Kshivets O. Esophagogastric Cancer Surgery

Oleg Kshivets
Oleg Kshivetsthoracic/abdominal/general surgeon & surgical oncologist at Siauliai Public Hospital
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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