Outcome Prediction in Cancer

by Taktak; Fisher

• ISBN13: 9780444528551
• ISBN10: 0444528555
• Edition: 1st
• Format: Hardcover
• Copyright: 2007-03-26
• Publisher: Elsevier Science

Summary

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

Table of Contents

The Clinical Problem

The predictive value of detailed histological staging of surgical resection specimens in oral cancer

The predictive value of detailed histological staging of surgical resection specimens in oral cancer

Survival after Treatment of Intraocular Melanoma

Recent developments in relative survival analysis

Biological and Genetic Factors

Environmental and genetic risk factors of lung cancer

Chaos, cancer, the cellular operating system and the prediction of survival in head and neck cancer

Mathematical Background of Prognostic Models

Flexible hazard modelling for outcome prediction in cancer - perspectives for the use of bioinformatics knowledge

Information geometry for survival analysis and feature selection by neural networks

Artificial neural networks used in the survival analysis of breast cancer patients: A node negative study

Application of Machine Learning Methods

The use of artificial neural networks for the diagnosis and estimation of prognosis in cancer patients

Machine learning contribution to solve prognosis medical problems

Classification of brain tumours by pattern recognition of Magnetic Resonance Imaging and Spectroscopic data

Towards automatic risk analysis for hereditary non-polyposis colorectal cancer based on pedigree data

The impact of microarray technology in brain cancer

Dissemination of Information

The web and the new generation of medical information

Geoconda: a web environment for multi-centre research

The development and execution of medical prediction models

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