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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 role
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"
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