Outcome Prediction in Cancer

Edited by

  • Azzam Taktak, Royal Liverpool University Hospital, UK
  • Anthony Fisher, Royal Liverpool University Hospital, UK

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 rĂ´le 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.
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Cancer researchers, oncologists, medical staticians, and bioinformatics researchers.


Book information

  • Published: November 2006
  • Imprint: ELSEVIER
  • ISBN: 978-0-444-52855-1

Table of Contents

Section 1 – The Clinical Problem.THE PREDICTIVE VALUE OF DETAILED HISTOLOGICAL STAGING OF SURGICAL RESECTION SPECIMENS IN ORAL CANCERChapter 1: The predictive value of detailed histological staging of surgical resection specimens in oral cancer. J. Woolgar Liverpool Dental School, UKChapter 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 RegistrySection 2 – Biological and Genetic FactorsChapter 4: Environmental and genetic risk factors of lung cancer. A. Cassidy, J.K. Field, University of Liverpool, UKChapter 5: Chaos, cancer, the cellular operating system and the prediction of survival in head and neck cancer. A.S. Jones, University Hospital Aintree, UKSection 3 – Mathematical Background of Prognostic ModelsChapter 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, Italy2 Università degli Studi di Milano, Milano, ItalyChapter 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, Italy2INFN sez. Napoli, Italy3Universit`a di Salerno, Italy4INFN sez. distaccata di Salerno, ItalyChapter 8: Artificial neural networks used in the survival analysis of breast cancer patients: A node negative study. C.T.C. Arsene, P.J. Lisboa, Liverpool John Moores University, UKSection 4 – Application of Machine Learning Methods Chapter 9: The use of artificial neural networks for the diagnosis and estimation of prognosis in cancer patients. A. Marchevsky, Cedars-Sinai Medical Center, Los Angeles, USAChapter 10: Machine learning contribution to solve prognosis medical problems. F. Baronti, A. Micheli, A. Passaro, A.Starita,University of Pisa, ItalyChapter 11: Classification of brain tumours by pattern recognition of Magnetic Resonance Imaging and Spectroscopic data.A. Devos1, S. Van Huffel1 A.W. Simonetti1, M. van der Graaf2, A. Heerschap2, L.M.C. Buydens3 1Katholieke Universiteit Leuven, Belgium2University Nijmegen Medical Centre, The Netherlands3Radboud University Nijmegen, The Netherlands Chapter 12: Towards automatic risk analysis for hereditary non-polyposis colorectal cancer based on pedigree data.M. Kokuer1, R.N.G. Naguib1, P. Jancovic2, H.B. Younghusband3, R. Green31Coventry University, UK2University of Birmingham, UK3University of Newfoundland, CanadaChapter 13: The impact of microarray technology in brain cancer.M. Kounelakis1, M. Zervakis1, X. Kotsiakis21Technical University of Crete, GREECE2District Hospital of Chania, GREECESection 5 – Dissemination of InformationChapter 14: The web and the new generation of medical information. J.M. Fonseca, A.D. Mora, P. BarrosoUniversity of Lisbon, PortugalChapter 15: Geoconda: a web environment for multi-centre research.C. Setzkorn, A.F.G. Taktak, B.E. DamatoRoyal Liverpool University Hospital, Liverpool, UKChapter 16: The development and execution of medical prediction models. M.W. Kattan1, M. Gönen2, P.T. Scardino21The Cleveland Clinic Fondation, Cleveland, USA2Memorial Sloan-Kettering Cancer Center, New York, USA