LEADER 05548nam 2200709Ia 450 001 9910784592903321 005 20200520144314.0 010 $a1-280-74728-5 010 $a9786610747283 010 $a0-08-046803-9 035 $a(CKB)1000000000357713 035 $a(EBL)284006 035 $a(OCoLC)181845279 035 $a(SSID)ssj0000216939 035 $a(PQKBManifestationID)11185759 035 $a(PQKBTitleCode)TC0000216939 035 $a(PQKBWorkID)10197825 035 $a(PQKB)10244408 035 $a(Au-PeEL)EBL284006 035 $a(CaPaEBR)ebr10158401 035 $a(CaONFJC)MIL74728 035 $a(MiAaPQ)EBC284006 035 $a(EXLCZ)991000000000357713 100 $a20061222d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aOutcome prediction in cancer$b[electronic resource] /$feditors, Azzam F.G. Taktak and Anthony C. Fisher 205 $a1st ed. 210 $aAmsterdam ;$aBoston $cElsevier$d2007 215 $a1 online resource (483 p.) 300 $aDescription based upon print version of record. 311 $a0-444-52855-5 320 $aIncludes bibliographical references and index. 327 $aFront 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 327 $a2. 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 ANALYSIS 327 $a9. 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 ONCOLOGY 327 $a5. 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 DATA 327 $a3. 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; REFERENCES 327 $aChapter 8: Artificial Neural Networks Used in the Survival Analysis of Breast Cancer Patients: A Node-Negative Study 330 $aThis 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 ro?le of bioinformatics. Understanding of the genetic and environmental basis of cancers will help in identifying high-risk populations and developing effectiv 606 $aCancer$xDiagnosis 606 $aCancer$xPrognosis 606 $aNeural networks (Computer science) 606 $aSurvival analysis (Biometry) 615 0$aCancer$xDiagnosis. 615 0$aCancer$xPrognosis. 615 0$aNeural networks (Computer science) 615 0$aSurvival analysis (Biometry) 676 $a362.196994 676 $a616.994 701 $aTaktak$b Azzam F. G$01517909 701 $aFisher$b Anthony C.$cDr.$0247779 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784592903321 996 $aOutcome prediction in cancer$93755165 997 $aUNINA