Section 1 – The Clinical Problem.<br><br>THE PREDICTIVE VALUE OF DETAILED HISTOLOGICAL STAGING OF SURGICAL RESECTION SPECIMENS IN ORAL CANCER<br><br>Chapter 1: The predictive value of detailed histological staging of surgical resection specimens in oral cancer. <br>J. Woolgar <br>Liverpool Dental School, UK<br><br>Chapter 2: Survival after Treatment of Intraocular Melanoma. <br>B.E. Damato, A.F.G. Taktak, <br>Royal Liverpool University Hospital, UK <br><br>Chapter 3: Recent developments in relative survival analysis. <br>T. Hakulinen, T.A. Dyba, <br>Finnish Cancer Registry<br><br>Section 2 – Biological and Genetic Factors<br><br>Chapter 4: Environmental and genetic risk factors of lung cancer. <br>A. Cassidy, J.K. Field, <br>University of Liverpool, UK<br><br>Chapter 5: Chaos, cancer, the cellular operating system and the prediction of survival in head and neck cancer. <br>A.S. Jones, <br>University Hospital Aintree, UK<br><br>Section 3 – Mathematical Background of Prognostic Models<br><br>Chapter 6: Flexible hazard modelling for outcome prediction in cancer - perspectives for the use of bioinformatics knowledge.<br>E.Biganzoli1, P. Boracchi2 <br>1 Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano, Italy<br>2 Università degli Studi di Milano, Milano, Italy<br><br>Chapter 7: Information geometry for survival analysis and feature selection by neural networks. <br>A. Eleuteri 1,2, R. Tagliaferri 3,4, L. Milano 1,2, M. De Laurentiis 1<br> 1Università di Napoli, Italy<br>2INFN sez. Napoli, Italy<br>3Universit`a di Salerno, Italy<br>4INFN sez. distaccata di Salerno, Italy<br><br>Chapter 8: Artificial neural networks used in the survival analysis of breast cancer patients: A node negative study. <br>C.T.C. Arsene, P.J. Lisboa, <br>Liverpool John Moores University, UK<br><br>Section 4 – Application of Machine Learning Methods <br><br>Chapter 9: The use of artificial neural networks for the diagnosis and estimation of prognosis in cancer patients. <br>A. Marchevsky, <br>Cedars-Sinai Medical Center, Los Angeles, USA<br><br>Chapter 10: Machine learning contribution to solve prognosis medical problems. <br>F. Baronti, A. Micheli, A. Passaro, A.Starita,<br>University of Pisa, Italy<br><br>Chapter 11: Classification of brain tumours by pattern recognition of Magnetic Resonance Imaging and Spectroscopic data.<br>A. Devos1, S. Van Huffel1 A.W. Simonetti1, M. van der Graaf2, A. Heerschap2, L.M.C. Buydens3 <br>1Katholieke Universiteit Leuven, Belgium<br>2University Nijmegen Medical Centre, The Netherlands<br>3Radboud University Nijmegen, The Netherlands<br> <br>Chapter 12: Towards automatic risk analysis for hereditary non-polyposis colorectal cancer based on pedigree data.<br>M. Kokuer1, R.N.G. Naguib1, P. Jancovic2, H.B. Younghusband3, R. Green3<br>1Coventry University, UK<br>2University of Birmingham, UK<br>3University of Newfoundland, Canada<br><br>Chapter 13: The impact of microarray technology in brain cancer.<br>M. Kounelakis1, M. Zervakis1, X. Kotsiakis2<br>1Technical University of Crete, GREECE<br>2District Hospital of Chania, GREECE<br><br>Section 5 – Dissemination of Information<br><br>Chapter 14: The web and the new generation of medical information. <br>J.M. Fonseca, A.D. Mora, P. Barroso<br>University of Lisbon, Portugal<br><br>Chapter 15: Geoconda: a web environment for multi-centre research.<br>C. Setzkorn, A.F.G. Taktak, B.E. Damato<br>Royal Liverpool University Hospital, Liverpool, UK<br><br>Chapter 16: The development and execution of medical prediction models. <br>M.W. Kattan1, M. Gönen2, P.T. Scardino2<br>1The Cleveland Clinic Fondation, Cleveland, USA<br>2Memorial Sloan-Kettering Cancer Center, New York, USA
