November 23, 2020

Download Ebook Free Outcome Prediction In Cancer

Outcome Prediction in Cancer

Outcome Prediction in Cancer
Author : Azzam F.G. Taktak,Anthony C. Fisher
Publisher : Elsevier
Release Date : 2006-11-28
Category : Computers
Total pages :482
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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

Comprehensive Evaluation Composite Gene Features in Cancer Outcome Prediction

Comprehensive Evaluation Composite Gene Features in Cancer Outcome Prediction
Author : Dezhi Hou
Publisher : Unknown
Release Date : 2014
Category :
Total pages :76
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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.

Radiation Therapy Outcome Prediction Using Statistical Correlations & Deep Learning

Radiation Therapy Outcome Prediction Using Statistical Correlations & Deep Learning
Author : André Diamant Boustead
Publisher : Unknown
Release Date : 2020
Category :
Total pages :129
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"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"--

Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management

Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management
Author : R. N. G. Naguib,G. V. Sherbet
Publisher : CRC Press
Release Date : 2001-06-22
Category : Medical
Total pages :216
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The potential value of artificial neural networks (ANN) as a predictor of malignancy has begun to receive increased recognition. Research and case studies can be found scattered throughout a multitude of journals. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management brings together the work of top researchers - primaril

Issues in Cancer Epidemiology and Research: 2011 Edition

Issues in Cancer Epidemiology and Research: 2011 Edition
Author : Anonim
Publisher : ScholarlyEditions
Release Date : 2012-01-09
Category : Medical
Total pages :3510
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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/.

Decision Analytics and Optimization in Disease Prevention and Treatment

Decision Analytics and Optimization in Disease Prevention and Treatment
Author : Nan Kong
Publisher : John Wiley & Sons
Release Date : 2018-03-13
Category : Business & Economics
Total pages :432
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A systematic review of the most current decision models and techniques for disease prevention and treatment Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource of the most current decision models and techniques for disease prevention and treatment. With contributions from leading experts in the field, this important resource presents information on the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology. Designed to be accessible, in each chapter the text presents one decision problem with the related methodology to showcase the vast applicability of operations research tools and techniques in advancing medical decision making. This vital resource features the most recent and effective approaches to the quickly growing field of healthcare decision analytics, which involves cost-effectiveness analysis, stochastic modeling, and computer simulation. Throughout the book, the contributors discuss clinical applications of modeling and optimization techniques to assist medical decision making within complex environments. Accessible and authoritative, Decision Analytics and Optimization in Disease Prevention and Treatment: Presents summaries of the state-of-the-art research that has successfully utilized both decision analytics and optimization tools within healthcare operations research Highlights the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology Includes contributions by well-known experts from operations researchers to clinical researchers, and from data scientists to public health administrators Offers clarification on common misunderstandings and misnomers while shedding light on new approaches in this growing area Designed for use by academics, practitioners, and researchers, Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource for accessing the power of decision analytics and optimization tools within healthcare operations research.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine
Author : David Riaño,Szymon Wilk,Annette ten Teije
Publisher : Springer
Release Date : 2019-06-19
Category : Computers
Total pages :429
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This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

New Approaches to Classification and Diagnostic Prediction of Breast Cancers

New Approaches to Classification and Diagnostic Prediction of Breast Cancers
Author : Aleix Prat,Mothaffar Rimawi
Publisher : Frontiers Media SA
Release Date : 2020-06-16
Category :
Total pages :129
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Despite many years of translational research in breast cancer, very few new biomarkers have been implemented for clinical use beyond estrogen receptor, progesterone receptor, and HER2. The main reason is that many promising biomarkers are clinically validated but lack analytical and clinical utility. One explanation is that proper validation of the predictive ability of the biomarker in independent datasets, and with a pre-planned statistical analysis, is not always performed. Thus, there is a need to identify new biomarkers or new ways to subclassify breast cancer patients that are reproducible and easy to implement in the clinical setting but, more importantly, that improve patient’s outcomes.

Bioinformatics in Cancer and Cancer Therapy

Bioinformatics in Cancer and Cancer Therapy
Author : Gavin J. Gordon
Publisher : Springer Science & Business Media
Release Date : 2008-10-25
Category : Medical
Total pages :198
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Bioinformatics can be loosely defined as the collection, classification, storage, and analysis of biochemical and biological information using computers and mathematical algorithms. Bioinformatics represents a marriage of biology, medicine, computer science, physics, and mathematics, fields of study that have historically existed as mutually exclusive disciplines. Edited by Gavin Gordon, Bioinformatics in Cancer and Cancer Therapy, the focus of this book is to provide a historical and technical perspective on the analytical techniques, methodologies, and platforms used in bioinformatics experiments, to show how a bioinformatics approach has been used to characterize various cancer-related processes, and to demonstrate how a bioinformatics approach is being used to bridge basic science and the clinical arena to positively impact patient care and management.

Gynecologic Cancer

Gynecologic Cancer
Author : Loren K. Mell, MD
Publisher : Demos Medical Publishing
Release Date : 2011-12-20
Category : Medical
Total pages :200
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Radiation Medicine Rounds is a trinary, hard cover, periodical designed to provide an up-to-date review of a dedicated radiation medicine topic of interest to clinicians andscientists who are involved in the care of patients receiving radiotherapy. It is intended to serve as both a reference and instructional tool for students, housestaff, fellows, practicing clinicians, medical physicists, cancer biologists, radiobiologists, and interdisciplinary colleagues throughout the oncology spectrum. With contributions from experts across the U.S., Gynecologic Cancer details the current management of different types of gynecologic cancer. Today the management of gynecologic cancers is multidisciplinary in nature, requiring close collaboration between gynecologic oncologists, medical oncologists, and radiation oncologists. Gynecologic Cancer reviews new therapies in use and under development for gynecologic cancer including novel radiation approaches, the role of chemotherapy and novel biologic agents, new and emerging surgical approaches, and the role of MRI in assessing tumor response in cervical cancer patients. It is a valuable tool for clinicians, nurses, researchers, medical students, residents, and fellows. Included in Gynecologic Cancer Intensity-Modulated Radiotherapy for Gynecologic Malignancies In-Room Image-Guided Radiation Therapy for Cervical Cancers Image-Guided Brachytherapy in Cervical Cancer Intensity-Modulated Radiotherapy in Vulvar Cancer Chemotherapy in Advanced/Recurrent Endometrial Cancer Biologic Agents and Immune Therapy in Gynecologic Cancers Lymphadenectomy in Uterine Cancer Robotic Surgery in Gynecologic Cancer Magnetic Resonance Imaging for Assessing Tumor Response in Cervical Cancer

Bladder Cancer: New Insights for the Healthcare Professional: 2012 Edition

Bladder Cancer: New Insights for the Healthcare Professional: 2012 Edition
Author : Anonim
Publisher : ScholarlyEditions
Release Date : 2012-12-10
Category : Medical
Total pages :199
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Bladder Cancer: New Insights for the Healthcare Professional / 2012 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Bladder Cancer. The editors have built Bladder Cancer: New Insights for the Healthcare Professional / 2012 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Bladder Cancer 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 Bladder Cancer: New Insights for the Healthcare Professional / 2012 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/.

Breast Cancer: New Insights for the Healthcare Professional: 2011 Edition

Breast Cancer: New Insights for the Healthcare Professional: 2011 Edition
Author : Anonim
Publisher : ScholarlyEditions
Release Date : 2012-01-09
Category : Medical
Total pages :1416
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Breast Cancer: New Insights for the Healthcare Professional: 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Breast Cancer. The editors have built Breast Cancer: New Insights for the Healthcare Professional: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Breast Cancer 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 Breast Cancer: New Insights for the Healthcare Professional: 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/.

Frontiers of Biostatistical Methods and Applications in Clinical Oncology

Frontiers of Biostatistical Methods and Applications in Clinical Oncology
Author : Shigeyuki Matsui,John Crowley
Publisher : Springer
Release Date : 2017-10-03
Category : Medical
Total pages :438
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This book presents the state of the art of biostatistical methods and their applications in clinical oncology. Many methodologies established today in biostatistics have been brought about through its applications to the design and analysis of oncology clinical studies. This field of oncology, now in the midst of evolution owing to rapid advances in biotechnologies and cancer genomics, is becoming one of the most promising disease fields in the shift toward personalized medicine. Modern developments of diagnosis and therapeutics of cancer have also been continuously fueled by recent progress in establishing the infrastructure for conducting more complex, large-scale clinical trials and observational studies. The field of cancer clinical studies therefore will continue to provide many new statistical challenges that warrant further progress in the methodology and practice of biostatistics. This book provides a systematic coverage of various stages of cancer clinical studies. Topics from modern cancer clinical trials include phase I clinical trials for combination therapies, exploratory phase II trials with multiple endpoints/treatments, and confirmative biomarker-based phase III trials with interim monitoring and adaptation. It also covers important areas of cancer screening, prognostic analysis, and the analysis of large-scale molecular data in the era of big data.

Renal Cancer

Renal Cancer
Author : John A Libertino
Publisher : Springer Science & Business Media
Release Date : 2013-06-14
Category : Medical
Total pages :397
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Renal Cancer: Contemporary Management provides a state of the art overview of the major topics in the field of kidney cancer. The material has been collected from the most current evidence based resources to allow for the appropriate care of these patients. The sections of the book have been structured to give an overview of the major issues dealing with renal cell carcinoma and transitional cell carcinoma of the kidney. The text also reviews the new staging system, discusses familial syndromes of renal cell carcinoma, and provides new perspectives with regard to imaging and managing renal tumors. A valuable resource for physicians and researchers dealing with renal cancer, Renal Cancer: Contemporary Management provides a comprehensive summary of the field that will guide patient management and stimulate further clinical and basic science research efforts.

Diagnostic Histopathology of Tumors

Diagnostic Histopathology of Tumors
Author : Christopher D. M. Fletcher
Publisher : Elsevier Health Sciences
Release Date : 2013-04-02
Category : Medical
Total pages :2296
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Diagnose tumors with confidence with Diagnostic Histopathology of Tumors, 4th Edition. Dr. Christopher Fletcher's renowned reference provides the advanced, expert guidance you need to evaluate and interpret even the most challenging histopathology specimens more quickly and accurately. Consult this title on your favorite e-reader with intuitive search tools and adjustable font sizes. Elsevier eBooks provide instant portable access to your entire library, no matter what device you're using or where you're located. Diagnose efficiently and effectively using diagnostic flow charts, correlations of gross appearances to microscopic findings, and differential diagnosis tables for better recognition and evaluation of similar-looking entities. Employ immunohistochemistry, molecular and genetic diagnostic tests, and other modern techniques as well as the best morphologic diagnostic methods to effectively identify each tumor or tumor-like entity. Utilize new, clinically important molecular genetic data and updated classification schemes to help guide treatment and targeted therapy. Apply the latest techniques and diagnostic criteria with completely rewritten chapters on Small and Large Intestines, Heart, Larynx and Trachea, Ear, and Peritoneum. Find critical information quickly thanks to more tables and bulleted lists throughout.