OPTIMIZATION OF TREATMENT FOR PATIENTS WITH LOCAL GASTRIC CANCER RELAPSE AFTER COMPLETE STOMACH STUMP EXTIRPATIONS Oleg Kshivets, MD, PhD Department of Surgery, Siauliai Public Hospital & Cancer Center, Siauliai, Lithuania 2007 Gastrointestinal Cancers Symposium, January 19-21, 2007, Orlando, FL, the USA
OPTIMIZATION OF TREATMENT FOR PATIENTS WITH LOCAL GASTRIC CANCER RELAPSE AFTER COMPLETE STOMACH STUMP EXTIRPATIONS
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OPTIMIZATION OF TREATMENT FOR PATIENTS WITH LOCAL GASTRIC CANCER RELAPSE AFTER
COMPLETE STOMACH STUMP EXTIRPATIONS
Oleg Kshivets, MD, PhD Department of Surgery, Siauliai Public Hospital & Cancer Center, Siauliai, Lithuania2007 Gastrointestinal Cancers Symposium, January 19-21, 2007, Orlando, FL, the USA
Abstract• OBJECTIVE: The survival of patients with local relapse of gastric cancer (RGC) after subtotal
gastrectomies takes several months. Repeated radical operations are extremely complex and remain the prerogative of several best surgeons of the world. We examined the clinicomorphologic factors associated with the low- and high-risk of generalization of RGC (T1-4N0-2M0) after complete stomach stump extirpations (SSE). Relapses were diagnosed during 1-4 years after complete subtotal gastrectomies. METHODS: We analyzed data of 77 consecutive RGC patients (RGCP) (age=54.1±1.1 years; tumor size=9.0±0.4 cm) radically operated and monitored in 1975-2006 (males=54, females=23; combined SSE with resection of 1-5 adjacent organs: esophagus, pancreas, liver, diaphragm, colon transversum, splenectomies =63; T1=4, T2=10, T3=39, T4=24; N0=25, N1=4, N2=48; G1=12, G2=8, G3=58; adjuvant chemoimmunotherapy 5FU+taktivin/thymalin-AT=16). Variables selected for 5-year survival (5YS) study were input levels of 45 blood parameters, sex, age, TNMPG, cell type, tumor size, AT. Survival curves were estimated by the Kaplan-Meier method. Differences in curves between groups of RGCP were evaluated using a log-rank test. Multivariate Cox modeling, multi-factor clustering, discriminant analysis, structural equation modeling, Monte Carlo, bootstrap simulation and neural networks computing were used to determine any significant dependence. RESULTS: For total of 77 RGCP overall LS was 964.3±154.6 days and cumulative 5YS reached 18.3%. 23 RGCP are alive, 8 RGCP lived more than 5 years and 4 – 10 years without RGC progressing. 53 RGCP died because of RGC LC during first 5 years after surgery. Cox modeling displayed that 5YS of RGCP after SSE significantly depended on: N0-2, T1-4, combined procedures, AT, histology, G1-3, blood monocytes, neutrophils, lymphocytes, eosinophils, ratio of lymphocytes to RGC cells, lymphoid infiltration of RGC, age, hemorrhage time, blood chlorides, RGC growth (P=0.049-0.000). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS of CECP and combined procedures (rank=1), G1-3 (2), gender (3), AT (4), RGC growth (5), age (6), N0-2 (7), weight (8), histology (9), blood monocytes (10), lymphocytes (11), neutrophils (12). CONCLUSIONS: Correct prediction of RGCP survival after SSE was 100% by discriminant analysis and neural networks computing (area under ROC curve=1.0; error=0.0011). AT significantly improved RGCP 5YS after SSE (P=0.046 by log-rank test).
Factors1) Antropometric Factors……………….…….…..42) Blood Analysis…………………………..……..263) Hemostasis Factors……………………..……....34) Cell Ratio Factors………………………….…...9 6) Gastric Cancer Relapse Characteristics..…...127) Biochemic Factors……………………………...78) Treatment Characteristics……………………..39) Survival Data………...…………………………4 In All………………………...…………………..68
Main Problem of Analysis of Alive Supersystems including Combinatorial Optimization (e.g. Cancer Patient Homeostasis, Search of Optimal Treatment Plan ): Phenomenon of «Combinatorial Explosion»
Number of Clinicomorphological Factors:……...…..68Number of Possible Combination for Random Search:……………..………………….n!=68!=2.48e+96 Operation Time of IBM Blue Gene/L Supercomputer (135.5TFLOPS) …………………………5.8e+74 YearsThe Age of Our Universe……….....1.3e+10 Years
Basis:NP RP P n! n*n*2(e+n) or n log n n AI CSA+S+B SMAI - Artificial IntelligenceCSA - Complex System AnalysisS - Statistics B - Biometrics
Classification of Cases by Discriminant Analysis, n=61Observed Pred.Losses Pred.Survivors CorrectLosses 52 1 98.1%5-Year Survivors 2 6 75.0%Total 54 7 95.1%
Classification of Cases by Neural Networks, n=61Observed Pred.Losses Pred.Survivors CorrectLosses 53 0 100%5-Year Survivors 0 8 100%Total 53 8 100%
Ratio Lymphocytes to Cancer Cells Populations in Prediction of Patients Survival with Local Gastric Cancer Relapse after Complete Stomach Stump
Extirpations (n=61)
Conclusions:Optimal treatment strategies for patients with local gastric cancer relapse are:1) dynamic monitoring of gastric cancer patients after gastrectomies for early detection of local cancer relapce; 2) availability of very experienced surgeons because of baffling complexity repeated radical procedures;3) aggressive en block surgery for completeness; 4) precise prediction; 5) adjuvant chemioimmunotherapy for patients with unfavorable prognosis.
Oleg Kshivets, M.D., Ph.D. Consultant Thoracic/Abdominal/General Surgeon & Surgical Oncologist Department of Surgery, Siauliai Public Hospital & Cancer CenterAddress: Tilzes:42-16, LT78206 Siauliai, Lithuania