05548nam 2200709Ia 450 991078459290332120200520144314.01-280-74728-597866107472830-08-046803-9(CKB)1000000000357713(EBL)284006(OCoLC)181845279(SSID)ssj0000216939(PQKBManifestationID)11185759(PQKBTitleCode)TC0000216939(PQKBWorkID)10197825(PQKB)10244408(Au-PeEL)EBL284006(CaPaEBR)ebr10158401(CaONFJC)MIL74728(MiAaPQ)EBC284006(EXLCZ)99100000000035771320061222d2007 uy 0engur|n|---|||||txtccrOutcome prediction in cancer[electronic resource] /editors, Azzam F.G. Taktak and Anthony C. Fisher1st ed.Amsterdam ;Boston Elsevier20071 online resource (483 p.)Description based upon print version of record.0-444-52855-5 Includes bibliographical references and index.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. INTRODUCTION2. INTRAOCULAR MELANOMA3. 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 ANALYSIS9. AGE STANDARDIZATION10. 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 ONCOLOGY5. COMPLEX SYSTEMS ANALYSIS AS APPLIED TO BIOLOGICAL SYSTEMS AND SURVIVAL ANALYSIS6. 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 DATA3. PARTITION AND GROUPING OF FAILURE TIMES4. 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; REFERENCESChapter 8: Artificial Neural Networks Used in the Survival Analysis of Breast Cancer Patients: A Node-Negative StudyThis 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 effectivCancerDiagnosisCancerPrognosisNeural networks (Computer science)Survival analysis (Biometry)CancerDiagnosis.CancerPrognosis.Neural networks (Computer science)Survival analysis (Biometry)362.196994616.994Taktak Azzam F. G1517909Fisher Anthony C.Dr.247779MiAaPQMiAaPQMiAaPQBOOK9910784592903321Outcome prediction in cancer3755165UNINA