Kshivets O. Esophagogastric Cancer Surgery

ARTIFICIAL INTELLIGENCE, SYSTEM ANALYSIS AND SIMULATION MODELING IN OPTIMIZATION OF TREATMENT FOR ESOPHAGOGASTRIC CANCER PATIENTS Oleg Kshivets, MD, PhD Department of Surgery, Siauliai Public Hospital & Cancer Center, Siauliai, Lithuania The 2006 Gastrointestinal Cancers Simposium, January 26-28, 2006, San Francisco, CA, the USA

Abstract ARTFICIAL INTELLIGENCE, SYSTEM ANALYSIS AND SIMULATION MODELING IN OPTIMIZATION OF TREATMENT FOR ESOPHAGOGASTRIC CANCER PATIENTS Oleg Kshivets Department of Surgery, Siauliai Public Hospital & Cancer Center, Siauliai, Lithuania ,[object Object]

Factors: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Main Problem of Analysis of Alive Supersystems including Combinatorial Optimization (e.g. Esophagogastric Cancer Patient Homeostasis, Search of Optimal Treatment Plan ): Phenomenon of «Combinatorial Explosion» ,[object Object],[object Object],[object Object],[object Object]

Basis: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Data: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Radical Procedures: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Staging: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Survival: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

General Esopagogastric Cancer Patients Survival after Complete Ivor-Lewis & Garlock Esophagogastrectomies (Kaplan-Meier) ( n=187 )

Results of Univariate Analysis in Prediction of Esopagogastric Cancer Patients Survival ( n=187 )

Results of Cox Regression Modeling in Prediction of Esophagogastric Cancer Patients Survival after Complete Esophagogastrectomies (n=187 ) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Results of Cox Regression Modeling in Prediction of Esophagogastric Cancer Patients Survival after Complete Esophagogastrectomies (n=187 ) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Results of Discriminant Analysis in Prediction of Esophagogastric Cancer Patients Survival after Complete Esophagogastrectomies (n=145) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Results of Logistic Regression Analysis in Prediction of Esophagogastric Cancer Patients Survival after Complete Esophagogastrectomies (n=145) Chi2=105.09; df=18; P=0.00000; Odds ratio=98.813 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Prognostic SEPATH-Model of Esophagogastric Cancer Patients Survival after Complete Esophagogastrectomies (n=145)

Neural Networks in Prediction of Esophagogastric Cancer Patients Survival after Complete Esophagogastrectomies (n=145) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Results of Neural Networks Computing in Prediction of Esophagogastric Cancer Patients Survival after Complete Esophagogastrectomies (n=145) Error=0.0018; Area under ROC Curve=1.00; Correct Classification Rate=100% ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Results of Bootstrap Simulation in Prediction of Esophagogastric Cancer Patients Survival after Complete Esophagogastrectomies (n=145) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Prediction of Esophagogastric Cancer Patients Survival after Complete Esophagogastrectomies (n=145) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Ratio Lymphocytes to Cancer Cells Populations in Prediction 5-Year Survival of Esophagogastric Cancer Patients after Complete Esophagogastrectomies (n=145)

Holling-Tenner Models of Esophagogastric Cancer Cell Population and Cytotoxic Cell Population Dynamics

Esophagogastric Cancer Dynamics

Conclusions: ,[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object]

Kshivets O. Esophagogastric Cancer Surgery

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