. © 2011 Japanese Cancer Association.

  9. In situ immune response after neoadjuvant chemotherapy for breast cancer predicts survival.

    Science.gov (United States)

    Ladoire, Sylvain; Mignot, Grégoire; Dabakuyo, Sandrine; Arnould, Laurent; Apetoh, Lionel; Rébé, Cedric; Coudert, Bruno; Martin, Francois; Bizollon, Marie Hélène; Vanoli, André; Coutant, Charles; Fumoleau, Pierre; Bonnetain, Franck; Ghiringhelli, François

    2011-07-01

    Accumulating preclinical evidence suggests that anticancer immune responses contribute to the success of chemotherapy. However, the predictive value of tumour-infiltrating lymphocytes after neoadjuvant chemotherapy for breast cancer remains unknown. We hypothesized that the nature of the immune infiltrate following neoadjuvant chemotherapy would predict patient survival. In a series of 111 consecutive HER2- and a series of 51 non-HER2-overexpressing breast cancer patients treated by neoadjuvant chemotherapy, we studied by immunohistochemistry tumour infiltration by FOXP3 and CD8 T lymphocytes before and after chemotherapy. Kaplan-Meier analysis and Cox modelling were used to assess relapse-free survival (RFS) and overall survival (OS). A predictive scoring system using American Joint Committee on Cancer (AJCC) pathological staging and immunological markers was created. Association of high CD8 and low FOXP3 cell infiltrates after chemotherapy was significantly associated with improved RFS (p = 0.02) and OS (p = 0.002), and outperformed classical predictive factors in multivariate analysis. A combined score associating CD8/FOXP3 ratio and pathological AJCC staging isolated a subgroup of patients with a long-term overall survival of 100%. Importantly, this score also identified patients with a favourable prognosis in an independent cohort of HER2-negative breast cancer patients. These results suggest that immunological CD8 and FOXP3 cell infiltrate after treatment is an independent predictive factor of survival in breast cancer patients treated with neoadjuvant chemotherapy and provides new insights into the role of the immune milieu and cancer. Copyright © 2011 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

  10. Identification of a Genomic Signature Predicting for Recurrence in Early Stage Ovarian Cancer

    Science.gov (United States)

    2015-12-01

    do it. Thus, instead of simply sequencing all the FFPE samples, we used 10 tumor samples (5 recurrent and 5 non recurrent ) to test sequencing and...Award Number: W81XWH-12-1-0521 TITLE: Identification of a Genomic Signature Predicting for Recurrence in Early-Stage Ovarian Cancer PRINCIPAL...4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-12-1-0521 Identification of a Genomic Signature Predicting for Recurrence in

  11. Visually directed vs. software-based targeted biopsy compared to transperineal template mapping biopsy in the detection of clinically significant prostate cancer.

    Science.gov (United States)

    Valerio, Massimo; McCartan, Neil; Freeman, Alex; Punwani, Shonit; Emberton, Mark; Ahmed, Hashim U

    2015-10-01

    Targeted biopsy based on cognitive or software magnetic resonance imaging (MRI) to transrectal ultrasound registration seems to increase the detection rate of clinically significant prostate cancer as compared with standard biopsy. However, these strategies have not been directly compared against an accurate test yet. The aim of this study was to obtain pilot data on the diagnostic ability of visually directed targeted biopsy vs. software-based targeted biopsy, considering transperineal template mapping (TPM) biopsy as the reference test. Prospective paired cohort study included 50 consecutive men undergoing TPM with one or more visible targets detected on preoperative multiparametric MRI. Targets were contoured on the Biojet software. Patients initially underwent software-based targeted biopsies, then visually directed targeted biopsies, and finally systematic TPM. The detection rate of clinically significant disease (Gleason score ≥3+4 and/or maximum cancer core length ≥4mm) of one strategy against another was compared by 3×3 contingency tables. Secondary analyses were performed using a less stringent threshold of significance (Gleason score ≥4+3 and/or maximum cancer core length ≥6mm). Median age was 68 (interquartile range: 63-73); median prostate-specific antigen level was 7.9ng/mL (6.4-10.2). A total of 79 targets were detected with a mean of 1.6 targets per patient. Of these, 27 (34%), 28 (35%), and 24 (31%) were scored 3, 4, and 5, respectively. At a patient level, the detection rate was 32 (64%), 34 (68%), and 38 (76%) for visually directed targeted, software-based biopsy, and TPM, respectively. Combining the 2 targeted strategies would have led to detection rate of 39 (78%). At a patient level and at a target level, software-based targeted biopsy found more clinically significant diseases than did visually directed targeted biopsy, although this was not statistically significant (22% vs. 14%, P = 0.48; 51.9% vs. 44.3%, P = 0.24). Secondary

  12. Chronic and episodic stress predict physical symptom bother following breast cancer diagnosis.

    Science.gov (United States)

    Harris, Lauren N; Bauer, Margaret R; Wiley, Joshua F; Hammen, Constance; Krull, Jennifer L; Crespi, Catherine M; Weihs, Karen L; Stanton, Annette L

    2017-12-01

    Breast cancer patients often experience adverse physical side effects of medical treatments. According to the biobehavioral model of cancer stress and disease, life stress during diagnosis and treatment may negatively influence the trajectory of women's physical health-related adjustment to breast cancer. This longitudinal study examined chronic and episodic stress as predictors of bothersome physical symptoms during the year after breast cancer diagnosis. Women diagnosed with breast cancer in the previous 4 months (N = 460) completed a life stress interview for contextual assessment of chronic and episodic stress severity at study entry and 9 months later. Physical symptom bother (e.g., pain, fatigue) was measured at study entry, every 6 weeks through 6 months, and at nine and 12 months. In multilevel structural equation modeling (MSEM) analyses, both chronic stress and episodic stress occurring shortly after diagnosis predicted greater physical symptom bother over the study period. Episodic stress reported to have occurred prior to diagnosis did not predict symptom bother in MSEM analyses, and the interaction between chronic and episodic stress on symptom bother was not significant. Results suggest that ongoing chronic stress and episodic stress occurring shortly after breast cancer diagnosis are important predictors of bothersome symptoms during and after cancer treatment. Screening for chronic stress and recent stressful life events in the months following diagnosis may help to identify breast cancer patients at risk for persistent and bothersome physical symptoms. Interventions to prevent or ameliorate treatment-related physical symptoms may confer added benefit by addressing ongoing non-cancer-related stress in women's lives.

  13. High serum uric acid concentration predicts poor survival in patients with breast cancer.

    Science.gov (United States)

    Yue, Cai-Feng; Feng, Pin-Ning; Yao, Zhen-Rong; Yu, Xue-Gao; Lin, Wen-Bin; Qian, Yuan-Min; Guo, Yun-Miao; Li, Lai-Sheng; Liu, Min

    2017-10-01

    Uric acid is a product of purine metabolism. Recently, uric acid has gained much attraction in cancer. In this study, we aim to investigate the clinicopathological and prognostic significance of serum uric acid concentration in breast cancer patients. A total of 443 female patients with histopathologically diagnosed breast cancer were included. After a mean follow-up time of 56months, survival was analysed using the Kaplan-Meier method. To further evaluate the prognostic significance of uric acid concentrations, univariate and multivariate Cox regression analyses were applied. Of the clinicopathological parameters, uric acid concentration was associated with age, body mass index, ER status and PR status. Univariate analysis identified that patients with increased uric acid concentration had a significantly inferior overall survival (HR 2.13, 95% CI 1.15-3.94, p=0.016). In multivariate analysis, we found that high uric acid concentration is an independent prognostic factor predicting death, but insufficient to predict local relapse or distant metastasis. Kaplan-Meier analysis indicated that high uric acid concentration is related to the poor overall survival (p=0.013). High uric acid concentration predicts poor survival in patients with breast cancer, and might serve as a potential marker for appropriate management of breast cancer patients. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data.

    Science.gov (United States)

    Nidheesh, N; Abdul Nazeer, K A; Ameer, P M

    2017-12-01

    Clustering algorithms with steps involving randomness usually give different results on different executions for the same dataset. This non-deterministic nature of algorithms such as the K-Means clustering algorithm limits their applicability in areas such as cancer subtype prediction using gene expression data. It is hard to sensibly compare the results of such algorithms with those of other algorithms. The non-deterministic nature of K-Means is due to its random selection of data points as initial centroids. We propose an improved, density based version of K-Means, which involves a novel and systematic method for selecting initial centroids. The key idea of the algorithm is to select data points which belong to dense regions and which are adequately separated in feature space as the initial centroids. We compared the proposed algorithm to a set of eleven widely used single clustering algorithms and a prominent ensemble clustering algorithm which is being used for cancer data classification, based on the performances on a set of datasets comprising ten cancer gene expression datasets. The proposed algorithm has shown better overall performance than the others. There is a pressing need in the Biomedical domain for simple, easy-to-use and more accurate Machine Learning tools for cancer subtype prediction. The proposed algorithm is simple, easy-to-use and gives stable results. Moreover, it provides comparatively better predictions of cancer subtypes from gene expression data. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Esophageal cancer prediction based on qualitative features using adaptive fuzzy reasoning method

    Directory of Open Access Journals (Sweden)

    Raed I. Hamed

    2015-04-01

    Full Text Available Esophageal cancer is one of the most common cancers world-wide and also the most common cause of cancer death. In this paper, we present an adaptive fuzzy reasoning algorithm for rule-based systems using fuzzy Petri nets (FPNs, where the fuzzy production rules are represented by FPN. We developed an adaptive fuzzy Petri net (AFPN reasoning algorithm as a prognostic system to predict the outcome for esophageal cancer based on the serum concentrations of C-reactive protein and albumin as a set of input variables. The system can perform fuzzy reasoning automatically to evaluate the degree of truth of the proposition representing the risk degree value with a weight value to be optimally tuned based on the observed data. In addition, the implementation process for esophageal cancer prediction is fuzzily deducted by the AFPN algorithm. Performance of the composite model is evaluated through a set of experiments. Simulations and experimental results demonstrate the effectiveness and performance of the proposed algorithms. A comparison of the predictive performance of AFPN models with other methods and the analysis of the curve showed the same results with an intuitive behavior of AFPN models.

  16. A 2-stage ovarian cancer screening strategy using the Risk of Ovarian Cancer Algorithm (ROCA) identifies early-stage incident cancers and demonstrates high positive predictive value.

    Science.gov (United States)

    Lu, Karen H; Skates, Steven; Hernandez, Mary A; Bedi, Deepak; Bevers, Therese; Leeds, Leroy; Moore, Richard; Granai, Cornelius; Harris, Steven; Newland, William; Adeyinka, Olasunkanmi; Geffen, Jeremy; Deavers, Michael T; Sun, Charlotte C; Horick, Nora; Fritsche, Herbert; Bast, Robert C

    2013-10-01

    A 2-stage ovarian cancer screening strategy was evaluated that incorporates change of carbohydrate antigen 125 (CA125) levels over time and age to estimate risk of ovarian cancer. Women with high-risk scores were referred for transvaginal ultrasound (TVS). A single-arm, prospective study of postmenopausal women was conducted. Participants underwent an annual CA125 blood test. Based on the Risk of Ovarian Cancer Algorithm (ROCA) result, women were triaged to next annual CA125 test (low risk), repeat CA125 test in 3 months (intermediate risk), or TVS and referral to a gynecologic oncologist (high risk). A total of 4051 women participated over 11 years. The average annual rate of referral to a CA125 test in 3 months was 5.8%, and the average annual referral rate to TVS and review by a gynecologic oncologist was 0.9%. Ten women underwent surgery on the basis of TVS, with 4 invasive ovarian cancers (1 with stage IA disease, 2 with stage IC disease, and 1 with stage IIB disease), 2 ovarian tumors of low malignant potential (both stage IA), 1 endometrial cancer (stage I), and 3 benign ovarian tumors, providing a positive predictive value of 40% (95% confidence interval = 12.2%, 73.8%) for detecting invasive ovarian cancer. The specificity was 99.9% (95% confidence interval = 99.7%, 100%). All 4 women with invasive ovarian cancer were enrolled in the study for at least 3 years with low-risk annual CA125 test values prior to rising CA125 levels. ROCA followed by TVS demonstrated excellent specificity and positive predictive value in a population of US women at average risk for ovarian cancer. Copyright © 2013 American Cancer Society.

  17. lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis

    Directory of Open Access Journals (Sweden)

    Silu Zhang

    2018-01-01

    Full Text Available Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs, which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.

  18. 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.

  19. Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

    Directory of Open Access Journals (Sweden)

    Christof Winter

    Full Text Available Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.

  20. A Critical Evaluation of Network and Pathway-Based Classifiers for Outcome Prediction in Breast Cancer

    NARCIS (Netherlands)

    C. Staiger (Christine); S. Cadot; R Kooter; M. Dittrich (Marcus); T. Müller (Tobias); G.W. Klau (Gunnar); L.F.A. Wessels (Lodewyk)

    2012-01-01

    htmlabstractRecently, 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

  1. Early Diagnosis of Breas Cancer Dissemination by Tumor Markers Follow-Up and Method of Prediction

    Czech Academy of Sciences Publication Activity Database

    Nekulová, M.; Šimíčková, M.; Pecen, Ladislav; Eben, Kryštof; Vermousek, I.; Stratil, P.; Černoch, M.; Lang, B.

    1994-01-01

    Roč. 41, č. 2 (1994), s. 113-118 ISSN 0028-2685 R&D Projects: GA AV ČR IAA230106 Keywords : breast cancer * progression * CEA * CA 15-3 * MCA * TPA * mathematical method of prediction Impact factor: 0.354, year: 1994

  2. Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype

    NARCIS (Netherlands)

    Lakhani, Sunil R.; Reis-Filho, Jorge S.; Fulford, Laura; Penault-Llorca, Frederique; van der Vijver, Marc; Parry, Suzanne; Bishop, Timothy; Benitez, Javier; Rivas, Carmen; Bignon, Yves-Jean; Chang-Claude, Jenny; Hamann, Ute; Cornelisse, Cees J.; Devilee, Peter; Beckmann, Matthias W.; Nestle-Krämling, Carolin; Daly, Peter A.; Haites, Neva; Varley, Jenny; Lalloo, Fiona; Evans, Gareth; Maugard, Christine; Meijers-Heijboer, Hanne; Klijn, Jan G. M.; Olah, Edith; Gusterson, Barry A.; Pilotti, Silvana; Radice, Paolo; Scherneck, Siegfried; Sobol, Hagay; Jacquemier, Jocelyne; Wagner, Teresa; Peto, Julian; Stratton, Michael R.; McGuffog, Lesley; Easton, Douglas F.

    2005-01-01

    To investigate the proportion of breast cancers arising in patients with germ line BRCA1 and BRCA2 mutations expressing basal markers and developing predictive tests for identification of high-risk patients. Histopathologic material from 182 tumors in BRCA1 mutation carriers, 63 BRCA2 carriers, and

  3. Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

    Science.gov (United States)

    Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

    2015-08-01

    Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. 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

  5. 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

  6. Predictive Value of Serum HER-2/neu in Breast Cancer Patients Treated with HERCEPTIN

    Czech Academy of Sciences Publication Activity Database

    Šimíčková, M.; Petráková, K.; Pecen, Ladislav; Nekulová, M.; Nenutil, R.

    2004-01-01

    Roč. 8, - (2004), s. 87 ISSN 1211-8869. [CECHTUMA 2004. 01.10.2004-03.10.2004, Prague] Institutional research plan: CEZ:AV0Z1030915 Keywords : predictive value * HER-2 * breast cancer Subject RIV: BB - Applied Statistics, Operational Research

  7. Colorectal cancer intrinsic subtypes predict chemotherapy benefit, deficient mismatch repair and epithelial-to-mesenchymal transition

    NARCIS (Netherlands)

    Roepman, P.; Schlicker, A.; Tabernero, J.; Majewski, I.; Tian, S.; Moreno, V.; Snel, M.H.; Chresta, C.M.; Rosenberg, R.; Nitsche, U.; Macarulla, T.; Capella, G.; Salazar, R.; Orphanides, G.; Wessels, L.F.A.; Bernards, R.; Simon, I.M.

    2013-01-01

    In most colorectal cancer (CRC) patients, outcome cannot be predicted because tumors with similar clinicopathological features can have differences in disease progression and treatment response. Therefore, a better understanding of the CRC biology is required to identify those patients who will

  8. The predictive value of the 70-gene signature for adjuvant chemotherapy in early breast cancer

    NARCIS (Netherlands)

    Knauer, Michael; Mook, Stella; Rutgers, Emiel J. T.; Bender, Richard A.; Hauptmann, Michael; van de Vijver, Marc J.; Koornstra, Rutger H. T.; Bueno-de-Mesquita, Jolien M.; Linn, Sabine C.; van 't Veer, Laura J.

    2010-01-01

    Multigene assays have been developed and validated to determine the prognosis of breast cancer. In this study, we assessed the additional predictive value of the 70-gene MammaPrint signature for chemotherapy (CT) benefit in addition to endocrine therapy (ET) from pooled study series. For 541

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

    NARCIS (Netherlands)

    van Alphen, M.J.A.; Kreeft, A.M.; van der Heijden, Ferdinand; Smeele, L.E.; Balm, A.J.M.; Balm, Alfonsus Jacobus Maria

    2013-01-01

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

  10. Preoperative Metabolic Syndrome Is Predictive of Significant Gastric Cancer Mortality after Gastrectomy: The Fujian Prospective Investigation of Cancer (FIESTA Study

    Directory of Open Access Journals (Sweden)

    Dan Hu

    2017-02-01

    Full Text Available Metabolic syndrome (MetS has been shown to be associated with an increased risk of gastric cancer. However, the impact of MetS on gastric cancer mortality remains largely unknown. Here, we prospectively examined the prediction of preoperative MetS for gastric cancer mortality by analyzing a subset of data from the ongoing Fujian prospective investigation of cancer (FIESTA study. This study was conducted among 3012 patients with gastric cancer who received radical gastrectomy between 2000 and 2010. The latest follow-up was completed in 2015. Blood/tissue specimens, demographic and clinicopathologic characteristics were collected at baseline. During 15-year follow-up, 1331 of 3012 patients died of gastric cancer. The median survival time (MST of patients with MetS was 31.3 months, which was significantly shorter than that of MetS-free patients (157.1 months. The coexistence of MetS before surgery was associated with a 2.3-fold increased risk for gastric cancer mortality (P < 0.001. The multivariate-adjusted hazard ratios (HRs were increased with invasion depth T1/T2 (HR = 2.78, P < 0.001, regional lymph node metastasis N0 (HR = 2.65, P < 0.001, positive distant metastasis (HR = 2.53, P < 0.001, TNM stage I/II (HR = 3.00, P < 0.001, intestinal type (HR = 2.96, P < 0.001, negative tumor embolus (HR = 2.34, P < 0.001, and tumor size ≤4.5 cm (HR = 2.49, P < 0.001. Further survival tree analysis confirmed the top splitting role of TNM stage, followed by MetS or hyperglycemia with remarkable discrimination ability. In this large cohort study, preoperative MetS, especially hyperglycemia, was predictive of significant gastric cancer mortality in patients with radical gastrectomy, especially for early stage of gastric cancer.

  11. Predictive Biomarkers in Colorectal Cancer: From the Single Therapeutic Target to a Plethora of Options

    Directory of Open Access Journals (Sweden)

    Daniela Rodrigues

    2016-01-01

    Full Text Available Colorectal cancer (CRC is one of the most frequent cancers and is a leading cause of cancer death worldwide. Treatments used for CRC may include some combination of surgery, radiation therapy, chemotherapy, and targeted therapy. The current standard drugs used in chemotherapy are 5-fluorouracil and leucovorin in combination with irinotecan and/or oxaliplatin. Most recently, biologic agents have been proven to have therapeutic benefits in metastatic CRC alone or in association with standard chemotherapy. However, patients present different treatment responses, in terms of efficacy and toxicity; therefore, it is important to identify biological markers that can predict the response to therapy and help select patients that would benefit from specific regimens. In this paper, authors review CRC genetic markers that could be useful in predicting the sensitivity/resistance to chemotherapy.

  12. A Comparison of Hazard Prediction and Assessment Capability (HPAC) Software Dose-Rate Contour Plots to a Sample of Local Fallout Data From Test Detonations in the Continental United States, 1945 - 1962

    National Research Council Canada - National Science Library

    Chancellor, Richard W

    2005-01-01

    A comparison of Hazard Prediction and Assessment Capability (HPAC) software dose-rate contour plots to a sample of local nuclear fallout data from test detonations in the continental United States, 1945 - 1962, is performed...

  13. Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study.

    Directory of Open Access Journals (Sweden)

    Kevin Ten Haaf

    2017-04-01

    Full Text Available Selection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years. Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer. All models considered age and various aspects of smoking exposure (smoking status, smoking duration, cigarettes per day, pack-years smoked, time since smoking cessation as risk predictors. In addition, some models considered factors such as gender, race, ethnicity, education, body mass index, chronic obstructive pulmonary disease, emphysema, personal history of cancer, personal history of pneumonia, and family history of lung cancer.Retrospective analyses were performed on 53,452 National Lung Screening Trial (NLST participants (1,925 lung cancer cases and 884 lung cancer deaths and 80,672 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO ever-smoking participants (1,463 lung cancer cases and 915 lung cancer deaths. Six-year lung cancer incidence and mortality risk predictions were assessed for (1 calibration (graphically by comparing the agreement between the predicted and the observed risks, (2 discrimination (area under the receiver operating characteristic curve [AUC] between individuals with and without lung cancer (death, and (3 clinical usefulness (net benefit in decision curve analysis by identifying risk thresholds at which applying risk-based eligibility would improve lung cancer screening efficacy. To further assess performance, risk model sensitivities and specificities in the PLCO were compared to those based on the NLST eligibility criteria. Calibration was satisfactory, but discrimination ranged widely (AUCs from 0.61 to 0.81. The models outperformed the NLST eligibility criteria over a substantial range of risk thresholds in decision curve analysis, with a higher sensitivity for all models and a

  14. A seven-gene CpG-island methylation panel predicts breast cancer progression

    International Nuclear Information System (INIS)

    Li, Yan; Melnikov, Anatoliy A.; Levenson, Victor; Guerra, Emanuela; Simeone, Pasquale; Alberti, Saverio; Deng, Youping

    2015-01-01

    DNA methylation regulates gene expression, through the inhibition/activation of gene transcription of methylated/unmethylated genes. Hence, DNA methylation profiling can capture pivotal features of gene expression in cancer tissues from patients at the time of diagnosis. In this work, we analyzed a breast cancer case series, to identify DNA methylation determinants of metastatic versus non-metastatic tumors. CpG-island methylation was evaluated on a 56-gene cancer-specific biomarker microarray in metastatic versus non-metastatic breast cancers in a multi-institutional case series of 123 breast cancer patients. Global statistical modeling and unsupervised hierarchical clustering were applied to identify a multi-gene binary classifier with high sensitivity and specificity. Network analysis was utilized to quantify the connectivity of the identified genes. Seven genes (BRCA1, DAPK1, MSH2, CDKN2A, PGR, PRKCDBP, RANKL) were found informative for prognosis of metastatic diffusion and were used to calculate classifier accuracy versus the entire data-set. Individual-gene performances showed sensitivities of 63–79 %, 53–84 % specificities, positive predictive values of 59–83 % and negative predictive values of 63–80 %. When modelled together, these seven genes reached a sensitivity of 93 %, 100 % specificity, a positive predictive value of 100 % and a negative predictive value of 93 %, with high statistical power. Unsupervised hierarchical clustering independently confirmed these findings, in close agreement with the accuracy measurements. Network analyses indicated tight interrelationship between the identified genes, suggesting this to be a functionally-coordinated module, linked to breast cancer progression. Our findings identify CpG-island methylation profiles with deep impact on clinical outcome, paving the way for use as novel prognostic assays in clinical settings. The online version of this article (doi:10.1186/s12885-015-1412-9) contains supplementary

  15. Does folic acid supplementation prevent or promote colorectal cancer? Results from model-based predictions.

    Science.gov (United States)

    Luebeck, E Georg; Moolgavkar, Suresh H; Liu, Amy Y; Boynton, Alanna; Ulrich, Cornelia M

    2008-06-01

    Folate is essential for nucleotide synthesis, DNA replication, and methyl group supply. Low-folate status has been associated with increased risks of several cancer types, suggesting a chemopreventive role of folate. However, recent findings on giving folic acid to patients with a history of colorectal polyps raise concerns about the efficacy and safety of folate supplementation and the long-term health effects of folate fortification. Results suggest that undetected precursor lesions may progress under folic acid supplementation, consistent with the role of folate role in nucleotide synthesis and cell proliferation. To better understand the possible trade-offs between the protective effects due to decreased mutation rates and possibly concomitant detrimental effects due to increased cell proliferation of folic acid, we used a biologically based mathematical model of colorectal carcinogenesis. We predict changes in cancer risk based on timing of treatment start and the potential effect of folic acid on cell proliferation and mutation rates. Changes in colorectal cancer risk in response to folic acid supplementation are likely a complex function of treatment start, duration, and effect on cell proliferation and mutations rates. Predicted colorectal cancer incidence rates under supplementation are mostly higher than rates without folic acid supplementation unless supplementation is initiated early in life (before age 20 years). To the extent to which this model predicts reality, it indicates that the effect on cancer risk when starting folic acid supplementation late in life is small, yet mostly detrimental. Experimental studies are needed to provide direct evidence for this dual role of folate in colorectal cancer and to validate and improve the model predictions.

  16. [Circulating miR-152 helps early prediction of postoperative biochemical recurrence of prostate cancer].

    Science.gov (United States)

    Chen, Jun-Feng; Liao, Yu-Feng; Ma, Jian-Bo; Mao, Qi-Feng; Jia, Guang-Cheng; Dong, Xue-Jun

    2017-07-01

    To investigate the value of circulating miR-152 in the early prediction of postoperative biochemical recurrence of prostate cancer. Sixty-six cases of prostate cancer were included in this study, 35 with and 31 without biochemical recurrence within two years postoperatively, and another 31 healthy individuals were enrolled as normal controls. The relative expression levels of circulating miR-152 in the serum of the subjects were detected by qRT-PCR, its value in the early diagnosis of postoperative biochemical recurrence of prostate cancer was assessed by ROC curve analysis, and the correlation of its expression level with the clinicopathological parameters of the patients were analyzed. The expression of circulating miR-152 was significantly lower in the serum of the prostate cancer patients than in the normal controls (t = -5.212, P = 0.001), and so was it in the patients with than in those without postoperative biochemical recurrence (t = -5.727, P = 0.001). The ROC curve for the value of miR-152 in the early prediction of postoperative biochemical recurrence of prostate cancer showed the area under the curve (AUC) to be 0.906 (95% CI: 0.809-0.964), with a sensitivity of 91.4% and a specificity of 80.6%. The expression level of miR-152 was correlated with the Gleason score, clinical stage of prostate cancer, biochemical recurrence, and bone metastasis (P 0.05). The expression level of circulating miR-152 is significantly reduced in prostate cancer patients with biochemical recurrence after prostatectomy and could be a biomarker in the early prediction of postoperative biochemical recurrence of the malignancy.

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

    International Nuclear Information System (INIS)

    Kasamatsu, Atsushi; Uzawa, Katsuhiro; Minakawa, Yasuyuki; Ishige, Shunsaku; Kasama, Hiroki; Endo-Sakamoto, Yosuke; Ogawara, Katsunori; Shiiba, Masashi; Takiguchi, Yuichi; Tanzawa, Hideki

    2015-01-01

    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

  18. MicroRNA classifier and nomogram for metastasis prediction in colon cancer.

    Science.gov (United States)

    Goossens-Beumer, Inès J; Derr, Remco S; Buermans, Henk P J; Goeman, Jelle J; Böhringer, Stefan; Morreau, Hans; Nitsche, Ulrich; Janssen, Klaus-Peter; van de Velde, Cornelis J H; Kuppen, Peter J K

    2015-01-01

    Colon cancer prognosis and treatment are currently based on a classification system still showing large heterogeneity in clinical outcome, especially in TNM stages II and III. Prognostic biomarkers for metastasis risk are warranted as development of distant recurrent disease mainly accounts for the high lethality rates of colon cancer. miRNAs have been proposed as potential biomarkers for cancer. Furthermore, a verified standard for normalization of the amount of input material in PCR-based relative quantification of miRNA expression is lacking. A selection of frozen tumor specimens from two independent patient cohorts with TNM stage II-III microsatellite stable primary adenocarcinomas was used for laser capture microdissection. Next-generation sequencing was performed on small RNAs isolated from colorectal tumors from the Dutch cohort (N = 50). Differential expression analysis, comparing in metastasized and nonmetastasized tumors, identified prognostic miRNAs. Validation was performed on colon tumors from the German cohort (N = 43) using quantitative PCR (qPCR). miR25-3p and miR339-5p were identified and validated as independent prognostic markers and used to construct a multivariate nomogram for metastasis risk prediction. The nomogram showed good probability prediction in validation. In addition, we recommend combination of miR16-5p and miR26a-5p as standard for normalization in qPCR of colon cancer tissue-derived miRNA expression. In this international study, we identified and validated a miRNA classifier in primary cancers, and propose a nomogram capable of predicting metastasis risk in microsatellite stable TNM stage II-III colon cancer. In conjunction with TNM staging, by means of a nomogram, this miRNA classifier may allow for personalized treatment decisions based on individual tumor characteristics. ©2014 American Association for Cancer Research.

  19. 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

  20. Advanced colorectal adenoma related gene expression signature may predict prognostic for colorectal cancer patients with adenoma-carcinoma sequence.

    Science.gov (United States)

    Li, Bing; Shi, Xiao-Yu; Liao, Dai-Xiang; Cao, Bang-Rong; Luo, Cheng-Hua; Cheng, Shu-Jun

    2015-01-01

    There are still no absolute parameters predicting progression of adenoma into cancer. The present study aimed to characterize functional differences on the multistep carcinogenetic process from the adenoma-carcinoma sequence. All samples were collected and mRNA expression profiling was performed by using Agilent Microarray high-throughput gene-chip technology. Then, the characteristics of mRNA expression profiles of adenoma-carcinoma sequence were described with bioinformatics software, and we analyzed the relationship between gene expression profiles of adenoma-adenocarcinoma sequence and clinical prognosis of colorectal cancer. The mRNA expressions of adenoma-carcinoma sequence were significantly different between high-grade intraepithelial neoplasia group and adenocarcinoma group. The biological process of gene ontology function enrichment analysis on differentially expressed genes between high-grade intraepithelial neoplasia group and adenocarcinoma group showed that genes enriched in the extracellular structure organization, skeletal system development, biological adhesion and itself regulated growth regulation, with the P value after FDR correction of less than 0.05. In addition, IPR-related protein mainly focused on the insulin-like growth factor binding proteins. The variable trends of gene expression profiles for adenoma-carcinoma sequence were mainly concentrated in high-grade intraepithelial neoplasia and adenocarcinoma. The differentially expressed genes are significantly correlated between high-grade intraepithelial neoplasia group and adenocarcinoma group. Bioinformatics analysis is an effective way to study the gene expression profiles in the adenoma-carcinoma sequence, and may provide an effective tool to involve colorectal cancer research strategy into colorectal adenoma or advanced adenoma.

  1. Tumor-stroma ratio predicts recurrence in patients with colon cancer treated with neoadjuvant chemotherapy

    DEFF Research Database (Denmark)

    Hansen, Torben Frøstrup; Kjær-Frifeldt, Sanne; Lindebjerg, Jan

    2017-01-01

    BACKGROUND: Neoadjuvant chemotherapy represents a new treatment approach to locally advanced colon cancer. The aim of this study was to analyze the ability of tumor-stroma ratio (TSR) to predict disease recurrence in patients with locally advanced colon cancer treated with neoadjuvant chemotherapy....... MATERIAL AND METHODS: This study included 65 patients with colon cancer treated with neoadjuvant chemotherapy in a phase II trial. All patients were planned for three cycles of capecitabine and oxaliplatin before surgery. Hematoxylin and eosin stained tissue sections from surgically resected primary tumors...... was 55%, compared to 94% in the group of patients with a high TSR. CONCLUSIONS: TSR assessed in the surgically resected primary tumor from patients with locally advanced colon cancer treated with neoadjuvant chemotherapy provides prognostic value and may serve as a relevant parameter in selecting...

  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

    2007-01-01

    was associated with a hazard ratio of 0.5 (95% confidence interval, 0.23-1.11; P = 0.083) in survival analysis. CONCLUSION: The higher gene expression of MSH2 in responders and the trend for predicting overall survival indicates a predictive value of this marker in the treatment of advanced CRC with capecitabine.......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...

  3. An ensemble machine learning approach to predict survival in breast cancer.

    Science.gov (United States)

    Djebbari, Amira; Liu, Ziying; Phan, Sieu; Famili, Fazel

    2008-01-01

    Current breast cancer predictive signatures are not unique. Can we use this fact to our advantage to improve prediction? From the machine learning perspective, it is well known that combining multiple classifiers can improve classification performance. We propose an ensemble machine learning approach which consists of choosing feature subsets and learning predictive models from them. We then combine models based on certain model fusion criteria and we also introduce a tuning parameter to control sensitivity. Our method significantly improves classification performance with a particular emphasis on sensitivity which is critical to avoid misclassifying poor prognosis patients as good prognosis.

  4. Family history of cancer predicts endometrial cancer risk independently of Lynch Syndrome: Implications for genetic counselling.

    Science.gov (United States)

    Johnatty, Sharon E; Tan, Yen Y; Buchanan, Daniel D; Bowman, Michael; Walters, Rhiannon J; Obermair, Andreas; Quinn, Michael A; Blomfield, Penelope B; Brand, Alison; Leung, Yee; Oehler, Martin K; Kirk, Judy A; O'Mara, Tracy A; Webb, Penelope M; Spurdle, Amanda B

    2017-11-01

    To determine endometrial cancer (EC) risk according to family cancer history, including assessment by degree of relatedness, type of and age at cancer diagnosis of relatives. Self-reported family cancer history was available for 1353 EC patients and 628 controls. Logistic regression was used to quantify the association between EC and cancer diagnosis in ≥1 first or second degree relative, and to assess whether level of risk differed by degree of relationship and/or relative's age at diagnosis. Risk was also evaluated for family history of up to three cancers from known familial syndromes (Lynch, Cowden, hereditary breast and ovarian cancer) overall, by histological subtype and, for a subset of 678 patients, by EC tumor mismatch repair (MMR) gene expression. Report of EC in ≥1 first- or second-degree relative was associated with significantly increased risk of EC (P=3.8×10 -7 ), independent of lifestyle risk factors. There was a trend in increasing EC risk with closer relatedness and younger age at EC diagnosis in relatives (P Trend =4.43×10 -6 ), and with increasing numbers of Lynch cancers in relatives (P Trend ≤0.0001). EC risk associated with family history did not differ by proband tumor MMR status, or histological subtype. Reported EC in first- or second-degree relatives remained associated with EC risk after conservative correction for potential misreported family history (OR 2.0; 95% CI, 1.24-3.37, P=0.004). The strongest predictor of EC risk was closer relatedness and younger EC diagnosis age in ≥1 relative. Associations remained significant irrespective of proband MMR status, and after excluding MMR pathogenic variant carriers, indicating that Lynch syndrome genes do not fully explain familial EC risk. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Expression of estrogen-related gene markers in breast cancer tissue predicts aromatase inhibitor responsiveness.

    Directory of Open Access Journals (Sweden)

    Irene Moy

    Full Text Available Aromatase inhibitors (AIs are the most effective class of drugs in the endocrine treatment of breast cancer, with an approximate 50% treatment response rate. Our objective was to determine whether intratumoral expression levels of estrogen-related genes are predictive of AI responsiveness in postmenopausal women with breast cancer. Primary breast carcinomas were obtained from 112 women who received AI therapy after failing adjuvant tamoxifen therapy and developing recurrent breast cancer. Tumor ERα and PR protein expression were analyzed by immunohistochemistry (IHC. Messenger RNA (mRNA levels of 5 estrogen-related genes-AKR1C3, aromatase, ERα, and 2 estradiol/ERα target genes, BRCA1 and PR-were measured by real-time PCR. Tumor protein and mRNA levels were compared with breast cancer progression rates to determine predictive accuracy. Responsiveness to AI therapy-defined as the combined complete response, partial response, and stable disease rates for at least 6 months-was 51%; rates were 56% in ERα-IHC-positive and 14% in ERα-IHC-negative tumors. Levels of ERα, PR, or BRCA1 mRNA were independently predictive for responsiveness to AI. In cross-validated analyses, a combined measurement of tumor ERα and PR mRNA levels yielded a more superior specificity (36% and identical sensitivity (96% to the current clinical practice (ERα/PR-IHC. In patients with ERα/PR-IHC-negative tumors, analysis of mRNA expression revealed either non-significant trends or statistically significant positive predictive values for AI responsiveness. In conclusion, expression levels of estrogen-related mRNAs are predictive for AI responsiveness in postmenopausal women with breast cancer, and mRNA expression analysis may improve patient selection.

  6. 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.

  7. The role of social support, family identification, and family constraints in predicting posttraumatic stress after cancer.

    Science.gov (United States)

    Swartzman, Samantha; Sani, Fabio; Munro, Alastair J

    2017-09-01

    We compared social support with other potential psychosocial predictors of posttraumatic stress after cancer. These included family identification, or a sense of belonging to and commonality with family members, and family constraints, or the extent to which family members are closed, judgmental, or unreceptive in conversations about cancer. We also tested the hypothesis that family constraints mediate the relationship between family identification and cancer-related posttraumatic stress. We used a cross-sectional design. Surveys were collected from 205 colorectal cancer survivors in Tayside, Scotland. Both family identification and family constraints were stronger independent predictors of posttraumatic stress than social support. In multivariate analyses, social support was not a significant independent predictor of posttraumatic stress. In addition, there was a significant indirect effect of family identification on posttraumatic stress through family constraints. Numerous studies demonstrate a link between social support and posttraumatic stress. However, experiences within the family may be more important in predicting posttraumatic stress after cancer. Furthermore, a sense of belonging to and commonality with the family may reduce the extent to which cancer survivors experience constraints on conversations about cancer; this may, in turn, reduce posttraumatic stress. Copyright © 2016 John Wiley & Sons, Ltd.

  8. The Role of BRCA2 Mutation Status as Diagnostic, Predictive, and Prognosis Biomarker for Pancreatic Cancer

    Directory of Open Access Journals (Sweden)

    Javier Martinez-Useros

    2016-01-01

    Full Text Available Pancreatic cancer is one of the deadliest cancers worldwide, and life expectancy after diagnosis is often short. Most pancreatic tumours appear sporadically and have been highly related to habits such as cigarette smoking, high alcohol intake, high carbohydrate, and sugar consumption. Other observational studies have suggested the association between pancreatic cancer and exposure to arsenic, lead, or cadmium. Aside from these factors, chronic pancreatitis and diabetes have also come to be considered as risk factors for these kinds of tumours. Studies have found that 10% of pancreatic cancer cases arise from an inherited syndrome related to some genetic alterations. One of these alterations includes mutation in BRCA2 gene. BRCA2 mutations impair DNA damage response and homologous recombination by direct regulation of RAD51. In light of these findings that link genetic factors to tumour development, DNA damage agents have been proposed as target therapies for pancreatic cancer patients carrying BRCA2 mutations. Some of these drugs include platinum-based agents and PARP inhibitors. However, the acquired resistance to PARP inhibitors has created a need for new chemotherapeutic strategies to target BRCA2. The present systematic review collects and analyses the role of BRCA2 alterations to be used in early diagnosis of an inherited syndrome associated with familiar cancer and as a prognostic and predictive biomarker for the management of pancreatic cancer patients.

  9. Distinct work-related, clinical and psychological factors predict return to work following treatment in four different cancer types.

    Science.gov (United States)

    Cooper, Alethea F; Hankins, Matthew; Rixon, Lorna; Eaton, Emma; Grunfeld, Elizabeth A

    2013-03-01

    Many factors influence return to work (RTW) following cancer treatment. However specific factors affecting RTW across different cancer types are unclear. This study examined the role of clinical, sociodemographic, work and psychological factors in RTW following treatment for breast, gynaecological, head and neck, and urological cancer. A 12-month prospective questionnaire study was conducted with 290 patients. Cox regression analyses were conducted to calculate hazard ratios (HR) for time to RTW. Between 89-94% of cancer survivors returned to work. Breast cancer survivors took the longest to return (median 30 weeks), and urology cancer survivors returned the soonest (median 5 weeks). Earlier return among breast cancer survivors was predicted by a greater sense of control over their cancer at work (HR 1.2; 95% CI: 1.09-1.37) and by full-time work (HR 2.1; CI: 1.24-3.4). Predictive of a longer return among gynaecological cancer survivors was a belief that cancer treatment may impair ability to work (HR 0.75; CI: 0.62-0.91). Among urological cancer survivors constipation was predictive of longer RTW (HR 0.99; CI: 0.97-1.00), whereas undertaking flexible working was predictive of returning sooner (HR 1.70; CI: 1.07-2.7). Head and neck cancer survivors who perceived greater negative consequences of their cancer took longer to return (HR 0.27; CI: 0.11-0.68). Those reporting better physical functioning returned sooner (HR1.04; CI: 1.01-1.08). A different profile of predictive factors emerged for the four cancer types. In addition to optimal symptom management and workplace adaptations, the findings suggest that eliciting and challenging specific cancer and treatment-related perceptions may facilitate RTW. Copyright © 2012 John Wiley & Sons, Ltd.

  10. On some descriptive and predictive methods for the dynamics of cancer growth

    Directory of Open Access Journals (Sweden)

    Iulian T. Vlad

    2015-09-01

    Full Text Available Cancer is a widely spread disease that affects a large proportion of the human population, and many research teams are developing algorithms to help medics to understand this disease. In particular, tumor growth has been studied from different viewpoints and several mathematical models have been proposed. In this paper, we review a set of comprehensive and modern tools that are useful for prediction of cancer growth in space and time. We comment on three alternative approaches. We first consider spatio-temporal stochastic processes within a Bayesian framework to model spatial heterogeneity, temporal dependence and spatio-temporal interactions amongst the pixels, providing a general modeling framework for such dynamics. We then consider predictions based on geometric properties of plane curves and vectors, and propose two methods of geometric prediction. Finally we focus on functional data analysis to statistically compare tumor contour evolutions. We also analyze real data on brain tumor.

  11. Hashimoto's thyroiditis predicts outcome in intrathyroidal papillary thyroid cancer.

    Science.gov (United States)

    Marotta, Vincenzo; Sciammarella, Concetta; Chiofalo, Maria Grazia; Gambardella, Claudio; Bellevicine, Claudio; Grasso, Marica; Conzo, Giovanni; Docimo, Giovanni; Botti, Gerardo; Losito, Simona; Troncone, Giancarlo; De Palma, Maurizio; Giacomelli, Laura; Pezzullo, Luciano; Colao, Annamaria; Faggiano, Antongiulio

    2017-09-01

    Hashimoto's thyroiditis (HT) seems to have favourable prognostic impact on papillary thyroid cancer (PTC), but data were obtained analysing all disease stages. Given that HT-related microenvironment involves solely the thyroid, we aimed to assess the relationship between HT, as detected through pathological assessment, and outcome in intrathyroidal PTC. This was a multicentre, retrospective, observational study including 301 PTC with no evidence of extrathyroidal disease. Primary study endpoint was the rate of clinical remission. Auxiliary endpoint was recurrence-free survival (RFS). HT was detected in 42.5% of the cohort and was associated to female gender, smaller tumour size, lower rate of aggressive PTC variants and less frequent post-surgery radio-iodine administration. HT showed relationship with significantly higher rate of clinical remission ( P  < 0.001, OR 4, 95% CI 1.78-8.94). PTCs with concomitant HT had significantly longer RFS, as compared with non-HT tumours ( P  = 0.004). After adjustment for other parameters affecting disease outcome at univariate analysis (age at diagnosis, histology, tumour size and multifocality), prognostic effect of HT remained significant ( P  = 0.006, OR 3.28, 95% CI 1.39-7.72). To verify whether HT could optimise the identification of PTCs with unfavourable outcome, we assessed the accuracy of 'non-HT status' as negative prognostic marker, demonstrating poor capability of identifying patients not maintaining clinical remission until final follow-up (probability of no clinical remission in PTCs without HT: 21.05%, 95% CI 15.20-27.93). In conclusion, our data show that HT represents an independent prognostic parameter in intrathyroidal PTC, but cannot improve prognostic specificity. © 2017 Society for Endocrinology.

  12. Tumor RNA disruption predicts survival benefit from breast cancer chemotherapy.

    Science.gov (United States)

    Parissenti, Amadeo M; Guo, Baoqing; Pritzker, Laura B; Pritzker, Kenneth P H; Wang, Xiaohui; Zhu, Mu; Shepherd, Lois E; Trudeau, Maureen E

    2015-08-01

    In a prior substudy of the CAN-NCIC-MA.22 clinical trial (ClinicalTrials.gov identifier NCT00066443), we observed that neoadjuvant chemotherapy reduced tumor RNA integrity in breast cancer patients, a phenomenon we term "RNA disruption." The purpose of the current study was to assess in the full patient cohort the relationship between mid-treatment tumor RNA disruption and both pCR post-treatment and, subsequently, disease-free survival (DFS) up to 108 months post-treatment. To meet these objectives, we developed the RNA disruption assay (RDA) to quantify RNA disruption and stratify it into 3 response zones of clinical importance. Zone 1 is a level of RNA disruption inadequate for pathologic complete response (pCR); Zone 2 is an intermediate level, while Zone 3 has high RNA disruption. The same RNA disruption cut points developed for pCR response were then utilized for DFS. Tumor RDA identified >fourfold more chemotherapy non-responders than did clinical response by calipers. pCR responders were clustered in RDA Zone 3, irrespective of tumor subtype. DFS was about 2-fold greater for patients with tumors in Zone 3 compared to Zone 1 patients. Kaplan-Meier survival curves corroborated these findings that high tumor RNA disruption was associated with increased DFS. DFS values for patients in zone 3 that did not achieve a pCR were similar to that of pCR recipients across tumor subtypes, including patients with hormone receptor positive tumors that seldom achieve a pCR. RDA appears superior to pCR as a chemotherapy response biomarker, supporting the prospect of its use in response-guided chemotherapy.

  13. Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm.

    Science.gov (United States)

    Manganaro, Alberto; Pizzo, Fabiola; Lombardo, Anna; Pogliaghi, Alberto; Benfenati, Emilio

    2016-02-01

    The ability of a substance to resist degradation and persist in the environment needs to be readily identified in order to protect the environment and human health. Many regulations require the assessment of persistence for substances commonly manufactured and marketed. Besides laboratory-based testing methods, in silico tools may be used to obtain a computational prediction of persistence. We present a new program to develop k-Nearest Neighbor (k-NN) models. The k-NN algorithm is a similarity-based approach that predicts the property of a substance in relation to the experimental data for its most similar compounds. We employed this software to identify persistence in the sediment compartment. Data on half-life (HL) in sediment were obtained from different sources and, after careful data pruning the final dataset, containing 297 organic compounds, was divided into four experimental classes. We developed several models giving satisfactory performances, considering that both the training and test set accuracy ranged between 0.90 and 0.96. We finally selected one model which will be made available in the near future in the freely available software platform VEGA. This model offers a valuable in silico tool that may be really useful for fast and inexpensive screening. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. In silico modeling predicts drug sensitivity of patient-derived cancer cells.

    Science.gov (United States)

    Pingle, Sandeep C; Sultana, Zeba; Pastorino, Sandra; Jiang, Pengfei; Mukthavaram, Rajesh; Chao, Ying; Bharati, Ila Sri; Nomura, Natsuko; Makale, Milan; Abbasi, Taher; Kapoor, Shweta; Kumar, Ansu; Usmani, Shahabuddin; Agrawal, Ashish; Vali, Shireen; Kesari, Santosh

    2014-05-21

    Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents. Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings. These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.

  15. Development and validation of risk prediction equations to estimate survival in patients with colorectal cancer: cohort study

    OpenAIRE

    Hippisley-Cox, Julia; Coupland, Carol

    2017-01-01

    Objective: To develop and externally validate risk prediction equations to estimate absolute and conditional survival in patients with colorectal cancer. \\ud \\ud Design: Cohort study.\\ud \\ud Setting: General practices in England providing data for the QResearch database linked to the national cancer registry.\\ud \\ud Participants: 44 145 patients aged 15-99 with colorectal cancer from 947 practices to derive the equations. The equations were validated in 15 214 patients with colorectal cancer ...

  16. Immunohistochemistry for predictive biomarkers in non-small cell lung cancer.

    Science.gov (United States)

    Mino-Kenudson, Mari

    2017-10-01

    In the era of targeted therapy, predictive biomarker testing has become increasingly important for non-small cell lung cancer. Of multiple predictive biomarker testing methods, immunohistochemistry (IHC) is widely available and technically less challenging, can provide clinically meaningful results with a rapid turn-around-time and is more cost efficient than molecular platforms. In fact, several IHC assays for predictive biomarkers have already been implemented in routine pathology practice. In this review, we will discuss: (I) the details of anaplastic lymphoma kinase (ALK) and proto-oncogene tyrosine-protein kinase ROS (ROS1) IHC assays including the performance of multiple antibody clones, pros and cons of IHC platforms and various scoring systems to design an optimal algorithm for predictive biomarker testing; (II) issues associated with programmed death-ligand 1 (PD-L1) IHC assays; (III) appropriate pre-analytical tissue handling and selection of optimal tissue samples for predictive biomarker IHC.

  17. Contralateral prophylactic mastectomy rate and predictive factors among patients with breast cancer who underwent multigene panel testing for hereditary cancer.

    Science.gov (United States)

    Elsayegh, Nisreen; Webster, Rachel D; Gutierrez Barrera, Angelica M; Lin, Heather; Kuerer, Henry M; Litton, Jennifer K; Bedrosian, Isabelle; Arun, Banu K

    2018-05-07

    Although multigene panel testing is increasingly common in patients with cancer, the relationship between its use among breast cancer patients with non-BRCA mutations or variants of uncertain significance (VUS) and disease management decisions has not been well described. This study evaluated the rate and predictive factors of CPM patients who underwent multigene panel testing. Three hundred and fourteen patients with breast cancer who underwent multigene panel testing between 2014 and 2017 were included in the analysis. Of the 314 patients, 70 elected CPM. Election of CPM by gene status was as follows: BRCA carriers (42.3%), non-BRCA carriers (30.1%), and VUS (10.6%). CPM election rates did not differ between non-BRCA carriers and BRCA carriers (P = 0.6205). Among non-BRCA carriers, negative hormone receptor status was associated with CPM (P = 0.0115). For those with a VUS, hormone receptor status was not associated with CPM (P = 0.1879). Although the rate of CPM between BRCA carriers and non-BRCA carriers was not significantly different, the predictors of CPM were different in each group. Our analyses shed the light on the increasing use of CPM among patients who are non-BRCA carriers as well those with a VUS. Our study elucidates the differing predictive factors of CPM election among BRCA carriers, non-BRCA carries, and those with a VUS. Our findings reveal the need for providers to be cognizant that non-BRCA genes and VUS drive women to elect CPM despite the lack of data for contralateral breast cancer risk associated with these genes. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

  18. PiSCES: Pi(scine) stream community estimation software: A tool for nationwide fish assemblage predictions

    Science.gov (United States)

    Background/Question/Methods What species of fish might someone find in a local stream? How might that community change as a result of changes to characteristics of the stream and its watershed? PiSCES is a browser-based toolkit developed to predict a fish community for any NHD-Pl...

  19. Prediction of toxicity and comparison of alternatives using WebTEST (Web-services Toxicity Estimation Software Tool)

    Science.gov (United States)

    A Java-based web service is being developed within the US EPA’s Chemistry Dashboard to provide real time estimates of toxicity values and physical properties. WebTEST can generate toxicity predictions directly from a simple URL which includes the endpoint, QSAR method, and ...

  20. Depressive symptoms predict head and neck cancer survival: Examining plausible behavioral and biological pathways.

    Science.gov (United States)

    Zimmaro, Lauren A; Sephton, Sandra E; Siwik, Chelsea J; Phillips, Kala M; Rebholz, Whitney N; Kraemer, Helena C; Giese-Davis, Janine; Wilson, Liz; Bumpous, Jeffrey M; Cash, Elizabeth D

    2018-03-01

    Head and neck cancers are associated with high rates of depression, which may increase the risk for poorer immediate and long-term outcomes. Here it was hypothesized that greater depressive symptoms would predict earlier mortality, and behavioral (treatment interruption) and biological (treatment response) mediators were examined. Patients (n = 134) reported depressive symptomatology at treatment planning. Clinical data were reviewed at the 2-year follow-up. Greater depressive symptoms were associated with significantly shorter survival (hazard ratio, 0.868; 95% confidence interval [CI], 0.819-0.921; P ratio, 0.865; 95% CI, 0.774-0.966; P = .010), and poorer treatment response (odds ratio, 0.879; 95% CI, 0.803-0.963; P = .005). The poorer treatment response partially explained the depression-survival relation. Other known prognostic indicators did not challenge these results. Depressive symptoms at the time of treatment planning predict overall 2-year mortality. Effects are partly influenced by the treatment response. Depression screening and intervention may be beneficial. Future studies should examine parallel biological pathways linking depression to cancer survival, including endocrine disruption and inflammation. Cancer 2018;124:1053-60. © 2018 American Cancer Society. © 2018 American Cancer Society.

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

    Science.gov (United States)

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

    2012-03-28

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

  2. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

    Science.gov (United States)

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

    2012-01-01

    The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available1. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens2. PMID:22460905

  3. Prediction of prostate cancer in unscreened men: external validation of a risk calculator.

    Science.gov (United States)

    van Vugt, Heidi A; Roobol, Monique J; Kranse, Ries; Määttänen, Liisa; Finne, Patrik; Hugosson, Jonas; Bangma, Chris H; Schröder, Fritz H; Steyerberg, Ewout W

    2011-04-01

    Prediction models need external validation to assess their value beyond the setting where the model was derived from. To assess the external validity of the European Randomized study of Screening for Prostate Cancer (ERSPC) risk calculator (www.prostatecancer-riskcalculator.com) for the probability of having a positive prostate biopsy (P(posb)). The ERSPC risk calculator was based on data of the initial screening round of the ERSPC section Rotterdam and validated in 1825 and 531 men biopsied at the initial screening round in the Finnish and Swedish sections of the ERSPC respectively. P(posb) was calculated using serum prostate specific antigen (PSA), outcome of digital rectal examination (DRE), transrectal ultrasound and ultrasound assessed prostate volume. The external validity was assessed for the presence of cancer at biopsy by calibration (agreement between observed and predicted outcomes), discrimination (separation of those with and without cancer), and decision curves (for clinical usefulness). Prostate cancer was detected in 469 men (26%) of the Finnish cohort and in 124 men (23%) of the Swedish cohort. Systematic miscalibration was present in both cohorts (mean predicted probability 34% versus 26% observed, and 29% versus 23% observed, both pscreened men, but overestimated the risk of a positive biopsy. Further research is necessary to assess the performance and applicability of the ERSPC risk calculator when a clinical setting is considered rather than a screening setting. Copyright © 2010 Elsevier Ltd. All rights reserved.

  4. A comparison of machine learning techniques for survival prediction in breast cancer.

    Science.gov (United States)

    Vanneschi, Leonardo; Farinaccio, Antonella; Mauri, Giancarlo; Antoniotti, Mauro; Provero, Paolo; Giacobini, Mario

    2011-05-11

    The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.

  5. A comparison of machine learning techniques for survival prediction in breast cancer

    Directory of Open Access Journals (Sweden)

    Vanneschi Leonardo

    2011-05-01

    Full Text Available Abstract Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.

  6. Selecting the minimum prediction base of historical data to perform 5-year predictions of the cancer burden: The GoF-optimal method.

    Science.gov (United States)

    Valls, Joan; Castellà, Gerard; Dyba, Tadeusz; Clèries, Ramon

    2015-06-01

    Predicting the future burden of cancer is a key issue for health services planning, where a method for selecting the predictive model and the prediction base is a challenge. A method, named here Goodness-of-Fit optimal (GoF-optimal), is presented to determine the minimum prediction base of historical data to perform 5-year predictions of the number of new cancer cases or deaths. An empirical ex-post evaluation exercise for cancer mortality data in Spain and cancer incidence in Finland using simple linear and log-linear Poisson models was performed. Prediction bases were considered within the time periods 1951-2006 in Spain and 1975-2007 in Finland, and then predictions were made for 37 and 33 single years in these periods, respectively. The performance of three fixed different prediction bases (last 5, 10, and 20 years of historical data) was compared to that of the prediction base determined by the GoF-optimal method. The coverage (COV) of the 95% prediction interval and the discrepancy ratio (DR) were calculated to assess the success of the prediction. The results showed that (i) models using the prediction base selected through GoF-optimal method reached the highest COV and the lowest DR and (ii) the best alternative strategy to GoF-optimal was the one using the base of prediction of 5-years. The GoF-optimal approach can be used as a selection criterion in order to find an adequate base of prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software.

    Science.gov (United States)

    Peach, Megan L; Zakharov, Alexey V; Liu, Ruifeng; Pugliese, Angelo; Tawa, Gregory; Wallqvist, Anders; Nicklaus, Marc C

    2012-10-01

    Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

  8. Development of Castration Resistant Prostate Cancer can be Predicted by a DNA Hypermethylation Profile.

    Science.gov (United States)

    Angulo, Javier C; Andrés, Guillermo; Ashour, Nadia; Sánchez-Chapado, Manuel; López, Jose I; Ropero, Santiago

    2016-03-01

    Detection of DNA hypermethylation has emerged as a novel molecular biomarker for prostate cancer diagnosis and evaluation of prognosis. We sought to define whether a hypermethylation profile of patients with prostate cancer on androgen deprivation would predict castrate resistant prostate cancer. Genome-wide methylation analysis was performed using a methylation cancer panel in 10 normal prostates and 45 tumor samples from patients placed on androgen deprivation who were followed until castrate resistant disease developed. Castrate resistant disease was defined according to EAU (European Association of Urology) guideline criteria. Two pathologists reviewed the Gleason score, Ki-67 index and neuroendocrine differentiation. Hierarchical clustering analysis was performed and relationships with outcome were investigated by Cox regression and log rank analysis. We found 61 genes that were significantly hypermethylated in greater than 20% of tumors analyzed. Three clusters of patients were characterized by a DNA methylation profile, including 1 at risk for earlier castrate resistant disease (log rank p = 0.019) and specific mortality (log rank p = 0.002). Hypermethylation of ETV1 (HR 3.75) and ZNF215 (HR 2.89) predicted disease progression despite androgen deprivation. Hypermethylation of IRAK3 (HR 13.72), ZNF215 (HR 4.81) and SEPT9 (HR 7.64) were independent markers of prognosis. Prostate specific antigen greater than 25 ng/ml, Gleason pattern 5, Ki-67 index greater than 12% and metastasis at diagnosis also predicted a negative response to androgen deprivation. Study limitations included the retrospective design and limited number of cases. Epigenetic silencing of the mentioned genes could be novel molecular markers for the prognosis of advanced prostate cancer. It might predict castrate resistance during hormone deprivation and, thus, disease specific mortality. Gene hypermethylation is associated with disease progression in patients who receive hormone therapy. It

  9. The Predictive Value of Inflammation-Related Peripheral Blood Measurements in Cancer Staging and Prognosis

    Directory of Open Access Journals (Sweden)

    Joanna L. Sylman

    2018-03-01

    Full Text Available In this review, we discuss the interaction between cancer and markers of inflammation (such as levels of inflammatory cells and proteins in the circulation, and the potential benefits of routinely monitoring these markers in peripheral blood measurement assays. Next, we discuss the prognostic value and limitations of using inflammatory markers such as neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios and C-reactive protein measurements. Furthermore, the review discusses the benefits of combining multiple types of measurements and longitudinal tracking to improve staging and prognosis prediction of patients with cancer, and the ability of novel in silico frameworks to leverage this high-dimensional data.

  10. Predictions of lung cancer based on county averages for indoor radon versus the historic incidence of regional lung cancer

    International Nuclear Information System (INIS)

    Mose, D.G.; Chrosniak, C.E.; Mushrush, G.W.

    1992-01-01

    After a decade of effort to determine the health risk associated with indoor radon, the efforts of the US Environmental Protection Agency have prevailed in the US, and 4 pCi/1 is commonly used as an Action Level. Proposals by other groups supporting lower or higher Action Levels have failed, largely due to paucity of information supporting any particular level of indoor radon. The authors' studies have compared indoor radon for zip code and county size areas with parameters such as geology, precipitation and home construction. Their attempts to verify the relative levels of lung cancer using US-EPA estimates of radon-vs-cancer have not been supportive of the EPA risk estimates. In general, when they compare the number of lung cancer cases in particular geological or geographical areas with the indoor radon levels in that area, they find the EPA predicted number of lung cancer cases to exceed the total number of lung cancer cases from all causes. Comparisons show a correlation between the incidence of lung cancer and indoor radon, but the level of risk is about 1/10 that proposed by the US-EPA. Evidently the assumptions used in their studies are flawed. Even though they find lower risk estimates using many counties in several states, fundamental flaws must be present in this type of investigation. Care must be taken in presenting health risks to the general population in cases, such as in indoor radon, where field data do not support risk estimates obtained by other means

  11. 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.

  12. Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer.

    Science.gov (United States)

    Enshaei, A; Robson, C N; Edmondson, R J

    2015-11-01

    The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, conventional algorithms become too complex for routine clinical use. This study therefore investigated the potential for an artificial intelligence model to provide this information and compared it with conventional statistical approaches. The authors created a database comprising 668 cases of epithelial ovarian cancer during a 10-year period and collected data routinely available in a clinical environment. They also collected survival data for all the patients, then constructed an artificial intelligence model capable of comparing a variety of algorithms and classifiers alongside conventional statistical approaches such as logistic regression. The model was used to predict overall survival and demonstrated that an artificial neural network (ANN) algorithm was capable of predicting survival with high accuracy (93 %) and an area under the curve (AUC) of 0.74 and that this outperformed logistic regression. The model also was used to predict the outcome of surgery and again showed that ANN could predict outcome (complete/optimal cytoreduction vs. suboptimal cytoreduction) with 77 % accuracy and an AUC of 0.73. These data are encouraging and demonstrate that artificial intelligence systems may have a role in providing prognostic and predictive data for patients. The performance of these systems likely will improve with increasing data set size, and this needs further investigation.

  13. [Establishment of risk evaluation model of peritoneal metastasis in gastric cancer and its predictive value].

    Science.gov (United States)

    Zhao, Junjie; Zhou, Rongjian; Zhang, Qi; Shu, Ping; Li, Haojie; Wang, Xuefei; Shen, Zhenbin; Liu, Fenglin; Chen, Weidong; Qin, Jing; Sun, Yihong

    2017-01-25

    To establish an evaluation model of peritoneal metastasis in gastric cancer, and to assess its clinical significance. Clinical and pathologic data of the consecutive cases of gastric cancer admitted between April 2015 and December 2015 in Department of General Surgery, Zhongshan Hospital of Fudan University were analyzed retrospectively. A total of 710 patients were enrolled in the study after 18 patients with other distant metastasis were excluded. The correlations between peritoneal metastasis and different factors were studied through univariate (Pearson's test or Fisher's exact test) and multivariate analyses (Binary Logistic regression). Independent predictable factors for peritoneal metastasis were combined to establish a risk evaluation model (nomogram). The nomogram was created with R software using the 'rms' package. In the nomogram, each factor had different scores, and every patient could have a total score by adding all the scores of each factor. A higher total score represented higher risk of peritoneal metastasis. Receiver operating characteristic (ROC) curve analysis was used to compare the sensitivity and specificity of the established nomogram. Delong. Delong. Clarke-Pearson test was used to compare the difference of the area under the curve (AUC). The cut-off value was determined by the AUC, when the ROC curve had the biggest AUC, the model had the best sensitivity and specificity. Among 710 patients, 47 patients had peritoneal metastasis (6.6%), including 30 male (30/506, 5.9%) and 17 female (17/204, 8.3%); 31 were ≥ 60 years old (31/429, 7.2%); 38 had tumor ≥ 3 cm(38/461, 8.2%). Lauren classification indicated that 2 patients were intestinal type(2/245, 0.8%), 8 patients were mixed type(8/208, 3.8%), 11 patients were diffuse type(11/142, 7.7%), and others had no associated data. CA19-9 of 13 patients was ≥ 37 kU/L(13/61, 21.3%); CA125 of 11 patients was ≥ 35 kU/L(11/36, 30.6%); CA72-4 of 11 patients was ≥ 10 kU/L(11/39, 28

  14. 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)

    Mould, R F; Lederman, M; Tai, P; Wong, J K M

    2002-01-01

    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

  15. Prediction model and treatment of high-output ileostomy in colorectal cancer surgery.

    Science.gov (United States)

    Fujino, Shiki; Miyoshi, Norikatsu; Ohue, Masayuki; Takahashi, Yuske; Yasui, Masayoshi; Sugimura, Keijiro; Akita, Hirohumi; Takahashi, Hidenori; Kobayashi, Shogo; Yano, Masahiko; Sakon, Masato

    2017-09-01

    The aim of the present study was to examine the risk factors of high-output ileostomy (HOI), which is associated with electrolyte abnormalities and/or stoma complications, and to create a prediction model. The medical records of 68 patients who underwent colorectal cancer surgery with ileostomy between 2011 and 2016 were retrospectively investigated. All the patients underwent surgical resection for colorectal cancer at the Osaka Medical Center for Cancer and Cardiovascular Diseases (Osaka, Japan). A total of 7 patients with inadequate data on ileostomy output were excluded. Using a group of 50 patients who underwent surgery between 2011 and 2013, the risk of HOI was classified by a decision tree model using a partition platform. The HOI prediction model was validated in an additional group of 11 patients who underwent surgery between 2014 and 2016. Univariate analysis of clinical factors demonstrated that young age (P=0.003) and high white blood cell (WBC) count (Pmodel, three factors (gender, age and WBC on postoperative day 1) were generated for the prediction of HOI. The patients were classified into five groups, and HOI was observed in 0-88% of patients in each group. The area under the curve (AUC) was 0.838. The model was validated by an external dataset in an independent patient group, for which the AUC was 0.792. In conclusion, HOI patients were classified and an HOI prediction model was developed that may help clinicians in postoperative care.

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

    International Nuclear Information System (INIS)

    Bilbao, Cristina; Lara, Pedro Carlos; Ramirez, Raquel; Henriquez-Hernandez, Luis Alberto; Rodriguez, German; Falcon, Orlando; Leon, Laureano; Perucho, Manuel

    2010-01-01

    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.

  17. Risk score predicts high-grade prostate cancer in DNA-methylation positive, histopathologically negative biopsies.

    Science.gov (United States)

    Van Neste, Leander; Partin, Alan W; Stewart, Grant D; Epstein, Jonathan I; Harrison, David J; Van Criekinge, Wim

    2016-09-01

    Prostate cancer (PCa) diagnosis is challenging because efforts for effective, timely treatment of men with significant cancer typically result in over-diagnosis and repeat biopsies. The presence or absence of epigenetic aberrations, more specifically DNA-methylation of GSTP1, RASSF1, and APC in histopathologically negative prostate core biopsies has resulted in an increased negative predictive value (NPV) of ∼90% and thus could lead to a reduction of unnecessary repeat biopsies. Here, it is investigated whether, in methylation-positive men, DNA-methylation intensities could help to identify those men harboring high-grade (Gleason score ≥7) PCa, resulting in an improved positive predictive value. Two cohorts, consisting of men with histopathologically negative index biopsies, followed by a positive or negative repeat biopsy, were combined. EpiScore, a methylation intensity algorithm was developed in methylation-positive men, using area under the curve of the receiver operating characteristic as metric for performance. Next, a risk score was developed combining EpiScore with traditional clinical risk factors to further improve the identification of high-grade (Gleason Score ≥7) cancer. Compared to other risk factors, detection of DNA-methylation in histopathologically negative biopsies was the most significant and important predictor of high-grade cancer, resulting in a NPV of 96%. In methylation-positive men, EpiScore was significantly higher for those with high-grade cancer detected upon repeat biopsy, compared to those with either no or low-grade cancer. The risk score resulted in further improvement of patient risk stratification and was a significantly better predictor compared to currently used metrics as PSA and the prostate cancer prevention trial (PCPT) risk calculator (RC). A decision curve analysis indicated strong clinical utility for the risk score as decision-making tool for repeat biopsy. Low DNA-methylation levels in PCa-negative biopsies led

  18. Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.

    Directory of Open Access Journals (Sweden)

    Herman F Fumiã

    Full Text Available A Boolean dynamical system integrating the main signaling pathways involved in cancer is constructed based on the currently known protein-protein interaction network. This system exhibits stationary protein activation patterns--attractors--dependent on the cell's microenvironment. These dynamical attractors were determined through simulations and their stabilities against mutations were tested. In a higher hierarchical level, it was possible to group the network attractors into distinct cell phenotypes and determine driver mutations that promote phenotypic transitions. We find that driver nodes are not necessarily central in the network topology, but at least they are direct regulators of central components towards which converge or through which crosstalk distinct cancer signaling pathways. The predicted drivers are in agreement with those pointed out by diverse census of cancer genes recently performed for several human cancers. Furthermore, our results demonstrate that cell phenotypes can evolve towards full malignancy through distinct sequences of accumulated mutations. In particular, the network model supports routes of carcinogenesis known for some tumor types. Finally, the Boolean network model is employed to evaluate the outcome of molecularly targeted cancer therapies. The major find is that monotherapies were additive in their effects and that the association of targeted drugs is necessary for cancer eradication.

  19. 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 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. © 2016 UICC.

  20. An etiologic prediction model incorporating biomarkers to predict the bladder cancer risk associated with occupational exposure to aromatic amines: a pilot study.

    Science.gov (United States)

    Mastrangelo, Giuseppe; Carta, Angela; Arici, Cecilia; Pavanello, Sofia; Porru, Stefano

    2017-01-01

    No etiological prediction model incorporating biomarkers is available to predict bladder cancer risk associated with occupational exposure to aromatic amines. Cases were 199 bladder cancer patients. Clinical, laboratory and genetic data were predictors in logistic regression models (full and short) in which the dependent variable was 1 for 15 patients with aromatic amines related bladder cancer and 0 otherwise. The receiver operating characteristics approach was adopted; the area under the curve was used to evaluate discriminatory ability of models. Area under the curve was 0.93 for the full model (including age, smoking and coffee habits, DNA adducts, 12 genotypes) and 0.86 for the short model (including smoking, DNA adducts, 3 genotypes). Using the "best cut-off" of predicted probability of a positive outcome, percentage of cases correctly classified was 92% (full model) against 75% (short model). Cancers classified as "positive outcome" are those to be referred for evaluation by an occupational physician for etiological diagnosis; these patients were 28 (full model) or 60 (short model). Using 3 genotypes instead of 12 can double the number of patients with suspect of aromatic amine related cancer, thus increasing costs of etiologic appraisal. Integrating clinical, laboratory and genetic factors, we developed the first etiologic prediction model for aromatic amine related bladder cancer. Discriminatory ability was excellent, particularly for the full model, allowing individualized predictions. Validation of our model in external populations is essential for practical use in the clinical setting.

  1. External validation of models predicting the individual risk of metachronous peritoneal carcinomatosis from colon and rectal cancer.

    Science.gov (United States)

    Segelman, J; Akre, O; Gustafsson, U O; Bottai, M; Martling, A

    2016-04-01

    To externally validate previously published predictive models of the risk of developing metachronous peritoneal carcinomatosis (PC) after resection of nonmetastatic colon or rectal cancer and to update the predictive model for colon cancer by adding new prognostic predictors. Data from all patients with Stage I-III colorectal cancer identified from a population-based database in Stockholm between 2008 and 2010 were used. We assessed the concordance between the predicted and observed probabilities of PC and utilized proportional-hazard regression to update the predictive model for colon cancer. When applied to the new validation dataset (n = 2011), the colon and rectal cancer risk-score models predicted metachronous PC with a concordance index of 79% and 67%, respectively. After adding the subclasses of pT3 and pT4 stage and mucinous tumour to the colon cancer model, the concordance index increased to 82%. In validation of external and recent cohorts, the predictive accuracy was strong in colon cancer and moderate in rectal cancer patients. The model can be used to identify high-risk patients for planned second-look laparoscopy/laparotomy for possible subsequent cytoreductive surgery and hyperthermic intraperitoneal chemotherapy. Colorectal Disease © 2015 The Association of Coloproctology of Great Britain and Ireland.

  2. 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)

    La Macchia, Mariangela; Fellin, Francesco; Amichetti, Maurizio; Cianchetti, Marco; Gianolini, Stefano; Paola, Vitali; Lomax, Antony J; Widesott, Lamberto

    2012-01-01

    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

  3. Incremental validity of proactive personality over the Big Five for predicting job performance of software engineers in an innovative context

    Directory of Open Access Journals (Sweden)

    Nuno Rodrigues

    2013-01-01

    Full Text Available Este estudio examina la validez añadida de la personalidad proactiva sobre los «cinco grandes» para predecir el desempeño en el trabajo en el contexto de un puesto de trabajo de ingeniero de software. La personalidad proactiva y los «cinco grandes» fueron medidos en una muestra de 243 ingenieros y el desempeño global fue evaluado mediante valoraciones del supervisor en una sub-muestra de 95 de estos ingenieros. Los resultados mostraron que aun cuando la personalidad proactiva representa un importante y válido predictor del desempeño no muestra un incremento relevante en la predicción producida por la extraversión, apertura, conciencia, estabilidad emocional y antigüedad en el puesto. Se discuten las implicaciones, la relevancia y el valor práctico de la personalidad proactiva para la selección de personal.

  4. Histone demethylase GASC1 - a potential prognostic and predictive marker in invasive breast cancer

    International Nuclear Information System (INIS)

    Berdel, Bozena; Nieminen, Kaisa; Soini, Ylermi; Tengström, Maria; Malinen, Marjo; Kosma, Veli-Matti; Palvimo, Jorma J; Mannermaa, Arto

    2012-01-01

    The histone demethylase GASC1 (JMJD2C) is an epigenetic factor suspected of involvement in development of different cancers, including breast cancer. It is thought to be overexpressed in the more aggressive breast cancer types based on mRNA expression studies on cell lines and meta analysis of human breast cancer sets. This study aimed to evaluate the prognostic and predictive value of GASC1 for women with invasive breast cancer. All the 355 cases were selected from a cohort enrolled in the Kuopio Breast Cancer Project between April 1990 and December 1995. The expression of GASC1 was studied by immunohistochemistry (IHC) on tissue microarrays. Additionally relative GASC1 mRNA expression was measured from available 57 cases. In our material, 56% of the cases were GASC1 negative and 44% positive in IHC staining. Women with GASC1 negative tumors had two years shorter breast cancer specific survival and time to relapse than the women with GASC1 positive tumors (p=0.017 and p=0.034 respectively). The majority of GASC1 negative tumors were ductal cases (72%) of higher histological grade (84% of grade II and III altogether). When we evaluated estrogen receptor negative and progesterone receptor negative cases separately, there was 2 times more GASC1 negative than GASC1 positive tumors in each group (chi2, p= 0.033 and 0.001 respectively). In the HER2 positive cases, there was 3 times more GASC1 negative cases than GASC1 positives (chi2, p= 0.029). Patients treated with radiotherapy (n=206) and hormonal treatment (n=62) had better breast cancer specific survival, when they were GASC1 positive (Cox regression: HR=0.49, p=0.007 and HR=0.33, p=0.015, respectively). The expression of GASC1 mRNA was in agreement with the protein analysis. This study indicates that the GASC1 is both a prognostic and a predictive factor for women with invasive breast cancer. GASC1 negativity is associated with tumors of more aggressive histopathological types (ductal type, grade II and III, ER

  5. 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

    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...... 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...

  6. Lgr5 Methylation in Cancer Stem Cell Differentiation and Prognosis-Prediction in Colorectal Cancer.

    Directory of Open Access Journals (Sweden)

    Shasha Su

    Full Text Available Leucine-rich-repeat-containing G-protein-coupled receptor 5 (lgr5 is a candidate marker for colorectal cancer stem cells (CSC. In the current study, we investigated the methylation status within thelgr5 promoter and evaluated its relationship with CSC differentiation, prognosis for colorectal cancer, and its clinicopathological features.The methylation status within Lgr5 promoter was detected with a methylation-specific PCR in six colorectal cancer cell lines as well as 169 primary colorectal tumor tissues. Differentiation of CSC was examined with immunofluorescence and immunocytochemistry. Down-regulation of lgr5 was achieved with gene-specific siRNA. The associations between lgr5 methylation and the clinicopathological features as well as survival of patients were analyzed with statistical methods.The lgr5 promoter was methylated to different degrees for the six colorectal cell lines examined, with complete methylation observed in HCT116 cells in which the lgr5 expression was partially recovered following DAC treatment. The stem-cell sphere formation from HCT116 cells was accompanied by increasing methylation within the lgr5 promoter and decreasing expression of lgr5. Knocking down lgr5 by siRNA also led to stem-cell spheres formation. Among primary colorectal tumors, 40% (67/169 were positive for lgr5 methylation, while none of the normal colon tissues were positive for lgr5 methylation. Furthermore, lgr5 methylation significantly associated with higher tumor grade, and negative distant metastasis (p < 0.05, as well as better prognosis (p = 0.001 in patients with colorectal cancer.Our data suggests that lgr5 methylation, through the regulation of lgr5 expression and colorectal CSC differentiation, may constitute a novel prognostic marker for colorectal cancer patients.

  7. The Glasgow Prognostic Score Predicts Response to Chemotherapy in Patients with Metastatic Breast Cancer.

    Science.gov (United States)

    Wang, Dexing; Duan, Li; Tu, Zhiquan; Yan, Fei; Zhang, Cuicui; Li, Xu; Cao, Yuzhu; Wen, Hongsheng

    2016-01-01

    Breast cancer is one of the most common causes of cancer death in women worldwide. The Glasgow Prognostic Score (GPS), a cumulative prognostic score based on C-reactive protein and albumin, indicates the presence of a systemic inflammatory response. The GPS has been adopted as a powerful prognostic tool for patients with various types of malignant tumors, including breast cancer. The aim of this study was to assess the value of the GPS in predicting the response and toxicity in breast cancer patients treated with chemotherapy. Patients with metastatic breast cancers in a progressive stage for consideration of chemotherapy were eligible. The clinical characteristics and demographics were recorded. The GPS was calculated before the onset of chemotherapy. Data on the response to chemotherapy and progression-free survival (PFS) were also collected. Objective tumor responses were evaluated according to Response Evaluation Criteria in Solid Tumors (RECIST). Toxicities were graded according to National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTC) version 3.0 throughout therapy. In total, 106 breast cancer patients were recruited. The GPS was associated with the response rate (p = 0.05), the clinical benefit rate (p = 0.03), and PFS (p = 0.005). The GPS was the only independent predictor of PFS (p = 0.005). The GPS was significantly associated with neutropenia, thrombocytopenia, anorexia, nausea and vomiting, fatigue, and mucositis (p = 0.05-0.001). Our data demonstrate that GPS assessment is associated with poor clinical outcomes and severe chemotherapy-related toxicities in patients with metastatic breast cancer who have undergone chemotherapy, without any specific indication regarding the type of chemotherapy applied. © 2016 S. Karger AG, Basel.

  8. 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.

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

    International Nuclear Information System (INIS)

    Hirose, Yoshiaki; Imaeda, Takeyoshi; Doi, Hidetaka; Kokubo, Mitsuharu; Sakai, Satoshi; Hirose, Hajime

    1993-01-01

    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 99m Tc-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)

  10. 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

  11. 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

    2015-01-01

    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...... were included, and 475 (88%) were available for analysis at 1 year. At 1-year follow-up, the prevalence of moderate-to-severe pain at rest was 14% and during movement was 7%. Factors associated with pain at rest were age breast conserving surgery (OR: 2.0, P...

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

    Directory of Open Access Journals (Sweden)

    Chaya S. Moskowitz

    2014-07-01

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

  13. What do predict anxiety and depression in breast cancer patients? A follow-up study.

    Science.gov (United States)

    Vahdaninia, Mariam; Omidvari, Sepideh; Montazeri, Ali

    2010-03-01

    Psychological adjustment following cancer occurrence remains a key issue among the survivors. This study aimed to investigate psychological distress in patients with breast cancer following completion of breast cancer treatments and to determine its associated factors. This was a prospective study of anxiety and depression in breast cancer patients. Anxiety and depression were measured using the Hospital Anxiety and Depression Scale at three points in time: baseline (pre-diagnosis), 3 months after initial treatment and 1 year after completion of treatment (in all 18 months follow-up). At baseline, the questionnaires were administered to all the suspected patients while both patients and the interviewer were blind to the final diagnosis. Socio-demographic and clinical data included age, education, marital status, disease stage and initial treatment. Repeated measure analysis was performed to compare anxiety and depression over the study period. Logistic regression analysis was performed to determine variables that predict anxiety and depression. Altogether 167 patients were diagnosed with breast cancer. The mean age of breast cancer patients was 47.2 (SD = 13.5) years, and the vast majority underwent mastectomy (82.6%). At 18 months follow-up, data for 99 patients were available. The results showed that anxiety and depression improved over the time (P < 0.001) although at 18-month follow-up, 38.4% and 22.2% of the patients presented with severe anxiety and depression, respectively. 'Fatigue' was found to be a risk factor for developing anxiety and depression at 3 months follow-up [odds ratio (OR) = 1.04, 95% Confidence interval (CI) = 1.01-1.07 and OR = 1.04, 95% CI = 1.02-1.07 respectively]. At 18 months follow-up, anxiety was predicted by 'pain' (OR = 1.02, 95% CI = 1.00-1.05), whereas depression was predicted by both 'fatigue' (OR = 1.06, 95% CI = 1.02-1.09) and 'pain' (OR = 1.05, 95% CI = 1.01-1.08). Although the findings indicated that the levels of anxiety and

  14. Space Radiation Environment Prediction for VLSI microelectronics devices onboard a LEO Satellite using OMERE-Trad Software

    Science.gov (United States)

    Sajid, Muhammad

    This tutorial/survey paper presents the assessment/determination of level of hazard/threat to emerging microelectronics devices in Low Earth Orbit (LEO) space radiation environment with perigee at 300 Km, apogee at 600Km altitude having different orbital inclinations to predict the reliability of onboard Bulk Built-In Current Sensor (BBICS) fabricated in 350nm technology node at OptMA Lab. UFMG Brazil. In this context, the various parameters for space radiation environment have been analyzed to characterize the ionizing radiation environment effects on proposed BBICS. The Space radiation environment has been modeled in the form of particles trapped in Van-Allen radiation belts(RBs), Energetic Solar Particles Events (ESPE) and Galactic Cosmic Rays (GCR) where as its potential effects on Device- Under-Test (DUT) has been predicted in terms of Total Ionizing Dose (TID), Single-Event Effects (SEE) and Displacement Damage Dose (DDD). Finally, the required mitigation techniques including necessary shielding requirements to avoid undesirable effects of radiation environment at device level has been estimated /determined with assumed standard thickness of Aluminum shielding. In order to evaluate space radiation environment and analyze energetic particles effects on BBICS, OMERE toolkit developed by TRAD was utilized.

  15. Predictive factors for malignancy in incidental pulmonary nodules detected in breast cancer patients at baseline CT

    Energy Technology Data Exchange (ETDEWEB)

    Hammer, Mark M.; Mortani Barbosa, Eduardo J. [University of Pennsylvania, Division of Cardiothoracic Imaging, Department of Radiology, Perelman School of Medicine, Philadelphia, PA (United States)

    2017-07-15

    Pulmonary nodules are commonly encountered at staging CTs in patients with extrathoracic malignancies, but their significance on a per-patient basis remains uncertain. We undertook a retrospective analysis of pulmonary nodules identified in patients with a diagnosis of breast cancer from 2010 - 2015, evaluating nodules present at a baseline CT (i.e. prevalent nodules). We reviewed 211 patients with 248 individual nodules. The rate of malignancy in prevalent nodules is low, approximately 13 %. Variables associated with metastasis include pleural studding, hilar lymphadenopathy and the presence of extrapulmonary metastasis, as well as number of nodules, nodule size and nodule shape. Using a combination of these factors, we have developed an evidence-based multivariate decision tree to predict which nodules are malignant in these patients, which is 91 % accurate and 100 % sensitive for metastasis. We propose a simplified clinical prediction algorithm to guide radiologists and oncologists in managing patients with breast cancer and incidental pulmonary nodules. (orig.)

  16. Is it possible to predict low-volume and insignificant prostate cancer by core needle biopsies?

    DEFF Research Database (Denmark)

    Berg, Kasper Drimer; Toft, Birgitte Grønkaer; Røder, Martin Andreas

    2013-01-01

    M: tumour ≤5% of total prostate volume and prostate-specific antigen (PSA) ≤10 ng/mL. In all definitions, Gleason score (GS) was ≤6 and the tumour was organ confined. Biopsies alone performed poorly as a predictor of unifocal and unilateral cancer in the prostatectomy specimens with positive predictive......In an attempt to minimize overtreatment of localized prostate cancer (PCa) active surveillance (AS) and minor invasive procedures have received increased attention. We investigated the accuracy of pre-operative findings in defining insignificant disease and distinguishing between unilateral.......9% and 12.0%, respectively, for identifying InsigM, InsigW and InsigE in the prostate specimen. Conclusively, routine prostate biopsies cannot predict unifocal and unilateral PCa, and must be regarded insufficient to select patients for focal therapy. Although candidates for AS may be identified using...

  17. 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

    PURPOSE: To investigate the prognostic and predictive biomarker value of type IV collagen in colorectal cancer. EXPERIMENTAL DESIGN: Retrospective evaluation of two independent cohorts of patients with colorectal cancer included prospectively in 2004-2005 (training set) and 2006-2008 (validation....... RESULTS: High levels of type IV collagen showed independent prognostic significance in both cohorts with hazard ratios (HRs; for a one-unit change on the log base 2 scale) of 2.25 [95% confidence intervals (CIs), 1.78-2.84; P ... and 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...

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

    International Nuclear Information System (INIS)

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

    2015-01-01

    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. 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 and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC. A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction. Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model

  19. Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy.

    Science.gov (United States)

    Mani, Subramani; Chen, Yukun; Li, Xia; Arlinghaus, Lori; Chakravarthy, A Bapsi; Abramson, Vandana; Bhave, Sandeep R; Levy, Mia A; Xu, Hua; Yankeelov, Thomas E

    2013-01-01

    To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building. The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82. With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem. Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC.

  20. Factors Predictive of Sentinel Lymph Node Involvement in Primary Breast Cancer.

    Science.gov (United States)

    Malter, Wolfram; Hellmich, Martin; Badian, Mayhar; Kirn, Verena; Mallmann, Peter; Krämer, Stefan

    2018-06-01

    Sentinel lymph node biopsy (SLNB) has replaced axillary lymph node dissection (ALND) for axillary staging in patients with early-stage breast cancer. The need for therapeutic ALND is the subject of ongoing debate especially after the publication of the ACOSOG Z0011 trial. In a retrospective trial with univariate and multivariate analyses, factors predictive of sentinel lymph node involvement should be analyzed in order to define tumor characteristics of breast cancer patients, where SLNB should not be spared to receive important indicators for adjuvant treatment decisions (e.g. thoracic wall irradiation after mastectomy with or without reconstruction). Between 2006 and 2010, 1,360 patients with primary breast cancer underwent SLNB with/without ALND with evaluation of tumor localization, multicentricity and multifocality, histological subtype, tumor size, grading, lymphovascular invasion (LVI), and estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status. These characteristics were retrospectively analyzed in univariate and multivariate logistic regression models to define significant predictive factors for sentinel lymph node involvement. The multivariate analysis demonstrated that tumor size and LVI (pbreast cancer. Because of the increased risk for metastatic involvement of axillary sentinel nodes in cases with larger breast cancer or diagnosis of LVI, patients with these breast cancer characteristics should not be spared from SLNB in a clinically node-negative situation in order to avoid false-negative results with a high potential for wrong indication of primary breast reconstruction or wrong non-indication of necessary post-mastectomy radiation therapy. The prognostic impact of avoidance of axillary staging with SLNB is analyzed in the ongoing prospective INSEMA trial. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

  1. 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)

    Svanda, J.; Hustakova, H.; Fiser, V.

    2008-01-01

    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.)

  2. Software engineering

    CERN Document Server

    Sommerville, Ian

    2010-01-01

    The ninth edition of Software Engineering presents a broad perspective of software engineering, focusing on the processes and techniques fundamental to the creation of reliable, software systems. Increased coverage of agile methods and software reuse, along with coverage of 'traditional' plan-driven software engineering, gives readers the most up-to-date view of the field currently available. Practical case studies, a full set of easy-to-access supplements, and extensive web resources make teaching the course easier than ever.

  3. The impact of predictive genetic testing for hereditary nonpolyposis colorectal cancer: three years after testing.

    Science.gov (United States)

    Collins, Veronica R; Meiser, Bettina; Ukoumunne, Obioha C; Gaff, Clara; St John, D James; Halliday, Jane L

    2007-05-01

    To fully assess predictive genetic testing programs, it is important to assess outcomes over periods of time longer than the 1-year follow-up reported in the literature. We conducted a 3-year study of individuals who received predictive genetic test results for previously identified familial mutations in Australian Familial Cancer Clinics. Questionnaires were sent before attendance at the familial cancer clinic and 2 weeks, 4 months, 1 year, and 3 years after receiving test results. Psychological measures were included each time, and preventive behaviors were assessed at baseline and 1 and 3 years. Psychological measures were adjusted for age, gender, and baseline score. The study included 19 carriers and 54 non-carriers. We previously reported an increase in mean cancer-specific distress in carriers at 2 weeks with a return to baseline levels by 12 months. This level was maintained until 3 years. Non-carriers showed sustained decreases after testing with a significantly lower level at 3 years compared with baseline (P depression and anxiety scores did not differ between carriers and non-carriers and, at 3 years, were similar to baseline. All carriers and 7% of non-carriers had had a colonoscopy by 3 years, and 69% of 13 female carriers had undergone gynecological screening in the previous 2 years. Prophylactic surgery was rare. This report of long-term data indicates appropriate screening and improved psychological measures for non-carriers with no evidence of undue psychological distress in carriers of hereditary nonpolyposis colorectal cancer mutations.

  4. Effectiveness of gene expression profiling for response prediction of rectal cancer to preoperative radiotherapy

    International Nuclear Information System (INIS)

    Ojima, Eiki; Inoue, Yasuhiro; Miki, Chikao; Kusunoki, Masato; Mori, Masaki

    2007-01-01

    Our aim was to determine whether the expression levels of specific genes could predict clinical radiosensitivity in human colorectal cancer. Radioresistant colorectal cancer cell lines were established by repeated X-ray exposure (total, 100 Gy), and the gene expressions of the parent and radioresistant cell lines were compared in a microarray analysis. To verify the microarray data, we carried out a reverse transcriptase-polymerase chain reaction analysis of identified genes in clinical samples from 30 irradiated rectal cancer patients. A comparison of the intensity data for the parent and three radioresistant cell lines revealed 17 upregulated and 142 downregulated genes in all radioresistant cell lines. Next, we focused on two upregulated genes, PTMA (prothymosin α) and EIF5a2 (eukaryotic translation initiation factor 5A), in the radioresistant cell lines. In clinical samples, the expression of PTMA was significantly higher in the minor effect group than in the major effect group (P=0.004), but there were no significant differences in EIF5a2 expression between the two groups. We identified radiation-related genes in colorectal cancer and demonstrated that PTMA may play an important role in radiosensitivity. Our findings suggest that PTMA may be a novel marker for predicting the effectiveness of radiotherapy in clinical cases. (author)

  5. Predictive and Prognostic Value of sPRR in Patients with Primary Epithelial Ovarian Cancer

    Directory of Open Access Journals (Sweden)

    Katrin Kreienbring

    2016-01-01

    Full Text Available Aim. The purpose of the present study was to analyze the predictive and prognostic role of soluble (prorenin receptor (sPRR as a biomarker for clinicopathological outcome in patients with primary epithelial ovarian cancer (EOC. As part of the renin-angiotensin system (RAS whose activity is known to increase in ovarian cancer patients, the relation of sPRR and ovarian cancer should be further investigated. Patients and Methods. In this study 197 patients with primary EOC in our institution from 2000 to 2011 were included. sPRR was determined by enzyme-linked immunosorbent assay (ELISA in preoperative taken blood sera. Associations with clinicopathological outcome were analyzed and serum levels of sPRR in patients have been compared to those in healthy specimen. Kaplan-Meier and logistic/Cox regression assessed the impact of the markers on progression-free survival (PFS and overall survival (OS. Results. There have been no correlations proved of sPRR levels with neither clinicopathological factors nor prognostic data. Also the distribution of sPRR in patients and controls was normal. Conclusion. sPRR seems to have no predictive, prognostic, or diagnostic value in EOC. As several factors of the RAS which might indicate cancer events have been shown, sPRR seems not to be affected.

  6. 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...

  7. IGFBP3 methylation is a novel diagnostic and predictive biomarker in colorectal cancer.

    Directory of Open Access Journals (Sweden)

    Lucia Perez-Carbonell

    Full Text Available Aberrant hypermethylation of cancer-related genes has emerged as a promising strategy for the development of diagnostic, prognostic and predictive biomarkers in human cancer, including colorectal cancer (CRC. The aim of this study was to perform a systematic and comprehensive analysis of a panel of CRC-specific genes as potential diagnostic, prognostic and predictive biomarkers in a large, population-based CRC cohort.Methylation status of the SEPT9, TWIST1, IGFBP3, GAS7, ALX4 and miR137 genes was studied by quantitative bisulfite pyrosequencing in a population-based cohort of 425 CRC patients.Methylation levels of all genes analyzed were significantly higher in tumor tissues compared to normal mucosa (p<0.0001; however, cancer-associated hypermethylation was most frequently observed for miR137 (86.7% and IGFBP3 (83% in CRC patients. Methylation analysis using the combination of these two genes demonstrated greatest accuracy for the identification of colonic tumors (sensitivity 95.5%; specificity 90.5%. Low levels of IGFBP3 promoter methylation emerged as an independent risk factor for predicting poor disease free survival in stage II and III CRC patients (HR = 0.49, 95% CI: 0.28-0.85, p = 0.01. Our results also suggest that stage II & III CRC patients with high levels of IGFBP3 methylation do not benefit from adjuvant 5FU-based chemotherapy.By analyzing a large, population-based CRC cohort, we demonstrate the potential clinical significance of miR137 and IGFBP3 hypermethylation as promising diagnostic biomarkers in CRC. Our data also revealed that IGFBP3 hypermethylation may serve as an independent prognostic and predictive biomarker in stage II and III CRC patients.

  8. STRAP Is a Strong Predictive Marker of Adjuvant Chemotherapy Benefit in Colorectal Cancer

    OpenAIRE

    Buess, Martin; Terracciano, Luigi; Reuter, Jurgen; Ballabeni, Pierluigi; Boulay, Jean-Louis; Laffer, Urban; Metzger, Urs; Herrmann, Richard; Rochlitz, Christoph

    2004-01-01

    BACKGROUND: Molecular predictors for the effectiveness of adjuvant chemotherapy in colorectal cancer are of considerable clinical interest. To this aim, we analyzed the serine threonine receptor-associated protein (STRAP), an inhibitor of TGF-βsignaling, with regard to prognosis and prediction of adjuvant 5-FU chemotherapy benefit. i The gene copy status of STRAP was determined using quantitative realtime polymerase chain reaction in 166 colorectal tumor biopsies, which had been collected fro...

  9. Prediction model to predict critical weight loss in patients with head and neck cancer during (chemo)radiotherapy.

    Science.gov (United States)

    Langius, Jacqueline A E; Twisk, Jos; Kampman, Martine; Doornaert, Patricia; Kramer, Mark H H; Weijs, Peter J M; Leemans, C René

    2016-01-01

    Patients with head and neck cancer (HNC) frequently encounter weight loss with multiple negative outcomes as a consequence. Adequate treatment is best achieved by early identification of patients at risk for critical weight loss. The objective of this study was to detect predictive factors for critical weight loss in patients with HNC receiving (chemo)radiotherapy ((C)RT). In this cohort study, 910 patients with HNC were included receiving RT (±surgery/concurrent chemotherapy) with curative intent. Body weight was measured at the start and end of (C)RT. Logistic regression and classification and regression tree (CART) analyses were used to analyse predictive factors for critical weight loss (defined as >5%) during (C)RT. Possible predictors included gender, age, WHO performance status, tumour location, TNM classification, treatment modality, RT technique (three-dimensional conformal RT (3D-RT) vs intensity-modulated RT (IMRT)), total dose on the primary tumour and RT on the elective or macroscopic lymph nodes. At the end of (C)RT, mean weight loss was 5.1±4.9%. Fifty percent of patients had critical weight loss during (C)RT. The main predictors for critical weight loss during (C)RT by both logistic and CART analyses were RT on the lymph nodes, higher RT dose on the primary tumour, receiving 3D-RT instead of IMRT, and younger age. Critical weight loss during (C)RT was prevalent in half of HNC patients. To predict critical weight loss, a practical prediction tree for adequate nutritional advice was developed, including the risk factors RT to the neck, higher RT dose, 3D-RT, and younger age. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Caveolin-1 expression level in cancer associated fibroblasts predicts outcome in gastric cancer.

    Directory of Open Access Journals (Sweden)

    Xianda Zhao

    Full Text Available 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.

  11. CT volumetry can potentially predict the local stage for gastric cancer after chemotherapy

    Science.gov (United States)

    Wang, Zhi-Cong; Wang, Chen; Ding, Ying; Ji, Yuan; Zeng, Meng-Su; Rao, Sheng-Xiang

    2017-01-01

    PURPOSE We aimed to evaluate the value of CT tumor volumetry for predicting T and N stages of gastric cancer after chemotherapy, with pathologic results as the reference standard. METHODS This study retrospectively evaluated 42 patients diagnosed with gastric cancer, who underwent chemotherapy followed by surgery. Pre- and post-treatment CT tumor volumes (VT) were measured in portal venous phase and volume reduction ratios were calculated. Correlations between pre- and post-treatment VT, reduction ratio, and pathologic stages were analyzed. Receiver operator characteristic (ROC) analyses were also performed to assess diagnostic performance for prediction of downstaging to T0–2 stage and N0 stage. RESULTS Pretreatment VT, post-treatment VT, and VT reduction ratio were significantly correlated with T stage (rs=0.329, rs=0.546, rs= −0.422, respectively). Post-treatment VT and VT reduction ratio were significantly correlated with N stage (rs=0.442 and rs= −0.376, respectively). Pretreatment VT, post-treatment VT, and VT reduction ratio were significantly different between T0–2 and T3,4 stage tumors (P = 0.05, P volumetry, particularly post-treatment measurement of VT, is potentially valuable for predicting histopathologic T and N stages after chemotherapy in patients with gastric cancer. PMID:28703101

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

    International Nuclear Information System (INIS)

    Soerdjbalie-Maikoe, Vidija; Pelger, Rob C.M.; Nijeholt, Guus A.B. Lycklama; Arndt, Jan-Willem; Zwinderman, Aeilko H.; Bril, Herman; Papapoulos, Socrates E.; Hamdy, Neveen A.T.

    2004-01-01

    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.)

  13. The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

    Science.gov (United States)

    Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

    2014-06-01

    With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

  14. 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)

    Gigante, L.; Dellavedova, M.; Pasturenzi, C.; Lunghi, A.; Cardillo, P.

    2008-01-01

    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 (L i ), the limiting oxygen concentration (LOC, using nitrogen as inert gas) as a function of temperature, the adiabatic flame temperature T flame , the fundamental burning velocity (S u ), the quenching distance (Q d ), the minimum ignition energy (MIE) for esters and ethers, substances highly used in industry. [it

  15. ["Screening" in special situations. Assessing predictive genetic screening for hereditary breast and colorectal cancer].

    Science.gov (United States)

    Jonas, Susanna; Wild, Claudia; Schamberger, Chantal

    2003-02-01

    The aim of this health technology assessment was to analyse the current scientific and genetic counselling on predictive genetic testing for hereditary breast and colorectal cancer. Predictive genetic testing will be available for several common diseases in the future and questions related to financial issues and quality standards will be raised. This report is based on a systematic/nonsystematic literature search in several databases (e.g. EmBase, Medline, Cochrane Library) and on a specific health technology assessment report (CCOHTA) and review (American Gastroenterological Ass.), respectively. Laboratory test methods, early detection methods and the benefit from prophylactic interventions were analysed and social consequences interpreted. Breast and colorectal cancer are counted among the most frequently cancer diseases. Most of them are based on random accumulation of risk factors, 5-10% show a familial determination. A hereditary modified gene is responsible for the increased cancer risk. In these families, high tumour frequency, young age at diagnosis and multiple primary tumours are remarkable. GENETIC DIAGNOSIS: Sequence analysis is the gold standard. Denaturing high performance liquid chromatography is a quick alternative method. The identification of the responsible gene defect in an affected family member is important. If the test result is positive there is an uncertainty whether the disease will develop or not, when and in which degree, which is founded in the geno-/phenotype correlation. The individual risk estimation is based upon empirical evidence. The test results affect the whole family. Currently, primary prevention is possible for familial adenomatous polyposis (celecoxib, prophylactic colectomy) and for hereditary mamma carcinoma (prophylactic mastectomy). The so-called preventive medical check-ups are early detection examinations. The evidence about early detection methods for colorectal cancer is better than for breast cancer. Prophylactic

  16. Urinary EN-2 to predict prostate cancer: Systematic review and meta-analysis

    Directory of Open Access Journals (Sweden)

    Maria Inês da Rosa

    Full Text Available Summary Introduction: Prostate cancer is the second type of cancer diagnosed and the fifth cause of death in men worldwide. Early diagnosis helps to control disease progression. Currently, prostate specific antigen is the standard biomarker, as it has a broad scope of identification and, thus, new and more specific biomarkers must be studied. Objective: To evaluate the accuracy of engrailed-2 protein (EN2 in urine as a prostate cancer biomarker. Method: A comprehensive search was conducted in the period from January 2005 to July 2016 using the following electronic databases: Medline (PubMed, Embase, Cochrane Library and Lilacs. The keywords used in the databases were: "engrailed-2," "EN2," "prostatic neoplasms." The search was limited to humans and there was no language restriction. Critical appraisal of the included studies was performed according to Quadas-2. Statistical analysis was performed using Meta-DiSc® and RevMan 5.3 softwares. Results: A total of 248 studies were identified. After title and abstract screening, 231 studies were removed. A total of 17 studies were read in full and two studies were included in the meta-analysis. The pooled sensitivity was 66% (95CI 0.56-0.75 and specificity was 89% (95CI 0.86-0.92. The DOR was 15.08 (95CI 8.43-26.97. Conclusion: The EN2 test showed high specificity (89% and low sensitivity (66%.

  17. 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.

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

    International Nuclear Information System (INIS)

    Mays, C.W.; Taylor, G.N.; Lloyd, R.D.

    1986-01-01

    The toxicity ratio concept assumes the validity of certain relationships. In some examples for bone sarcoma induction, the approximate toxicity of 239 Pu in man can be calculated algebraically from the observed toxicity in the radium-dial painters and the ratio of 239 Pu/ 226 Ra toxicities in suitable laboratory mammals. In a species highly susceptible to bone sarcoma induction, the risk coefficients for both 239 Pu and 226 Ra are elevated, but the toxicity ratio of 239 Pu to 226 Ra tends to be similar to the ratio in resistant species. Among the tested species the toxicity ratio of 239 Pu to 226 Ra ranged from 6 to 22 (a fourfold range), whereas their relative sensitivities to 239 Pu 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

  19. MicroRNAs as biomarkers for early breast cancer diagnosis, prognosis and therapy prediction.

    Science.gov (United States)

    Nassar, Farah J; Nasr, Rihab; Talhouk, Rabih

    2017-04-01

    Breast cancer is a major health problem that affects one in eight women worldwide. As such, detecting breast cancer at an early stage anticipates better disease outcome and prolonged patient survival. Extensive research has shown that microRNA (miRNA) are dysregulated at all stages of breast cancer. miRNA are a class of small noncoding RNA molecules that can modulate gene expression and are easily accessible and quantifiable. This review highlights miRNA as diagnostic, prognostic and therapy predictive biomarkers for early breast cancer with an emphasis on the latter. It also examines the challenges that lie ahead in their use as biomarkers. Noteworthy, this review addresses miRNAs reported in patients with early breast cancer prior to chemotherapy, radiotherapy, surgical procedures or distant metastasis (unless indicated otherwise). In this context, miRNA that are mentioned in this review were significantly modulated using more than one statistical test and/or validated by at least two studies. A standardized protocol for miRNA assessment is proposed starting from sample collection to data analysis that ensures comparative analysis of data and reproducibility of results. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer

    International Nuclear Information System (INIS)

    Karlsson, Elin; Delle, Ulla; Danielsson, Anna; Olsson, Björn; Abel, Frida; Karlsson, Per; Helou, Khalil

    2008-01-01

    It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used for evaluation of the gene lists, a correlation-based classifier and a Voting Features Interval (VFI) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients. They had both developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients may have benefited from different adjuvant treatment than they recieved. The research on these tumours was approved by the Medical Faculty Research

  1. Prostatectomy-based validation of combined urine and plasma test for predicting high grade prostate cancer.

    Science.gov (United States)

    Albitar, Maher; Ma, Wanlong; Lund, Lars; Shahbaba, Babak; Uchio, Edward; Feddersen, Søren; Moylan, Donald; Wojno, Kirk; Shore, Neal

    2018-03-01

    Distinguishing between low- and high-grade prostate cancers (PCa) is important, but biopsy may underestimate the actual grade of cancer. We have previously shown that urine/plasma-based prostate-specific biomarkers can predict high grade PCa. Our objective was to determine the accuracy of a test using cell-free RNA levels of biomarkers in predicting prostatectomy results. This multicenter community-based prospective study was conducted using urine/blood samples collected from 306 patients. All recruited patients were treatment-naïve, without metastases, and had been biopsied, designated a Gleason Score (GS) based on biopsy, and assigned to prostatectomy prior to participation in the study. The primary outcome measure was the urine/plasma test accuracy in predicting high grade PCa on prostatectomy compared with biopsy findings. Sensitivity and specificity were calculated using standard formulas, while comparisons between groups were performed using the Wilcoxon Rank Sum, Kruskal-Wallis, Chi-Square, and Fisher's exact test. GS as assigned by standard 10-12 core biopsies was 3 + 3 in 90 (29.4%), 3 + 4 in 122 (39.8%), 4 + 3 in 50 (16.3%), and > 4 + 3 in 44 (14.4%) patients. The urine/plasma assay confirmed a previous validation and was highly accurate in predicting the presence of high-grade PCa (Gleason ≥3 + 4) with sensitivity between 88% and 95% as verified by prostatectomy findings. GS was upgraded after prostatectomy in 27% of patients and downgraded in 12% of patients. This plasma/urine biomarker test accurately predicts high grade cancer as determined by prostatectomy with a sensitivity at 92-97%, while the sensitivity of core biopsies was 78%. © 2018 Wiley Periodicals, Inc.

  2. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

    Science.gov (United States)

    Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui

    2017-07-15

    New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier

  3. Use of sequential endorectal US to predict the tumor response of preoperative chemoradiotherapy in rectal cancer.

    Science.gov (United States)

    Li, Ning; Dou, Lizhou; Zhang, Yueming; Jin, Jing; Wang, Guiqi; Xiao, Qin; Li, Yexiong; Wang, Xin; Ren, Hua; Fang, Hui; Wang, Weihu; Wang, Shulian; Liu, Yueping; Song, Yongwen

    2017-03-01

    Accurate prediction of the response to preoperative chemoradiotherapy (CRT) potentially assists in the individualized selection of treatment. Endorectal US (ERUS) is widely used for the pretreatment staging of rectal cancer, but its use for preoperatively predicting the effects of CRT is not well evaluated because of the inflammation, necrosis, and fibrosis induced by CRT. This study assessed the value of sequential ERUS in predicting the efficacy of preoperative CRT for locally advanced rectal cancer. Forty-one patients with clinical stage II/III rectal adenocarcinoma were enrolled prospectively. Radiotherapy was delivered to the pelvis with concurrent chemotherapy of capecitabine and oxaliplatin. Total mesorectal excision was performed 6 to 8 weeks later. EUS measurements of primary tumor maximum diameter were performed before (ERUS1), during (ERUS2), and 6 to 8 weeks after (ERUS3) CRT, and the ratios of these were calculated. Correlations between ERUS values, tumor regression grade (TRG), T down-staging rate, and pathologic complete response (pCR) rate were assessed, and survival was analyzed. There was no significant correlation between ERUS2/ERUS1 and TRG. The value of ERUS3/ERUS1 correlated with pCR rate and TRG but not T down-staging rate. An ERUS3 value of 6.3 mm and ERUS3/ERUS1 of 52% were used as the cut-off for predicting pCR, and patients were divided into good and poor prognosis groups. Although not statistically significant, 3-year recurrence and survival rates of the good prognosis group were better than those of the poor prognosis group. Sequential ERUS may predict therapeutic efficacy of preoperative CRT for locally advanced rectal cancer. (Clinical trial registration number: NCT01582750.). Copyright © 2017 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

  4. Postoperative Nomogram Predicting the 10-Year Probability of Prostate Cancer Recurrence After Radical Prostatectomy

    Science.gov (United States)

    Stephenson, Andrew J.; Scardino, Peter T.; Eastham, James A.; Bianco, Fernando J.; Dotan, Zohar A.; DiBlasio, Christopher J.; Reuther, Alwyn; Klein, Eric A.; Kattan, Michael W.

    2007-01-01

    Purpose A postoperative nomogram for prostate cancer recurrence after radical prostatectomy (RP) has been independently validated as accurate and discriminating. We have updated the nomogram by extending the predictions to 10 years after RP and have enabled the nomogram predictions to be adjusted for the disease-free interval that a patient has maintained after RP. Methods Cox regression analysis was used to model the clinical information for 1,881 patients who underwent RP for clinically-localized prostate cancer by two high-volume surgeons. The model was externally validated separately on two independent cohorts of 1,782 patients and 1,357 patients, respectively. Disease progression was defined as a rising prostate-specific antigen (PSA) level, clinical progression, radiotherapy more than 12 months postoperatively, or initiation of systemic therapy. Results The 10-year progression-free probability for the modeling set was 79% (95% CI, 75% to 82%). Significant variables in the multivariable model included PSA (P = .002), primary (P < .0001) and secondary Gleason grade (P = .0006), extracapsular extension (P < .0001), positive surgical margins (P = .028), seminal vesicle invasion (P < .0001), lymph node involvement (P = .030), treatment year (P = .008), and adjuvant radiotherapy (P = .046). The concordance index of the nomogram when applied to the independent validation sets was 0.81 and 0.79. Conclusion We have developed and validated as a robust predictive model an enhanced postoperative nomogram for prostate cancer recurrence after RP. Unique to predictive models, the nomogram predictions can be adjusted for the disease-free interval that a patient has achieved after RP. PMID:16192588

  5. Effect of Imaging Parameter Thresholds on MRI Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes.

    Directory of Open Access Journals (Sweden)

    Wei-Ching Lo

    Full Text Available The purpose of this study is to evaluate the predictive performance of magnetic resonance imaging (MRI markers in breast cancer patients by subtype. Sixty-four patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy were enrolled in this study. Each patient received a dynamic contrast-enhanced (DCE-MRI at baseline, after 1 cycle of chemotherapy and before surgery. Functional tumor volume (FTV, the imaging marker measured by DCE-MRI, was computed at various thresholds of percent enhancement (PEt and signal-enhancement ratio (SERt. Final FTV before surgery and percent changes of FTVs at the early and final treatment time points were used to predict patients' recurrence-free survival. The full cohort and each subtype defined by the status of hormone receptor and human epidermal growth factor receptor 2 (HR+/HER2-, HER2+, triple negative were analyzed. Predictions were evaluated using the Cox proportional hazard model when PEt changed from 30% to 200% in steps of 10% and SERt changed from 0 to 2 in steps of 0.2. Predictions with high hazard ratios and low p-values were considered as strong. Different profiles of FTV as predictors for recurrence-free survival were observed in each breast cancer subtype and strong associations with survival were observed at different PEt/SERt combinations that resulted in different FTVs. Findings from this retrospective study suggest that the predictive performance of imaging markers based on FTV may be improved with enhancement thresholds being optimized separately for clinically-relevant subtypes defined by HR and HER2 receptor expression.

  6. High ASMA+ Fibroblasts and Low Cytoplasmic HMGB1+ Breast Cancer Cells Predict Poor Prognosis.

    Science.gov (United States)

    Amornsupak, Kamolporn; Jamjuntra, Pranisa; Warnnissorn, Malee; O-Charoenrat, Pornchai; Sa-Nguanraksa, Doonyapat; Thuwajit, Peti; Eccles, Suzanne A; Thuwajit, Chanitra

    2017-10-01

    The influence of cancer-associated fibroblasts (CAFs) and high mobility group box 1 (HMGB1) has been recognized in several cancers, although their roles in breast cancer are unclear. The present study aimed to determine the levels and prognostic significance of α-smooth muscle actin-positive (ASMA + ) CAFs, plus HMGB1 and receptor for advanced glycation end products (RAGE) in cancer cells. A total of 127 breast samples, including 96 malignant and 31 benign, were examined for ASMA, HMGB1, and RAGE by immunohistochemistry. The χ 2 test and Fisher's exact test were used to test the association of each protein with clinicopathologic parameters. The Kaplan-Meier method or log-rank test and Cox regression were used for survival analysis. ASMA + fibroblast infiltration was significantly increased in the tumor stroma compared with that in benign breast tissue. The levels of cytoplasmic HMGB1 and RAGE were significantly greater in the breast cancer tissue than in the benign breast tissues. High ASMA expression correlated significantly with large tumor size, clinical stage III-IV, and angiolymphatic and perinodal invasion. In contrast, increased cytoplasmic HMGB1 correlated significantly with small tumor size, pT stage, early clinical stage, luminal subtype (but not triple-negative subtype), and estrogen receptor and progesterone receptor expression. The levels of ASMA (hazard ratio, 14.162; P = .010) and tumor cytoplasmic HMGB1 (hazard ratio, 0.221; P = .005) could serve as independent prognostic markers for metastatic relapse in breast cancer patients. The ASMA-high/HMGB1-low profile provided the most reliable prediction of metastatic relapse. We present for the first time, to the best of our knowledge, the potential clinical implications of the combined assessment of ASMA + fibroblasts and cytoplasmic HMGB1 in breast cancer. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  7. A nomogram for predicting survival in patients with breast cancer brain metastasis.

    Science.gov (United States)

    Huang, Zhou; Sun, Bing; Wu, Shikai; Meng, Xiangying; Cong, Yang; Shen, Ge; Song, Santai

    2018-05-01

    Brain metastasis (BM) is common in patients with breast cancer. Predicting patient survival is critical for the clinical management of breast cancer brain metastasis (BCBM). The present study was designed to develop and evaluate a prognostic model for patients with newly diagnosed BCBM. Based on the clinical data of patients with BCBM treated in the Affiliated Hospital of Academy of Military Medical Sciences (Beijing, China) between 2002 and 2014, a nomogram was developed to predict survival using proportional hazards regression analysis. The model was validated internally by bootstrapping, and the concordance index (c-index) was calculated. A calibration curve and c-index were used to evaluate discriminatory and predictive ability, in order to compare the nomogram with widely used models, including recursive partitioning analysis (RPA), graded prognostic assessment (GPA) and breast-graded prognostic assessment (Breast-GPA). A total of 411 patients with BCBM were included in the development of this predictive model. The median overall survival time was 14.1 months. Statistically significant predictors for patient survival included biological subtype, Karnofsky performance score, leptomeningeal metastasis, extracranial metastasis, the number of brain metastases and disease-free survival. A nomogram for predicting 1- and 2-year overall survival rates was constructed, which exhibited good accuracy in predicting overall survival with a concordance index of 0.735. This model outperformed RPA, GPA and Breast-GPA, based on the comparisons of the c-indexes. The nomogram constructed based on a multiple factor analysis was able to more accurately predict the individual survival probability of patients with BCBM, compared with existing models.

  8. Frequency of reporting and predictive factors for anxiety and depression in patients with advanced cancer.

    Science.gov (United States)

    Salvo, N; Zeng, L; Zhang, L; Leung, M; Khan, L; Presutti, R; Nguyen, J; Holden, L; Culleton, S; Chow, E

    2012-03-01

    The prevalence of anxiety and depression in patients with advanced cancer has been reported to be on average 25% and to significantly affect patients' quality of life. Despite high prevalence rates, these disorders remain underdiagnosed and undertreated. The purpose of our study was to examine the self-report rates of anxiety and depression with the Edmonton Symptom Assessment System (ESAS) and to assess the predictive factors for these reports in cancer patients with metastatic disease. Consecutive patients who attended the Rapid Response Radiotherapy Program (RRRP) completed the ESAS as well as baseline demographic information. Ordinal logistic regression analysis was used to determine factors that significantly predicted anxiety and/or depression. Pearson χ(2) was used to test goodness-of-fit for categorical variables and established whether or not an observed frequency distribution differed from a predicted frequency distribution. A univariate analysis was conducted first and those variables with a P valueanalysis. A score test was used to test the proportional odds assumption. In total, 1439 patients seen in the RRRP between January 1999 and October 2009 completed ESAS questionnaires. Fifty-five per cent of patients reported at least mild symptoms of depression and 65% reported at least mild anxiety. In the univariate analysis, patients who were female, who had a lower performance status score, or primary lung cancer were more likely to report depressed and anxious feelings. Primary prostate cancer patients were significantly less likely to report depression and anxiety. Patients referred for spinal cord compression were significantly less depressed. The multivariate models showed that younger patients were significantly more anxious than older patients and females reported more anxiety than males. Patients who reported higher feelings of nausea, tiredness, drowsiness, dyspnoea, and worse appetite and overall well-being on the ESAS tool were more likely to

  9. Predicting the performances of a CAMPRO engine retrofitted with liquefied petroleum gas (LPG system using 1-dimensional software

    Directory of Open Access Journals (Sweden)

    Kamaruddin M. Hazeem

    2017-01-01

    Full Text Available Recently, the depletion of petroleum resources and the impact of exhaust emission caused by combustion towards environmental has been forced to all researchers to come out with an alternative ways to prevent this situation become worse. Liquefied petroleum gas (LPG is the most compatible and have a potential to become a source of energy for internal combustion engine. Unfortunately, the investigation of LPG in internal combustion engine among researcher still have a gap in research. Thus, in this study a 1-Dimensional simulation CAMPRO 1.6L engine model using GT-Power is developed to predict the performances of engines that using LPG as a fuel for internal combustion engine. The constructed model simulation will throughout the validation process with the experimental data to make sure the precision of this model. The validation process shows that the results have a good agreement between the simulation model and the experimental data. As a result, the performance of LPG simulation model shows that a Brake Torque (BT, Brake Power (BP and Brake Mean Effective Pressure (BMEP were significantly improved in average of 7% in comparison with gasoline model. In addition, Brake Specific Fuel Consumption (BSFC also shows an improvement by 5%, which is become more economic. Therefore, the developed GT-Power model offer a successful fuel conversion to LPG systems via retrofit technology to provide comprehensive support for implementation of energy efficient and environmental friendly vehicles.

  10. A germline mutation in the BRCA1 3'UTR predicts Stage IV breast cancer.

    Science.gov (United States)

    Dorairaj, Jemima J; Salzman, David W; Wall, Deirdre; Rounds, Tiffany; Preskill, Carina; Sullivan, Catherine A W; Lindner, Robert; Curran, Catherine; Lezon-Geyda, Kim; McVeigh, Terri; Harris, Lyndsay; Newell, John; Kerin, Michael J; Wood, Marie; Miller, Nicola; Weidhaas, Joanne B

    2014-06-10

    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 Vermont cohort. A variant in

  11. Prediction of postoperative respiratory function of lung cancer patients using quantitative lung scans

    International Nuclear Information System (INIS)

    Konishi, Hiroshi

    1982-01-01

    Quantitative sup(99m)Tc-MISA inhalation scan and sup(99m)Tc-MAA perfusion scan were performed in 35 patients with lung cancer who underwent lobectomies. Quantitative 133 Xe ventilation-perfusion scans were also performed in 34 patients with lung cancer who underwent lobectomies. To predict functional loss after lobectomy, the proportion of the No. of segments in the lobe to be resected to the No. of entire segments of that lung was provided for the study. Postoperative FVC, FEVsub(1.0) and MVV were predicted in the study, and which were compared to the respiratory function at one month after operation and more than four months after operation. The predicted postoperative respiratory function was highly correlated with the actually observed postoperative respiratory function (0.7413 lt r lt 0.9278, p lt 0.001). In this study, the postoperative respiratory function was proven to be quite accurately predicted preoperatively with combination of quantitative lung scans and spirometric respiratory function. Therefore this method is useful not only for judgement of operative indication but also for choice of operative method and for counterplan of postoperative respiratory insufficiency. (J.P.N.)

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

    International Nuclear Information System (INIS)

    Soto, Daniel E.; Andridge, Rebecca R.; Pan, Charlie C.; Williams, Scott G.; Taylor, Jeremy M.G.; Sandler, Howard M.

    2009-01-01

    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

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

    International Nuclear Information System (INIS)

    Álvarez Goyanes, Rosa Irene

    2011-01-01

    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)

  14. Prognostic Value of Histology and Lymph Node Status in Bilharziasis-Bladder Cancer: Outcome Prediction Using Neural Networks

    National Research Council Canada - National Science Library

    Ji, W

    2001-01-01

    .... Throughout the analysis of the prognostic feature combinations, two features, histological type and lymph node status, have been identified as the important indicators for outcome prediction of this type of cancer...

  15. Predicting retroperitoneal histology in postchemotherapy testicular germ cell cancer : A model update and multicentre validation with more than 1000 patients

    NARCIS (Netherlands)

    Vergouwe, Yvonne; Steyerberg, Ewout W.; Foster, Richard S.; Sleijfer, Dirk T.; Fossa, Sophie D.; Gerl, Arthur; de Wit, Ronald; Roberts, J. Trevor; Habbema, J. Dik F.

    Objectives: Surgical resection of postchemotherapy retroperitoneal lymph nodes is often performed in patients with advanced nonseminomatous testicular germ cell cancer. We previously developed a model to predict the probability that the lymph nodes contain only necrotic or fibrotic (benign) tissue

  16. A SOFTWARE RELIABILITY ESTIMATION METHOD TO NUCLEAR SAFETY SOFTWARE

    Directory of Open Access Journals (Sweden)

    GEE-YONG PARK

    2014-02-01

    Full Text Available A method for estimating software reliability for nuclear safety software is proposed in this paper. This method is based on the software reliability growth model (SRGM, where the behavior of software failure is assumed to follow a non-homogeneous Poisson process. Two types of modeling schemes based on a particular underlying method are proposed in order to more precisely estimate and predict the number of software defects based on very rare software failure data. The Bayesian statistical inference is employed to estimate the model parameters by incorporating software test cases as a covariate into the model. It was identified that these models are capable of reasonably estimating the remaining number of software defects which directly affects the reactor trip functions. The software reliability might be estimated from these modeling equations, and one approach of obtaining software reliability value is proposed in this paper.

  17. Prediction of response to neoadjuvant chemotherapy in breast cancer: a radiomic study

    Science.gov (United States)

    Wu, Guolin; Fan, Ming; Zhang, Juan; Zheng, Bin; Li, Lihua

    2017-03-01

    Breast cancer is one of the most malignancies among women in worldwide. Neoadjuvant Chemotherapy (NACT) has gained interest and is increasingly used in treatment of breast cancer in recent years. Therefore, it is necessary to find a reliable non-invasive assessment and prediction method which can evaluate and predict the response of NACT. Recent studies have highlighted the use of MRI for predicting response to NACT. In addition, molecular subtype could also effectively identify patients who are likely have better prognosis in breast cancer. In this study, a radiomic analysis were performed, by extracting features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and immunohistochemistry (IHC) to determine subtypes. A dataset with fifty-seven breast cancer patients were included, all of them received preoperative MRI examination. Among them, 47 patients had complete response (CR) or partial response (PR) and 10 had stable disease (SD) to chemotherapy based on the RECIST criterion. A total of 216 imaging features including statistical characteristics, morphology, texture and dynamic enhancement were extracted from DCE-MRI. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.923 (P = 0.0002) in leave-one-out crossvalidation. The performance of the classifier increased to 0.960, 0.950 and 0.936 when status of HER2, Luminal A and Luminal B subtypes were added into the statistic model, respectively. The results of this study demonstrated that IHC determined molecular status combined with radiomic features from DCE-MRI could be used as clinical marker that is associated with response to NACT.

  18. In Vitro Drug Sensitivity Tests to Predict Molecular Target Drug Responses in Surgically Resected Lung Cancer.

    Directory of Open Access Journals (Sweden)

    Ryohei Miyazaki

    Full Text Available Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs and anaplastic lymphoma kinase (ALK inhibitors have dramatically changed the strategy of medical treatment of lung cancer. Patients should be screened for the presence of the EGFR mutation or echinoderm microtubule-associated protein-like 4 (EML4-ALK fusion gene prior to chemotherapy to predict their clinical response. The succinate dehydrogenase inhibition (SDI test and collagen gel droplet embedded culture drug sensitivity test (CD-DST are established in vitro drug sensitivity tests, which may predict the sensitivity of patients to cytotoxic anticancer drugs. We applied in vitro drug sensitivity tests for cyclopedic prediction of clinical responses to different molecular targeting drugs.The growth inhibitory effects of erlotinib and crizotinib were confirmed for lung cancer cell lines using SDI and CD-DST. The sensitivity of 35 cases of surgically resected lung cancer to erlotinib was examined using SDI or CD-DST, and compared with EGFR mutation status.HCC827 (Exon19: E746-A750 del and H3122 (EML4-ALK cells were inhibited by lower concentrations of erlotinib and crizotinib, respectively than A549, H460, and H1975 (L858R+T790M cells were. The viability of the surgically resected lung cancer was 60.0 ± 9.8 and 86.8 ± 13.9% in EGFR-mutants vs. wild types in the SDI (p = 0.0003. The cell viability was 33.5 ± 21.2 and 79.0 ± 18.6% in EGFR mutants vs. wild-type cases (p = 0.026 in CD-DST.In vitro drug sensitivity evaluated by either SDI or CD-DST correlated with EGFR gene status. Therefore, SDI and CD-DST may be useful predictors of potential clinical responses to the molecular anticancer drugs, cyclopedically.

  19. Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

    Science.gov (United States)

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Qiu, Yuchen; Zheng, Bin

    2018-02-01

    Objective of this study is to develop and test a new computer-aided detection (CAD) scheme with improved region of interest (ROI) segmentation combined with an image feature extraction framework to improve performance in predicting short-term breast cancer risk. A dataset involving 570 sets of "prior" negative mammography screening cases was retrospectively assembled. In the next sequential "current" screening, 285 cases were positive and 285 cases remained negative. A CAD scheme was applied to all 570 "prior" negative images to stratify cases into the high and low risk case group of having cancer detected in the "current" screening. First, a new ROI segmentation algorithm was used to automatically remove useless area of mammograms. Second, from the matched bilateral craniocaudal view images, a set of 43 image features related to frequency characteristics of ROIs were initially computed from the discrete cosine transform and spatial domain of the images. Third, a support vector machine model based machine learning classifier was used to optimally classify the selected optimal image features to build a CAD-based risk prediction model. The classifier was trained using a leave-one-case-out based cross-validation method. Applying this improved CAD scheme to the testing dataset, an area under ROC curve, AUC = 0.70+/-0.04, which was significantly higher than using the extracting features directly from the dataset without the improved ROI segmentation step (AUC = 0.63+/-0.04). This study demonstrated that the proposed approach could improve accuracy on predicting short-term breast cancer risk, which may play an important role in helping eventually establish an optimal personalized breast cancer paradigm.

  20. Repeated assessments of symptom severity improve predictions for risk of death among patients with cancer.

    Science.gov (United States)

    Sutradhar, Rinku; Atzema, Clare; Seow, Hsien; Earle, Craig; Porter, Joan; Barbera, Lisa

    2014-12-01

    Although prior studies show the importance of self-reported symptom scores as predictors of cancer survival, most are based on scores recorded at a single point in time. To show that information on repeated assessments of symptom severity improves predictions for risk of death and to use updated symptom information for determining whether worsening of symptom scores is associated with a higher hazard of death. This was a province-based longitudinal study of adult outpatients who had a cancer diagnosis and had assessments of symptom severity. We implemented a time-to-death Cox model with a time-varying covariate for each symptom to account for changing symptom scores over time. This model was compared with that using only a time-fixed (baseline) covariate for each symptom. The regression coefficients of each model were derived based on a randomly selected 60% of patients, and then, the predictive performance of each model was assessed via concordance probabilities when applied to the remaining 40% of patients. This study had 66,112 patients diagnosed with cancer and more than 310,000 assessments of symptoms. The use of repeated assessments of symptom scores improved predictions for risk of death compared with using only baseline symptom scores. Increased pain and fatigue and reduced appetite were the strongest predictors for death. If available, researchers should consider including changing information on symptom scores, as opposed to only baseline information on symptom scores, when examining hazard of death among patients with cancer. Worsening of pain, fatigue, and appetite may be a flag for impending death. Copyright © 2014 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.