Outcome Prediction in Cancer Book

Outcome Prediction in Cancer


  • Author : Azzam F.G. Taktak
  • Publisher : Elsevier
  • Release Date : 2006-11-28
  • Genre: Computers
  • Pages : 482
  • ISBN 10 : 0080468039

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Outcome Prediction in Cancer Excerpt :

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 Book

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


  • Author : David John Dellsperger
  • Publisher : Unknown
  • Release Date : 2014
  • Genre: Head
  • Pages : 31
  • ISBN 10 : OCLC:888441784

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

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 Book

Comprehensive Evaluation Composite Gene Features in Cancer Outcome Prediction


  • Author : Dezhi Hou
  • Publisher : Unknown
  • Release Date : 2014
  • Genre: Uncategoriezed
  • Pages : 76
  • ISBN 10 : OCLC:1164806431

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Comprehensive Evaluation Composite Gene Features in Cancer Outcome Prediction Excerpt :

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.

Head and Neck Tumor Segmentation and Outcome Prediction Book

Head and Neck Tumor Segmentation and Outcome Prediction


  • Author : Vincent Andrearczyk
  • Publisher : Springer Nature
  • Release Date : 2022
  • Genre: Application software
  • Pages : 339
  • ISBN 10 : 9783030982539

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Head and Neck Tumor Segmentation and Outcome Prediction Excerpt :

This book constitutes the Second 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 27, 2021, due to the COVID-19 pandemic. The 29 contributions presented, as well as an overview paper, were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 325 delineated PET/CT images was made available for training.

Radiation Therapy Outcome Prediction Using Statistical Correlations   Deep Learning Book

Radiation Therapy Outcome Prediction Using Statistical Correlations Deep Learning


  • Author : André Diamant Boustead
  • Publisher : Unknown
  • Release Date : 2020
  • Genre: Uncategoriezed
  • Pages : null
  • ISBN 10 : OCLC:1199006721

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Radiation Therapy Outcome Prediction Using Statistical Correlations Deep Learning Excerpt :

"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 co

Head and Neck Tumor Segmentation Book

Head and Neck Tumor Segmentation


  • Author : Vincent Andrearczyk
  • Publisher : Springer Nature
  • Release Date : 2021-01-12
  • Genre: Computers
  • Pages : 109
  • ISBN 10 : 9783030671945

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Head and Neck Tumor Segmentation Excerpt :

This book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic. The 2 full and 8 short papers presented together with an overview paper in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 204 delineated PET/CT images was made available for training as well as 53 PET/CT images for testing. Various deep learning methods were developed by the participants with excellent results.

Developing and Implementing the AJCC Prognostic System for Breast Cancer Book

Developing and Implementing the AJCC Prognostic System for Breast Cancer


  • Author : Anonim
  • Publisher : Unknown
  • Release Date : 1997
  • Genre: Uncategoriezed
  • Pages : 28
  • ISBN 10 : OCLC:227873402

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Developing and Implementing the AJCC Prognostic System for Breast Cancer Excerpt :

Accurate survival prediction is important for women with breast cancer because a woman's expected survival determines her therapy, provides her with vital outcome information, and is one of the main selection criteria for entry into new therapy clinical trials. For almost forty years breast cancer outcome prediction has been based on the TNM staging system. This system it is relatively inaccurate, its accuracy continues to decline as screening increases the early detection of breast cancer, and its accuracy cannot be significantly improved. The objective of this research program is to replace the TNM staging system with a computer-based clinical decision support system that provides the most accurate survival predictions possible for women with breast cancer.

Prognosis Research in Healthcare Book

Prognosis Research in Healthcare


  • Author : Richard D. Riley
  • Publisher : Oxford University Press
  • Release Date : 2019-01-17
  • Genre: Medical
  • Pages : 384
  • ISBN 10 : 9780192516657

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Prognosis Research in Healthcare Excerpt :

"What is going to happen to me?" Most patients ask this question during a clinical encounter with a health professional. As well as learning what problem they have (diagnosis) and what needs to be done about it (treatment), patients want to know about their future health and wellbeing (prognosis). Prognosis research can provide answers to this question and satisfy the need for individuals to understand the possible outcomes of their condition, with and without treatment. Central to modern medical practise, the topic of prognosis is the basis of decision making in healthcare and policy development. It translates basic and clinical science into practical care for patients and populations. Prognosis Research in Healthcare: Concepts, Methods and Impact provides a comprehensive overview of the field of prognosis and prognosis research and gives a global perspective on how prognosis research and prognostic information can improve the outcomes of healthcare. It details how to design, carry out, analyse and report prognosis studies, and how prognostic information can be the basis for tailored, personalised healthcare. In particular, the book discusses how information about the characteristics of people, their health, and environment can be used to predict an individual's future health. Prognosis Research in Healthcare: Concepts, Methods and Impact, addresses all types of prognosis research and provides a practical step-by-step guide to undertaking and interpreting prognosis research studies, ideal for medical students, health researchers, healthcare professionals and methodologists, as well as for guideline and policy makers in healthcare wishing to learn more about the field of prognosis.

Comparison of Diverse Genomic Data for Outcome Prediction in Cancer Book

Comparison of Diverse Genomic Data for Outcome Prediction in Cancer


  • Author : Hugo Gómez Rueda
  • Publisher : Unknown
  • Release Date : 2015
  • Genre: Uncategoriezed
  • Pages : null
  • ISBN 10 : OCLC:970600968

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Comparison of Diverse Genomic Data for Outcome Prediction in Cancer Excerpt :

"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".