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
1. Introduction - p. xvi 2. Section 1: The clinical problem - p. xx 3. Chapter 1: The Predictive Value of Detailed Histological Staging of Surgical Resection Specimens in Oral Cancer - p. xx 4. Chapter 2: Survival after Treatment of Intraocular Melanoma - p. 26 5. Chapter 3: Recent Developments in Relative Survival Analysis - p. 41 6. Section 2: Biological and Genetic Factors - p. 64 7. Chapter 4: Environmental and Genetic Risk Factors of Lung Cancer - p. 64 8. Chapter 5: Chaos, Cancer, the Cellular Operating System and the Prediction of Survival in Head and Neck Cancer - p. 100 9. Section 3: Mathematical Background of Prognostic Models - p. 143 10. Chapter 6: Flexible Hazard Modelling for Outcome Prediction in Cancer: Perspectives for the Use of Bioinformatics Knowledge - p. 143 11. Chapter 7: Information Geometry for Survival Analysis and Feature Selection by Neural Networks - p. 170 12. Chapter 8: Artificial Neural Networks Used in the Survival Analysis of Breast Cancer Patients: A Node-Negative Study - p. 189 13. Section 4: Application of Machine Learning Methods - p. 239 14. Chapter 9: The Use of Artificial Neural Networks for the Diagnosis and Estimation of Prognosis in Cancer Patients - p. 239 15. Chapter 10: Machine Learning Contribution to Solve Prognostic Medical Problems - p. 259 16. Chapter 11: Classification of Brain Tumours by Pattern Recognition of Magnetic Resonance Imaging and Spectroscopic Data - p. 283 17. Chapter 12: Towards Automatic Risk Analysis for Hereditary Non-Polyposis Colorectal Cancer Based on Pedigree Data - p. 318 18. Chapter 13: The Impact of Microarray Technology in Brain Cancer - p. 337 19. Section 5: Dissemination of Information - p. 388 20. Chapter 14: The Web and the New Generation of Medical Information Systems - p. 388 21. Chapter 15: Geoconda: A Web Environment for Multi-Centre Research - p. 414 22. Chapter 16: The Development and Execution of Medical Prediction Models - p. 441 23. Subject Index - p. 455
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
1. Introduction - p. xvi 2. Section 1: The clinical problem - p. xx 3. Chapter 1: The Predictive Value of Detailed Histological Staging of Surgical Resection Specimens in Oral Cancer - p. xx 4. Chapter 2: Survival after Treatment of Intraocular Melanoma - p. 26 5. Chapter 3: Recent Developments in Relative Survival Analysis - p. 41 6. Section 2: Biological and Genetic Factors - p. 64 7. Chapter 4: Environmental and Genetic Risk Factors of Lung Cancer - p. 64 8. Chapter 5: Chaos, Cancer, the Cellular Operating System and the Prediction of Survival in Head and Neck Cancer - p. 100 9. Section 3: Mathematical Background of Prognostic Models - p. 143 10. Chapter 6: Flexible Hazard Modelling for Outcome Prediction in Cancer: Perspectives for the Use of Bioinformatics Knowledge - p. 143 11. Chapter 7: Information Geometry for Survival Analysis and Feature Selection by Neural Networks - p. 170 12. Chapter 8: Artificial Neural Networks Used in the Survival Analysis of Breast Cancer Patients: A Node-Negative Study - p. 189 13. Section 4: Application of Machine Learning Methods - p. 239 14. Chapter 9: The Use of Artificial Neural Networks for the Diagnosis and Estimation of Prognosis in Cancer Patients - p. 239 15. Chapter 10: Machine Learning Contribution to Solve Prognostic Medical Problems - p. 259 16. Chapter 11: Classification of Brain Tumours by Pattern Recognition of Magnetic Resonance Imaging and Spectroscopic Data - p. 283 17. Chapter 12: Towards Automatic Risk Analysis for Hereditary Non-Polyposis Colorectal Cancer Based on Pedigree Data - p. 318 18. Chapter 13: The Impact of Microarray Technology in Brain Cancer - p. 337 19. Section 5: Dissemination of Information - p. 388 20. Chapter 14: The Web and the New Generation of Medical Information Systems - p. 388 21. Chapter 15: Geoconda: A Web Environment for Multi-Centre Research - p. 414 22. Chapter 16: The Development and Execution of Medical Prediction Models - p. 441 23. Subject Index - p. 455
