Outcome Prediction in CancerAzzam F.G. Taktak, Anthony C. Fisher 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 |
Contents
Biological and Genetic Factors | 65 |
Mathematical Background of Prognostic Models | 145 |
Application of Machine Learning Methods | 241 |
Dissemination of Information | 389 |
| 457 | |
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algorithm apoptosis application artificial neural networks assessment astrocytoma Bayesian Biganzoli Boracchi brain cancer brain tumours breast cancer cancer patients Cancer Res carcinogens cause-specific censored chromosome classification Clin clinical clustering complex computational covariates Cox’s dataset developed diagnosis disease estimated evaluation expression profiles follow-up function gene expression genetic genome genotype Geoconda gliomas Hakulinen healthcare histological HNPCC imaging input Kaplan–Meier linear Lisboa lung cancer lung cancer risk lymph node machine learning metastasis metastatic methods molecular molecules mortality neurons node-negative node-negative patients Oncol oncology oral output parameters pathological pattern PI group PLANN-ARD polymorphisms prediction model problem protein receptor recurrence regression model relative survival rates resonance risk group risk of lung sample smoking squamous cell carcinoma standard statistical survival analysis techniques therapy tissue treatment uveal melanoma values variables vector Woolgar
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