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.

Key Features

* 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


Cancer researchers, oncologists, medical staticians, and bioinformatics researchers.

Table of Contents

Section 1 – The Clinical Problem. THE PREDICTIVE VALUE OF DETAILED HISTOLOGICAL STAGING OF SURGICAL RESECTION SPECIMENS IN ORAL CANCER Chapter 1: The predictive value of detailed histological staging of surgical resection specimens in oral cancer. J. Woolgar Liverpool Dental School, UK Chapter 2: Survival after Treatment of Intraocular Melanoma. B.E. Damato, A.F.G. Taktak, Royal Liverpool University Hospital, UK Chapter 3: Recent developments in relative survival analysis. T. Hakulinen, T.A. Dyba, Finnish Cancer Registry Section 2 – Biological and Genetic Factors Chapter 4: Environmental and genetic risk factors of lung cancer. A. Cassidy, J.K. Field, University of Liverpool, UK Chapter 5: Chaos, cancer, the cellular operating system and the prediction of survival in head and neck cancer. A.S. Jones, University Hospital Aintree, UK Section 3 – Mathematical Background of Prognostic Models Chapter 6: Flexible hazard modelling for outcome prediction in cancer - perspectives for the use of bioinformatics knowledge. E.Biganzoli1, P. Boracchi2 1 Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano, Italy 2 Università degli Studi di Milano, Milano, Italy Chapter 7: Information geometry for survival analysis and feature selection by neural networks. A. Eleuteri 1,2, R. Tagliaferri 3,4, L. Milano 1,2, M. De Laurentiis 1 1Università di Napoli, Italy 2INFN sez. Napoli, Italy 3Universit`a di Salerno, Italy 4INFN sez. distaccata di Salerno, Italy


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© 2007
Elsevier Science
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About the editors

Azzam Taktak

Azzam Taktak is a Principal Clinical Scientist in the Department of Clinical Engineering, Royal Liverpool University Hospital and an Honorary Lecturer at the University of Liverpool. His main research interests are the application of mathematical models and artificial intelligence to medical applications specifically in cancer.

Affiliations and Expertise

Royal Liverpool University Hospital, UK

Anthony Fisher

Anthony Fisher is a Consultant Clinical Scientist in the Department of Clinical Engineering, Royal Liverpool University Hospital. Previously he was a Senior Lecturer in Bioengineering at the University of Strathclyde. Glasgow. His principal academic interests are biomedical instrumentation and signal processing.

Affiliations and Expertise

Royal Liverpool University Hospital, UK