Skip to main content

Outcome Prediction In Cancer

In Order to Read Online or Download Outcome Prediction In Cancer Full eBooks in PDF, EPUB, Tuebl and Mobi you need to create a Free account. Get any books you like and read everywhere you want. Fast Download Speed ~ Commercial & Ad Free. We cannot guarantee that every book is in the library!

Outcome Prediction in Cancer

Outcome Prediction in Cancer Book
Author : Azzam F.G. Taktak,Anthony C. Fisher
Publisher : Elsevier
Release : 2006-11-28
ISBN : 9780080468037
Language : En, Es, Fr & De

GET BOOK

Book Description :

This book is organized into 4 sections, each looking at the question of outcome prediction in cancer from a different angle. The first section describes the clinical problem and some of the predicaments that clinicians face in dealing with cancer. Amongst issues discussed in this section are the TNM staging, accepted methods for survival analysis and competing risks. The second section describes the biological and genetic markers and the rôle of bioinformatics. Understanding of the genetic and environmental basis of cancers will help in identifying high-risk populations and developing effective prevention and early detection strategies. The third section provides technical details of mathematical analysis behind survival prediction backed up by examples from various types of cancers. The fourth section describes a number of machine learning methods which have been applied to decision support in cancer. The final section describes how information is shared within the scientific and medical communities and with the general population using information technology and the World Wide Web. * Applications cover 8 types of cancer including brain, eye, mouth, head and neck, breast, lungs, colon and prostate * Include contributions from authors in 5 different disciplines * Provides a valuable educational tool for medical informatics

Outcome Prediction in Head and Neck Cancer Patients Using Machine Learning Methods

Outcome Prediction in Head and Neck Cancer Patients Using Machine Learning Methods Book
Author : David John Dellsperger,University of Iowa. College of Engineering. Biomedical Engineering
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

Head and Neck cancers account for approximately 3.2% of the estimated 1,660,290 new cancer cases for the year 2013 and roughly 1.9% of cancer-related deaths in 2013. In this research, machine learning techniques were employed to predict outcome in cancer patients supporting more objective assessment of the treatments, including surgery, radiation therapy, or chemotherapy. Selection of features capable of distinguishing between the possible outcomes was accomplished by using a highly selective cohort of 61 patients with similar treatment and location of the primary tumor. An accuracy of 80.33% (compared to a baseline majority classifier of 60.66%) was achieved utilizing this cohort. Further, it is shown that this limited cohort has the power to provide valuable information on outcome prediction utilizing as few as four features. Feature selection was drawn from both clinical features and quantitative imaging features including the site of cancer, primary tumor volume, and race.

Comprehensive Evaluation Composite Gene Features in Cancer Outcome Prediction

Comprehensive Evaluation Composite Gene Features in Cancer Outcome Prediction Book
Author : Dezhi Hou
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

There have been extensive studies of classification and prediction of cancer outcome with composite gene features that combine functionally related genes together as a single feature to improve the classification and prediction accuracy. Various algorithms have been proposed for feature extraction, feature activity inference, and feature selection, which all claim to improve the prediction accuracy. However, due to the limited test data sets used by each independent study, inconsistent test procedures, and conflicting results, it is difficult to obtain a comprehensive understanding of the relative performances of these algorithms. In this study, various algorithms for the three steps in using composite features for cancer outcome prediction were implemented and an extensive comparison and evaluation were performed by applying testing to seven microarray data sets covering two cancer types and three different clinical outcomes. Also by integrating algorithms in all three different steps, we aimed to investigate how to get the best cancer prediction by using different combinations of these techniques.

Improving Breast Cancer Outcome Prediction by Combining Multiple Data Sources

Improving Breast Cancer Outcome Prediction by Combining Multiple Data Sources Book
Author : Martinus Hendrikus van Vliet
Publisher : Unknown
Release : 2010
ISBN : 9789090251783
Language : En, Es, Fr & De

GET BOOK

Book Description :

Download Improving Breast Cancer Outcome Prediction by Combining Multiple Data Sources book written by Martinus Hendrikus van Vliet, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Comparison of Diverse Genomic Data for Outcome Prediction in Cancer

Comparison of Diverse Genomic Data for Outcome Prediction in Cancer Book
Author : Hugo Gómez Rueda
Publisher : Unknown
Release : 2015
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

"Background. In cancer, large-scale technologies such as next-generation sequencing and microarrays have produced a wide number of genomic features such as DNA copy number alterations (CNA), mRNA expression (EXPR), microRNA expression (MIRNA), and DNA somatic mutations (MUT), among others. Several analyses of a specific type of these genomic data have generated many prognostic biomarkers in many cancer types, and more frequently in breast cancer. However, it is uncertain which of these data is more powerful and whether the best data-type is cancer-type dependent. Objective. Characterize the prognostic power of models obtained from different genomic data types in Breast Cancer (BRCA) from public repositories and to compare the performance of these models with those obtained from data of Mexican patients".

Identification of DNA Methylation Biomarkers for Disease Outcome Prediction of Esophageal Cancer and Lung Cancer

Identification of DNA Methylation Biomarkers for Disease Outcome Prediction of Esophageal Cancer and Lung Cancer Book
Author : 郭懿瑩
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

Download Identification of DNA Methylation Biomarkers for Disease Outcome Prediction of Esophageal Cancer and Lung Cancer book written by 郭懿瑩, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

From Correlation to Casuality

From Correlation to Casuality Book
Author : Janine Roy
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

Download From Correlation to Casuality book written by Janine Roy, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Prognostic Value of Histology and Lymph Node Status in Bilharziasis Bladder Cancer Outcome Prediction Using Neural Networks

Prognostic Value of Histology and Lymph Node Status in Bilharziasis Bladder Cancer  Outcome Prediction Using Neural Networks Book
Author : Anonim
Publisher : Unknown
Release : 2001
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

In this paper, the evaluation of two features in predicting the outcomes of patients with bilharziasis bladder cancer has been investigated using an RBF neural network. Prior to prediction, the feature subsets were extracted from the whole set of features for the purpose of providing a high performance of the network. 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. The highest predictive accuracy reached 85.O% in this study.

CT radiomics in the Context of Outcome Prediction After Chemoradio Therapy CRT in Cancer Patients

CT radiomics in the Context of Outcome Prediction After Chemoradio Therapy  CRT  in Cancer Patients Book
Author : Jairo Andrés Socarrás Fernández
Publisher : Unknown
Release : 2020
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

Download CT radiomics in the Context of Outcome Prediction After Chemoradio Therapy CRT in Cancer Patients book written by Jairo Andrés Socarrás Fernández, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Issues in Cancer Epidemiology and Research 2011 Edition

Issues in Cancer Epidemiology and Research  2011 Edition Book
Author : Anonim
Publisher : ScholarlyEditions
Release : 2012-01-09
ISBN : 1464963525
Language : En, Es, Fr & De

GET BOOK

Book Description :

Issues in Cancer Epidemiology and Research / 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Cancer Epidemiology and Research. The editors have built Issues in Cancer Epidemiology and Research: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Cancer Epidemiology and Research in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Cancer Epidemiology and Research: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Predicting Cancer Outcome

Predicting Cancer Outcome Book
Author : Anonim
Publisher : Unknown
Release : 2005
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

We read with interest the paper by Michiels et al on the prediction of cancer with microarrays and the commentary by Ioannidis listing the potential as well as the limitations of this approach (February 5, p 488 and 454). Cancer is a disease characterized by complex, heterogeneous mechanisms and studies to define factors that can direct new drug discovery and use should be encouraged. However, this is easier said than done. Casti teaches that a better understanding does not necessarily extrapolate to better prediction, and that useful prediction is possible without complete understanding (1). To attempt both, explanation and prediction, in a single nonmathematical construct, is a tall order (Figure 1).

Radiation Therapy Outcome Prediction Using Statistical Correlations Deep Learning

Radiation Therapy Outcome Prediction Using Statistical Correlations   Deep Learning Book
Author : André Diamant Boustead
Publisher : Unknown
Release : 2020
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

"Prognosis after cancer treatment is a constant concern for physicians, patients and their surrounding friends and family. This is one of the reasons that treatment outcomes prediction is such a critical field of research. The sheer magnitude of data generated within a typical radiation oncology clinic each year facilitates the development and eventual validation of predictive and prognostic models. Furthermore, the technological advances driven by data science have enabled the usage of advanced machine learning techniques which can far exceed the performance of previously used conventional techniques.Most cancer patients follow a standard radiation oncology workflow, which among other things includes medical imaging (CT/PET) and the creation of a radiation therapy treatment plan. As these sorts of data are (in theory) present for every patient, they are ideal variables to input into a predictive model. The goal of this thesis was to investigate these two types of pre-treatment input data (diagnostic imaging and dosimetric data) along with patient characteristics to identify associations and create models capable of predicting a cancer patient's treatment response following radiation therapy. The first objective was to investigate dose-volume metrics as predictors of clinical outcomes in a cohort of 422 non-small cell lung cancer (NSCLC) patients who received stereotactic body radiation therapy (SBRT). A correlation between the dose delivered to the region outside the tumor and the occurrence of distant metastasis was revealed. In particular, patients who received above a certain threshold dose were shown to have significantly reduced distant metastasis recurrence rates compared to the rest of the population. This was first shown on 217 patients all of whom were treated with conventional SBRT treatment modalities. Next, a similar analysis was done on 205 patients who were treated with a robotic arm linear accelerator (CyberKnife). It was found that the CyberKnife cohort had both superior distant control and local control, suggesting that under current prescription practices, CyberKnife, as a delivery device, could be superior for treating NSCLC patients with SBRT. The second objective of this thesis was to investigate the usage of a deep learning framework applied to raw medical imaging data in order to predict the overall prognosis of head & neck cancer patients post-radiation therapy. A de novo architecture was built incorporating CT images, resulting in comparable performance to a state-of-the-art study. Furthermore, our model was shown to recognize imaging features (`radiomics') previously shown to be predictive without being explicitly presented with their definition. The final portion of this work was the development of a multi-modal deep learning framework which incorporated CT & PET images along with clinical information. This was compared to the previous architecture built, showing substantial increase in prediction performance for both overall survival and local recurrence. It was also shown to function in the presence of missing data, a common occurrence within the medical landscape.This work demonstrates that pre-treatment prediction of a cancer patient's post-radiation therapy outcomes is possible by learning correlations and building models from readily available data. Future efforts should be put towards data sharing & data curation to enable the creation and validation of models that eventually can be used in the clinic. Ultimately, predictive models should evolve into generative models whereupon one's treatment could be automatically created with the explicit intention of statistically optimizing that patient's outcomes"--

Identification of MicroRNAs for the Prediction of Breast Cancer Treatment Outcome

Identification of MicroRNAs for the Prediction of Breast Cancer Treatment Outcome Book
Author : Joanna Achinger-Kawecka
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

Download Identification of MicroRNAs for the Prediction of Breast Cancer Treatment Outcome book written by Joanna Achinger-Kawecka, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Biomarker Discovery and Clinical Outcome Prediction Using Knowledge Based bioinformatics

Biomarker Discovery and Clinical Outcome Prediction Using Knowledge Based bioinformatics Book
Author : John H. Phan
Publisher : Unknown
Release : 2009
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

Advances in high-throughput genomic and proteomic technology have led to a growing interest in cancer biomarkers. These biomarkers can potentially improve the accuracy of cancer subtype prediction and subsequently, the success of therapy. However, identification of statistically and biologically relevant biomarkers from high-throughput data can be unreliable due to the nature of the data--e.g., high technical variability, small sample size, and high dimension size. Due to the lack of available training samples, data-driven machine learning methods are often insufficient without the support of knowledge-based algorithms. We research and investigate the benefits of using knowledge-based algorithms to solve clinical prediction problems. Because we are interested in identifying biomarkers that are also feasible in clinical prediction models, we focus on two analytical components: feature selection and predictive model selection. In addition to data variance, we must also consider the variance of analytical methods. There are many existing feature selection algorithms, each of which may produce different results. Moreover, it is not trivial to identify model parameters that maximize the sensitivity and specificity of clinical prediction. Thus, we introduce a method that uses independently validated biological knowledge to reduce the space of relevant feature selection algorithms and to improve the reliability of clinical predictors. Finally, we implement several functions of this knowledge-based method as a web-based, user-friendly, and standards-compatible software application.

Cancer Research

Cancer Research Book
Author : Anonim
Publisher : Unknown
Release : 2009-02
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

Download Cancer Research book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Developing and Implementing the AJCC Prognostic System for Breast Cancer

Developing and Implementing the AJCC Prognostic System for Breast Cancer Book
Author : Anonim
Publisher : Unknown
Release : 1999
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

In the past staging' systems provided a simple, easily understood ordering of patient' outcomes. For over thirty years breast cancer outcome prediction has been based on the TNM staging system. There are two problems with staging systems generally, and specifically with the TNM system: (1) they are not very accurate, i.e., their predictions are not close to the true outcomes), and (2) their accuracy can not be substantially improved because additional predictive factors can not be included in the system without increasing the system's complexity to the point where it is not longer useful to the clinician. The objective of this research is to replace with current TNM stage system with a new prognostic system that is inherently more accurate than the current system and that can integrate new prognostic factors to further improve prognostic accuracy. There are three components to - accomplishing this objective, which are the goals of this research project: (1) the development of the prognostic model itself, (2) the creation of the prognostic system by training the model with breast cancer outcome data, and (3) the computer-based implementation of the system for clinicians and tumor registries (clinical decision support system).

Predicting Cancer Outcome with Multispectral Tumor Tissue Images

Predicting Cancer Outcome with Multispectral Tumor Tissue Images Book
Author : Jin Qu
Publisher : Unknown
Release : 2017
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

Tumor tissue slides have been used by clinicians to assess cancer patient's condition and indicate prognosis. Several recent studies have suggested that distribution of important immunological biomarkers on tumor tissue slides might help predict survival outcome. These studies rely upon non-parametric Kaplan-Meier survival analysis with Log-rank test to extract statistical insights, which, however, has several disadvantages such as prediction ambiguity and inability to directly model continuous variables. In this study, we engineered 676 features encoding cellular distribution information from multi-spectral tumor tissue images collected from 118 HPV-negative oral squamous cell cancer patients. We leveraged statistical methods and predictive models to explore the predictive power of these features. We identified 18 features as potential survival predictors through Kolmogorov-Smirnov test. Our best model, random forest model, has achieved 58.54% prediction accuracy rate on independent validation dataset. Although the model does not suggest strong predictive power of selected features, evaluation on large scale training data is still needed to further tune model parameters and generate more concrete results.

Journal of Bioscience and Bioengineering

Journal of Bioscience and Bioengineering Book
Author : Anonim
Publisher : Unknown
Release : 2004
ISBN : 0987650XXX
Language : En, Es, Fr & De

GET BOOK

Book Description :

Download Journal of Bioscience and Bioengineering book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.