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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]
Schemas of Procedures:
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]
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 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 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]

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Kshivets O. Esophagogastric Cancer Surgery

  • 1. 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
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  • 11. General Esopagogastric Cancer Patients Survival after Complete Ivor-Lewis & Garlock Esophagogastrectomies (Kaplan-Meier) ( n=187 )
  • 12. Results of Univariate Analysis in Prediction of Esopagogastric Cancer Patients Survival ( n=187 )
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  • 20. Prognostic SEPATH-Model of Esophagogastric Cancer Patients Survival after Complete Esophagogastrectomies (n=145)
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  • 25. Ratio Lymphocytes to Cancer Cells Populations in Prediction 5-Year Survival of Esophagogastric Cancer Patients after Complete Esophagogastrectomies (n=145)
  • 26. Holling-Tenner Models of Esophagogastric Cancer Cell Population and Cytotoxic Cell Population Dynamics
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