Taktak / Fisher Outcome Prediction in Cancer

1. Auflage 2006
ISBN: 978-0-08-046803-7
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 482 Seiten

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

Autoren/Hrsg.

Taktak, Azzam F. G.

Fisher, Anthony C.

Inhaltsverzeichnis

1;Cover;1
2;Copyright page;5
3;Foreword;8
4;Contents;10
5;Contributors;14
6;Introduction;18
7;Section 1: The Clinical Problem;22
7.1;Chapter 1: The Predictive Value of Detailed Histological Staging of Surgical Resection Specimens in Oral Cancer;24
7.1.1;1. INTRODUCTION;25
7.1.2;2. PREDICTIVE FEATURES RELATED TO THE PRIMARY TUMOUR;26
7.1.3;3. PREDICTIVE FEATURES RELATED TO THE REGIONAL LYMPH NODES;35
7.1.4;4. DISTANT (SYSTEMIC) METASTASES;38
7.1.5;5. GENERAL PATIENT FEATURES;38
7.1.6;6. MOLECULAR AND BIOLOGICAL MARKERS;38
7.1.7;7. THE WAY AHEAD?;41
7.1.8;REFERENCES;43
7.2;Chapter 2: Survival after Treatment of Intraocular Melanoma;48
7.2.1;1. INTRODUCTION;49
7.2.2;2. INTRAOCULAR MELANOMA;49
7.2.3;3. STATISTICAL METHODS FOR PREDICTING METASTATIC DISEASE;52
7.2.4;4. PREDICTING METASTATIC DEATH WITH NEURAL NETWORKS;54
7.2.5;5. MISCELLANEOUS ERRORS;55
7.2.6;6. A NEURAL NETWORK FOR PREDICTING SURVIVAL IN UVEAL MELANOMA PATIENTS;56
7.2.7;7. CAVEATS REGARDING INTERPRETATION OF SURVIVAL STATISTICS;58
7.2.8;8. FURTHER STUDIES;61
7.2.9;9. CONCLUSIONS;61
7.2.10;REFERENCES;61
7.3;Chapter 3: Recent Developments in Relative Survival Analysis;64
7.3.1;1. INTRODUCTION;65
7.3.2;2. CAUSE-SPECIFIC SURVIVAL;65
7.3.3;3. INDEPENDENCE ASSUMPTION;66
7.3.4;4. EXPECTED SURVIVAL;68
7.3.5;5. RELATIVE SURVIVAL;72
7.3.6;6. POINT OF CURE;76
7.3.7;7. REGRESSION ANALYSIS;77
7.3.8;8. PERIOD ANALYSIS;80
7.3.9;9. AGE STANDARDIZATION;81
7.3.10;10. PARAMETRIC METHODS;81
7.3.11;11. MULTIPLE TUMOURS;82
7.3.12;12. CONCLUSION;82
7.3.13;REFERENCES;83
8;Section 2: Biological and Genetic Factors;86
8.1;Chapter 4: Environmental and Genetic Risk Factors of Lung Cancer;88
8.1.1;1. INTRODUCTION;89
8.1.2;2. LUNG CANCER INCIDENCE AND MORTALITY;89
8.1.3;3. CONCLUSION;105
8.1.4;REFERENCES;108
8.2;Chapter 5: Chaos, Cancer, the Cellular Operating System and the Prediction of Survival in Head and Neck Cancer;122
8.2.1;1. INTRODUCTION;123
8.2.2;2. CANCER AND ITS CAUSATION;124
8.2.3;3. FUNDAMENTAL CELL BIOLOGY AND ONCOLOGY;125
8.2.4;4. A NEW DIRECTION FOR FUNDAMENTAL CELL BIOLOGY AND ONCOLOGY;145
8.2.5;5. COMPLEX SYSTEMS ANALYSIS AS APPLIED TO BIOLOGICAL SYSTEMS AND SURVIVAL ANALYSIS;150
8.2.6;6. METHODS OF ANALYSING FAILURE IN BIOLOGICAL SYSTEMS;151
8.2.7;7. A COMPARISON OF A NEURAL NETWORK WITH COX’S REGRESSION IN PREDICTING SURVIVAL IN OVER 800 PATIENTS;156
8.2.8;8. THE NEURAL NETWORK AND FUNDAMENTAL BIOLOGY AND ONCOLOGY;160
8.2.9;9. THE DIRECTION OF FUTURE WORK;161
8.2.10;10. SUMMARY;162
8.2.11;REFERENCES;163
9;Section 3: Mathematical Background of Prognostic Models;166
9.1;Chapter 6: Flexible Hazard Modelling for Outcome Prediction in Cancer: Perspectives for the Use of Bioinformatics Knowledge;168
9.1.1;1. INTRODUCTION;169
9.1.2;2. FAILURE TIME DATA;170
9.1.3;3. PARTITION AND GROUPING OF FAILURE TIMES;171
9.1.4;4. COMPETING RISKS;173
9.1.5;5. GLMs AND FFANNs;175
9.1.6;6. APPLICATIONS TO CANCER DATA;179
9.1.7;7. CONCLUSIONS;187
9.1.8;REFERENCES;191
9.2;Chapter 7: Information Geometry for Survival Analysis and Feature Selection by Neural Networks;192
9.2.1;1. INTRODUCTION;193
9.2.2;2. SURVIVAL FUNCTIONS;194
9.2.3;3. STANDARD MODELS FOR SURVIVAL ANALYSIS;194
9.2.4;4. THE NEURAL NETWORK MODEL;195
9.2.5;5. LEARNING IN THE CPENN MODEL;196
9.2.6;6. THE BAYESIAN APPROACH TO MODELLING;197
9.2.7;7. VARIABLE SELECTION;199
9.2.8;8. THE LAYERED PROJECTION ALGORITHM;199
9.2.9;9. A SEARCH STRATEGY;202
9.2.10;10. EXPERIMENTS;204
9.2.11;11. CONCLUSION;208
9.2.12;REFERENCES;209
9.3;Chapter 8: Artificial Neural Networks Used in the Survival Analysis of Breast Cancer Patients: A Node-Negative Study;212
9.3.1;1. INTRODUCTION;213
9.3.2;2. BREAST CANCER;214
9.3.3;3. STATISTICAL METHODS IN SURVIVAL ANALYSIS FOR BREAST CANCER CENSORED DATA;216
9.3.4;4. PARAMETRIC MODELS AND COX REGRESSION FOR BREAST CANCER DATA;219
9.3.5;5. ARTIFICIAL NEURAL NETWORKS FOR CENSORED SURVIVAL DATA;223
9.3.6;6. DATA DESCRIPTION;231
9.3.7;7. NODE-NEGATIVE BREAST CANCER PROGNOSIS;232
9.3.8;8. CONCLUSIONS;251
9.3.9;REFERENCES;255
10;Section 4: Application of Machine Learning Methods;262
10.1;Chapter 9: The Use of Artificial Neural Networks for the Diagnosis and Estimation of Prognosis in Cancer Patients;264
10.1.1;1. INTRODUCTION;265
10.1.2;2. ARTIFICIAL NEURAL NETWORK ARCHITECTURE: BASIC CONCEPTS;266
10.1.3;3. APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO THE ESTIMATION OF THE PROGNOSIS OF INDIVIDUAL CANCER PATIENTS;271
10.1.4;4. EXAMPLES OF ARTIFICIAL NEURAL NETWORK APPLICATIONS IN CANCER RESEARCH;273
10.1.5;5. CONCLUSIONS;275
10.1.6;REFERENCES;276
10.2;Chapter 10: Machine Learning Contribution to Solve Prognostic Medical Problems;282
10.2.1;1. INTRODUCTION;283
10.2.2;2. MACHINE LEARNING;284
10.2.3;3. CHARACTERISTICS OF MEDICAL APPLICATIONS;289
10.2.4;4. APPLICATION;291
10.2.5;5. LEARNING STRUCTURED DATA IN MEDICINAL CHEMISTRY AND PERSPECTIVES;300
10.2.6;6. CONCLUSIONS;302
10.2.7;REFERENCES;303
10.3;Chapter 11: Classification of Brain Tumours by Pattern Recognition of Magnetic Resonance Imaging and Spectroscopic Data;306
10.3.1;1. INTRODUCTION;307
10.3.2;2. MAGNETIC RESONANCE;309
10.3.3;3. PATTERN RECOGNITION;317
10.3.4;4. PATTERN RECOGNITION TECHNIQUES;327
10.3.5;5. TOWARDS A MEDICAL DECISION SUPPORT SYSTEM USING MR DATA;333
10.3.6;REFERENCES;335
10.4;Chapter 12: Towards Automatic Risk Analysis for Hereditary Non-Polyposis Colorectal Cancer Based on Pedigree Data;340
10.4.1;1. INTRODUCTION;340
10.4.2;2. DESCRIPTION OF THE PEDIGREE DATABASE;341
10.4.3;3. HNPCC RISK ASSESSMENT;344
10.4.4;4. RESULTS AND DISCUSSION;351
10.4.5;5. SUMMARY;354
10.4.6;REFERENCES;357
10.5;Chapter 13: The Impact of Microarray Technology in Brain Cancer;360
10.5.1;1. INTRODUCTION;361
10.5.2;2. PREPROCESSING MICROARRAY DATA;367
10.5.3;3. CLUSTERING OF MICROARRAY DATA OF BRAIN CANCER;372
10.5.4;4. CLASSIFICATION OF MICROARRAY DATA OF BRAIN CANCER;382
10.5.5;5. CLINICAL VERSUS GENETIC ANALYSIS OF BRAIN CANCER;393
10.5.6;6. CONCLUSIONS;403
10.5.7;REFERENCES;406
11;Section 5: Dissemination of Information;410
11.1;Chapter 14: The Web and the New Generation of Medical Information Systems;412
11.1.1;1. INTRODUCTION;413
11.1.2;2. PATIENTS ONLINE;414
11.1.3;3. ELECTRONIC HEALTH RECORD;420
11.1.4;4. DISTRIBUTED ELECTRONIC HEALTHCARE RECORDS;428
11.1.5;5. CONCLUSIONS;433
11.1.6;REFERENCES;433
11.2;Chapter 15: Geoconda: A Web Environment for Multi-Centre Research;436
11.2.1;1. INTRODUCTION;437
11.2.2;2. MATERIAL AND METHODS;438
11.2.3;3. DESCRIPTION OF THE GEOCONDA WEBSITE;439
11.2.4;4. DISCUSSION;460
11.2.5;5. SUMMARY AND CONCLUSIONS;461
11.2.6;6. FUTURE WORK;461
11.2.7;REFERENCES;462
11.3;Chapter 16: The Development and Execution of Medical Prediction Models;464
11.3.1;1. INTRODUCTION;465
11.3.2;2. METHODOLOGY;467
11.3.3;3. NOMOGRAM;471
11.3.4;4. SOFTWARE;472
11.3.5;REFERENCES;475
12;Subject Index;478