Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Synergistics, Complex System Analysis and Simulation of Alive Supersystems for Best Management.
5YS of local advanced ECP after combined radical procedures significantly depended on: tumor characteristics, blood cell circuit, cell ratio factors, biochemical factors, hemostasis system, anthropometric data and adjuvant treatment. Optimal strategies for local advanced ECP are: 1) availability of very experienced thoracoabdominal surgeons because of complexity radical procedures; 2) aggressive en block surgery and adequate lymph node dissection for completeness; 3) precise prediction; 4) AT for ECP with unfavorable prognosis.
Similaire à Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Synergistics, Complex System Analysis and Simulation of Alive Supersystems for Best Management.
Similaire à Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Synergistics, Complex System Analysis and Simulation of Alive Supersystems for Best Management. (20)
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Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Synergistics, Complex System Analysis and Simulation of Alive Supersystems for Best Management.
1. Local Advanced Esophageal Cancer
(T3-4N0-2M0): Artificial Intelligence,
Synergistics, Complex System Analysis and
Simulation of Alive Supersystems for Best
Management.
Kshivets Oleg Surgery Department, Bagrationovsk Hospital,
Bagrationovsk, Kaliningrad, Russia
2. ABSTRACT
OBJECTIVE: The survival of patients with local advanced of esophageal cancer (EC) takes several months. Radical operations are extremely
complex and remain the prerogative of several best thoracoabdominal surgeons of the world. The search of optimal treatment plan for EC patients
(ECP) with stage T3-4N0-2M0 was realized. We examined factors in terms of precise prediction of 5-year survival (5YS) of local advanced ECP after
complete (R0) combined esophagogastrectomies (E).
METHODS: We analyzed data of 313 consecutive ECP (age=56.2±9 years; tumor size=8.1±2.8 cm) radically operated and monitored in 1975-2024
(m=233, f=80; Lewis=122, Garlock=191, combined E with resection of trachea, bronchus, lung, aorta, VCS, pericardium, liver, pancreas, diaphragm,
colon, splenectomy=170; only surgery-S=217, adjuvant chemoimmunoradiotherapy-AT=96: 5FU+thymalin/taktivin +radiotherapy 45-50Gy; T3=185,
T4=128; N0=96, N1=38, N2=179, M0=313; G1=71, G2=57, G3=185; squamous=109, adenocarcinoma=194, mix=10. Variables selected for 5YS study were
input levels of 45 blood parameters, sex, age, TNMG, cell type, tumor size. Survival curves were estimated by the Kaplan-Meier method. Differences in
curves between groups of ECP were evaluated using a log-rank test. Multivariate Cox modeling, multi-factor clustering, discriminant analysis, structural
equation modeling, Monte Carlo, bootstrap, synergetics simulation and neural networks computing were used to determine any significant dependence.
RESULTS: Overall life span (LS) was 1243.6±1691.4 days and cumulative 5YS reached 29%, 10 years – 19.2%, 20 years – 13.3%. 55 ECP lived more
than 5 years (LS=4199.4±2295.3 days), 25 – more than 10 years (LS=6256.3±1884.8 days). 186 ECP died because of EC (LS=605.1±289.9 days). AT
significantly improved 5YS (56.8% vs. 22%) (P=0.0000). Cox modeling displayed that 5YS of ECP significantly depended on: Phase Transition N0-N12 in
terms of synergetics, G1-3, histology, AT, age, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), ESS,
prothrombin index, hemorrhage time, protein, residual nitrogen (P=0.000-0.033). Neural networks, genetic algorithm selection and bootstrap simulation
revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (2), healthy cells/CC (3), erythrocytes/CC (4), segmented neutrophils/CC (5),
eosinophils/CC (6), monocytes/CC (7), stick neutrophils/CC (8), lymphocytes/CC (9), leucocytes/CC (10). Correct prediction of 5YS was 100% by neural
networks computing.
CONCLUSIONS: 5YS of local advanced ECP after combined radical procedures significantly depended on: tumor characteristics, blood cell circuit,
cell ratio factors, biochemical factors, hemostasis system, anthropometric data and adjuvant treatment. Optimal strategies for local advanced ECP are:
1) availability of very experienced thoracoabdominal surgeons because of complexity radical procedures; 2) aggressive en block surgery and adequate
lymph node dissection for completeness; 3) precise prediction; 4) AT for ECP with unfavorable prognosis.
12. Results of Neural Networks and Monte Carlo
Computing in Prediction of Esophageal Cancer
Patients (T3-4) Survival after Complete
Esophagogastrectomies (n=241):
Corect Classification Rate=100%
Error=0.000
Area under ROC Curve=1.000
Neural Networks: Baseline Error=0.000
Area under ROC Curve=1.000; Correct
Classification Rate=100%; n=241
Rank Sensitivity
Phase Transition N0---N12 1 701
Eosinophils/Cancer Cells 2 491
Stick Neutrophils/Cancer Cells 3 269
Healthy Cells/Cancer Cells 4 176
Erythrocytes/Cancer Cells 5 175
Monocytes/Cancer Cells 6 151
Lymphocytes/Cancer Cells 7 124
Thrombocytes/Cancer Cells 8 100
Segmented Neutrophils/Cancer Cells 9 30
Leucocytes/Cancer Cells 10 24
13. Results of Bootstrap Simulation in Prediction
of Esophageal Cancer Patients Survival (T3-4)
after Complete Esophagogastrectomies (n=241):
Bootstrap Simulation:
Number of Samples=3333
Rank Kendall
Tau
P<
Hemorrhage Time 1 -0.171 0.05
Blood Chlorides 2 0.171 0.05
Esophageal/Cardioesophageal Cancer 3 -0.151 0.05
Prothrombin Index 4 -0.146 0.05
Phase Transition N0---N12 5 -0.144 0.05
Protein 6 -0.142 0.05
14. Results of Kohonen Self-Organizing
Neural Networks Computing in
Prediction of Esophageal Cancer Patients (T3-4) Survival after Complete Esophagogastrectomies
(n=241):
16. Prognostic Equation Models of
Esophageal Cancer Patients (T3-4)
Survival after Complete
Esophagogastrectomies (n=241):
17. Prognostic Equation Models of
Esophageal Cancer Patients (T3-4)
Survival after Complete
Esophagogastrectomies (n=241):
18. Prognostic Equation Models of
Esophageal Cancer Patients (T3-4)
Survival after Complete
Esophagogastrectomies (n=241):
19. Prognostic Equation Models of
Esophageal Cancer Patients (T3-4)
Survival after Complete
Esophagogastrectomies (n=241):
20. SEPATH Modeling in Prediction of
Esophageal Cancer Patients (T3-4)
Survival after Complete
Esophagogastrectomies (n=241):
21. 5-year survival of ECP after radical
procedures significantly depended on:
1) PT N0--N12;
2) Cell Ratio Factors;
3) Blood Cell Circuit;
4) Biochemical Factors;
5) Hemostasis System;
6) Adjuvant Treatment;
7) EC Characteristics;
8) EC Cell Dynamics;
9) Tumor Localization;
10) Anthropometric Data;
11) Surgery Type.
Conclusion:
22. Optimal treatment strategies for
local advanced ECP (T3-4) are:
1) Availability of Sufficient
Quantity of Very Experienced
Thoracoabdominal Surgeons
because of Extreme Complexity of
Radical Procedures;
2) Aggressive en block Combined
Surgery and Adequate Lymph
Node Dissection for
Completeness;
3) Precise Prediction;
4) Adjuvant
Chemoimmunoradiotherapy for
ECP with Unfavorable Prognosis.
Conclusion: