Front cover -- Title page -- Copyright page -- Foreword -- Table of Contents -- Contributors -- Introduction -- Section 1: The Clinical Problem -- Chapter 1: The Predictive Value of Detailed Histological Staging of Surgical Resection Specimens in Oral Cancer -- 1. INTRODUCTION -- 2. PREDICTIVE FEATURES RELATED TO THE PRIMARY TUMOUR -- 3. PREDICTIVE FEATURES RELATED TO THE REGIONAL LYMPH NODES -- 4. DISTANT (SYSTEMIC) METASTASES -- 5. GENERAL PATIENT FEATURES -- 6. MOLECULAR AND BIOLOGICAL MARKERS -- 7. THE WAY AHEAD? -- REFERENCES -- Chapter 2: Survival after Treatment of Intraocular Melanoma -- 1. INTRODUCTION -- 2. INTRAOCULAR MELANOMA -- 3. STATISTICAL METHODS FOR PREDICTING METASTATIC DISEASE -- 4. PREDICTING METASTATIC DEATH WITH NEURAL NETWORKS -- 5. MISCELLANEOUS ERRORS -- 6. A NEURAL NETWORK FOR PREDICTING SURVIVAL IN UVEAL MELANOMA PATIENTS -- 7. CAVEATS REGARDING INTERPRETATION OF SURVIVAL STATISTICS -- 8. FURTHER STUDIES -- 9. CONCLUSIONS -- REFERENCES -- Chapter 3: Recent Developments in Relative Survival Analysis -- 1. INTRODUCTION -- 2. CAUSE-SPECIFIC SURVIVAL -- 3. INDEPENDENCE ASSUMPTION -- 4. EXPECTED SURVIVAL -- 5. RELATIVE SURVIVAL -- 6. POINT OF CURE -- 7. REGRESSION ANALYSIS -- 8. PERIOD ANALYSIS -- 9. AGE STANDARDIZATION -- 10. PARAMETRIC METHODS -- 11. MULTIPLE TUMOURS -- 12. CONCLUSION -- REFERENCES -- Section 2: Biological and Genetic Factors -- Chapter 4: Environmental and Genetic Risk Factors of Lung Cancer -- 1. INTRODUCTION -- 2. LUNG CANCER INCIDENCE AND MORTALITY -- 3. CONCLUSION -- REFERENCES -- Chapter 5: Chaos, Cancer, the Cellular Operating System and the Prediction of Survival in Head and Neck Cancer -- 1. INTRODUCTION -- 2. CANCER AND ITS CAUSATION -- 3. FUNDAMENTAL CELL BIOLOGY AND ONCOLOGY -- 4. A NEW DIRECTION FOR FUNDAMENTAL CELL BIOLOGY AND ONCOLOGY.

5. COMPLEX SYSTEMS ANALYSIS AS APPLIED TO BIOLOGICAL SYSTEMS AND SURVIVAL ANALYSIS -- 6. METHODS OF ANALYSING FAILURE IN BIOLOGICAL SYSTEMS -- 7. A COMPARISON OF A NEURAL NETWORK WITH COX'S REGRESSION IN PREDICTING SURVIVAL IN OVER 800 PATIENTS -- 8. THE NEURAL NETWORK AND FUNDAMENTAL BIOLOGY AND ONCOLOGY -- 9. THE DIRECTION OF FUTURE WORK -- 10. SUMMARY -- REFERENCES -- Section 3: Mathematical Background of Prognostic Models -- Chapter 6: Flexible Hazard Modelling for Outcome Prediction in Cancer: Perspectives for the Use of Bioinformatics Knowledge -- 1. INTRODUCTION -- 2. FAILURE TIME DATA -- 3. PARTITION AND GROUPING OF FAILURE TIMES -- 4. COMPETING RISKS -- 5. GLMs AND FFANNs -- 6. APPLICATIONS TO CANCER DATA -- 7. CONCLUSIONS -- REFERENCES -- Chapter 7: Information Geometry for Survival Analysis and Feature Selection by Neural Networks -- 1. INTRODUCTION -- 2. SURVIVAL FUNCTIONS -- 3. STANDARD MODELS FOR SURVIVAL ANALYSIS -- 4. THE NEURAL NETWORK MODEL -- 5. LEARNING IN THE CPENN MODEL -- 6. THE BAYESIAN APPROACH TO MODELLING -- 7. VARIABLE SELECTION -- 8. THE LAYERED PROJECTION ALGORITHM -- 9. A SEARCH STRATEGY -- 10. EXPERIMENTS -- 11. CONCLUSION -- REFERENCES -- Chapter 8: Artificial Neural Networks Used in the Survival Analysis of Breast Cancer Patients: A Node-Negative Study -- 1. INTRODUCTION -- 2. BREAST CANCER -- 3. STATISTICAL METHODS IN SURVIVAL ANALYSIS FOR BREAST CANCER CENSORED DATA -- 4. PARAMETRIC MODELS AND COX REGRESSION FOR BREAST CANCER DATA -- 5. ARTIFICIAL NEURAL NETWORKS FOR CENSORED SURVIVAL DATA -- 6. DATA DESCRIPTION -- 7. NODE-NEGATIVE BREAST CANCER PROGNOSIS -- 8. CONCLUSIONS -- REFERENCES -- Section 4: Application of Machine Learning Methods -- Chapter 9: The Use of Artificial Neural Networks for the Diagnosis and Estimation of Prognosis in Cancer Patients -- 1. INTRODUCTION.

2. ARTIFICIAL NEURAL NETWORK ARCHITECTURE: BASIC CONCEPTS -- 3. APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO THE ESTIMATION OF THE PROGNOSIS OF INDIVIDUAL CANCER PATIENTS -- 4. EXAMPLES OF ARTIFICIAL NEURAL NETWORK APPLICATIONS IN CANCER RESEARCH -- 5. CONCLUSIONS -- REFERENCES -- Chapter 10: Machine Learning Contribution to Solve Prognostic Medical Problems -- 1. INTRODUCTION -- 2. MACHINE LEARNING -- 3. CHARACTERISTICS OF MEDICAL APPLICATIONS -- 4. APPLICATION -- 5. LEARNING STRUCTURED DATA IN MEDICINAL CHEMISTRY AND PERSPECTIVES -- 6. CONCLUSIONS -- REFERENCES -- Chapter 11: Classification of Brain Tumours by Pattern Recognition of Magnetic Resonance Imaging and Spectroscopic Data -- 1. INTRODUCTION -- 2. MAGNETIC RESONANCE -- 3. PATTERN RECOGNITION -- 4. PATTERN RECOGNITION TECHNIQUES -- 5. TOWARDS A MEDICAL DECISION SUPPORT SYSTEM USING MR DATA -- REFERENCES -- Chapter 12: Towards Automatic Risk Analysis for Hereditary Non-Polyposis Colorectal Cancer Based on Pedigree Data -- 1. INTRODUCTION -- 2. DESCRIPTION OF THE PEDIGREE DATABASE -- 3. HNPCC RISK ASSESSMENT -- 4. RESULTS AND DISCUSSION -- 5. SUMMARY -- REFERENCES -- Chapter 13: The Impact of Microarray Technology in Brain Cancer -- 1. INTRODUCTION -- 2. PREPROCESSING MICROARRAY DATA -- 3. CLUSTERING OF MICROARRAY DATA OF BRAIN CANCER -- 4. CLASSIFICATION OF MICROARRAY DATA OF BRAIN CANCER -- 5. CLINICAL VERSUS GENETIC ANALYSIS OF BRAIN CANCER -- 6. CONCLUSIONS -- REFERENCES -- Section 5: Dissemination of Information -- Chapter 14: The Web and the New Generation of Medical Information Systems -- 1. INTRODUCTION -- 2. PATIENTS ONLINE -- 3. ELECTRONIC HEALTH RECORD -- 4. DISTRIBUTED ELECTRONIC HEALTHCARE RECORDS -- 5. CONCLUSIONS -- REFERENCES -- Chapter 15: Geoconda: A Web Environment for Multi-Centre Research -- 1. INTRODUCTION -- 2. MATERIAL AND METHODS.

3. DESCRIPTION OF THE GEOCONDA WEBSITE -- 4. DISCUSSION -- 5. SUMMARY AND CONCLUSIONS -- 6. FUTURE WORK -- REFERENCES -- Chapter 16: The Development and Execution of Medical Prediction Models -- 1. INTRODUCTION -- 2. METHODOLOGY -- 3. NOMOGRAM -- 4. SOFTWARE -- REFERENCES -- Subject Index.