SlideShare a Scribd company logo
Survival of Esophageal Cancer Patients was Significantly Superior in
Comparison with Cardioesophageal Cancer Patients after Surgery P39
Oleg Kshivets, MD, PhD Surgery Department, Roshal Hospital, Roshal, Moscow, Russia
OBJECTIVE: This study aimed to determine localization influence of tumor for 5-year survival (5YS) of
esophageal (EC) or cardioesophageal (CC) cancer patients (ECP, CEP) after complete en block (R0)
esophagogastrectomies (EG) through left/right thoracoabdominal incision.
METHODS: We analyzed data of 543 consecutive patients (age=56.4±8.8 years; tumor size=6±3.5 cm)
radically operated (R0) and monitored in 1975-2019 (m=405, f=138; ECP=259, CEP=284;
esophagogastrectomies (EG) Garlock=280, EG Lewis=263, combined EG with resection of pancreas, liver,
diaphragm, aorta, VCS, colon transversum, lung, trachea, pericardium, splenectomy=151;
adenocarcinoma=308, squamous=225, mix=10; T1=126, T2=114, T3=178, T4=125; N0=275, N1=69, N2=199;
G1=157, G2=139, G3=247; early EC=107, invasive=436; only surgery=420, adjuvant
chemoimmunoradiotherapy-AT=123: 5-FU+thymalin/taktivin+radiotherapy 45-50Gy). Multivariate Cox
modeling, clustering, SEPATH, Monte Carlo, bootstrap and neural networks computing were used to
determine any significant dependence.
RESULTS: Overall life span (LS) was 1892.4±2241 days and cumulative 5-year survival (5YS) reached
51.9%, 10 years – 45.7%, 20 years – 33.5%. 183 ECP lived more than 5 years (LS=4311±2419.7 days), 98 ECP –
more than 10 years (LS=5903.4±2299.4 days). 224 died because of EC/CC (LS=629.2±320.1 days). 5YS of ECP
(67.3%, LS=2605±2628.9 days) was significantly superior in comparison with CEP (36.4%, LS=1242.6±1558.5
days) (P=0.00000 by log-rank test). AT significantly improved 5YS (68.2% vs. 48.5%) (P=0.00033 by log-rank
test). Cox modeling displayed that 5YS of ECP/CEP significantly depended on: phase transition (PT) N0—
N12 in terms of synergetics, cell ratio factors (ratio between cancer cells- CC and blood cells
subpopulations), T, G, histology, age, AT, localization, blood cells, prothrombin index, coagulation time,
residual nitrogen, blood group, Rh, glucose, protein (P=0.000-0.008). Neural networks, genetic algorithm
selection and bootstrap simulation revealed relationships between 5YS and healthy cells/CC (rank=1), PT
early-invasive EC (rank=2), PT N0—N12 (rank=3), erythrocytes/CC (4), thrombocytes/CC (5), stick
neutrophils/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), eosinophils/CC (9), leucocytes/CC
(10), monocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under
ROC curve=1.0; error=0.0).
CONCLUSIONS: Survival after radical procedures significantly depended on:
1) PT “early—invasive cancer”; 2) PT N0—N12; 3) Cell Ratio Factors; 4) blood cell circuit; 5) biochemical
factors; 6) hemostasis system; 7) AT; 8) EC characteristics; 9) localization: esophageal cancer—
cardioesophageal cancer; 10) anthropometric data.
Survival Function
General Esophageal Cancer Patients Survival, n=543
5-Year Survival=51.9%; 10-Year Survival=45.7%; 20-Year Survival=33.5%
Complete Censored
-5 0 5 10 15 20 25 30 35 40
Years after Esophagogastrectomies
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
CumulativeProportionSurviving
Cumulative Proportion Surviving (Kaplan-Meier)
5-Year Survival of Early ECP=100; 5-Year Survival of Invasive ECP=38.5%,
P=0.00000 by Log-Rank Test
Complete Censored
0 5 10 15 20 25 30 35 40
Years after Esophagogastrectomies
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
CumulativeProportionSurviving
Invasive ECP, n=436
Early ECP, n=107
Cumulative Proportion Surviving (Kaplan-Meier)
5-Year Survival of ECP with N0=73.9%; 5-Year Survival of ECP with N1-2=27.5%
P=0.00000 by Log-Rank Test
Complete Censored
0 5 10 15 20 25 30 35 40
Years after Esophagogastrectomies
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
CumulativeProportionSurviving
ECP with N1-2, n=268
ECP with N0, n=275
Cumulative Proportion Surviving (Kaplan-Meier)
5-Year Survival of ECP after Adjuvant Treatment=68.2%;
5-Year Survival of ECP after Surgery alone=48.5%;
P=0.00033 by Log-Rank Test
Complete Censored
0 5 10 15 20 25 30 35 40
Years after Esophagogastrectomies
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
CumulativeProportionSurviving
Adjuvant Chemoimmunoradiotherapy, n=123
Only Surgery, n=420
Cox Proportional Hazards Results;
ECP/CECP=543;
Factors:
Parameter
Estimate
Standard
Error
Chi-
square
P value
95%
Lower CL
95%
Upper CL
Hazard
Ratio
95%
Hazard
Ratio
Lower CL
95%
Hazard
Ratio
Upper CL
Blood Group 0.24084 0.072842 10.93155 0.000945 0.09807 0.383607 1.272315 1.103040 1.467569
Rh-Factor -0.56749 0.179791 9.96289 0.001597 -0.91988 -0.215110 0.566944 0.398567 0.806453
Hemorrhage Blood 0.00137 0.000411 11.17880 0.000827 0.00057 0.002177 1.001374 1.000568 1.002180
Glucose -0.22903 0.083815 7.46693 0.006284 -0.39330 -0.064756 0.795305 0.674823 0.937296
Residual Nitrogen 0.05126 0.012004 18.23193 0.000020 0.02773 0.074786 1.052594 1.028117 1.077653
Protein 0.02465 0.008997 7.50688 0.006146 0.00702 0.042284 1.024956 1.007041 1.043190
Prothrombin Index 0.01879 0.006672 7.93214 0.004856 0.00571 0.031867 1.018968 1.005730 1.032380
T1-4 0.40958 0.095588 18.35954 0.000018 0.22223 0.596926 1.506179 1.248855 1.816525
N0---N12 0.65015 0.163700 15.77344 0.000071 0.32930 0.970993 1.915823 1.389996 2.640565
Age 0.03171 0.007996 15.72396 0.000073 0.01603 0.047377 1.032214 1.016164 1.048517
Histology -0.34331 0.130570 6.91333 0.008556 -0.59922 -0.087398 0.709419 0.549239 0.916313
G1-3 0.39994 0.089500 19.96816 0.000008 0.22452 0.575354 1.491732 1.251723 1.777760
Adjuvant Chemoimmunoradiotherapy -0.98414 0.202411 23.64005 0.000001 -1.38086 -0.587424 0.373760 0.251362 0.555757
Leucocytes tot -1.40161 0.356959 15.41769 0.000086 -2.10124 -0.701985 0.246200 0.122305 0.495601
Eosinophils tot 1.42230 0.366785 15.03692 0.000105 0.70341 2.141185 4.146644 2.020639 8.509513
Stick Neutrophils tot 1.41661 0.359711 15.50931 0.000082 0.71159 2.121628 4.123112 2.037224 8.344714
Segmented Neutrophils tot 1.42786 0.356560 16.03634 0.000062 0.72901 2.126702 4.169756 2.073034 8.387157
Lymphocytes tot 1.37115 0.358701 14.61190 0.000132 0.66811 2.074194 3.939891 1.950551 7.958130
Monocytes tot 1.31857 0.370179 12.68771 0.000368 0.59303 2.044109 3.738076 1.809469 7.722272
Segmented Neutrophils/Cancer Cells -0.13490 0.045343 8.85118 0.002929 -0.22377 -0.046029 0.873803 0.799498 0.955014
Esophageal Cancer---Cardioesophageal Cancer 0.31674 0.140910 5.05257 0.024589 0.04056 0.592914 1.372640 1.041392 1.809253
Cumulative Proportion Surviving (Kaplan-Meier)
5-Year Survival of ECP=67.3%; 5-Year Survival of CECP=36.4%; P=0.000 by Log-Rank Test
Complete Censored
0 5 10 15 20 25 30 35 40
Years after Esophagogastrectomies
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
CumulativeProportionSurviving
ECP=259
CECP=284
Discriminant Function Analysis Summary:
No. of vars in model=32; n=407
Wilks' Lambda: .39522 approx. F (32,374)=17.885 p<0.0000
Wilks'
Lambda
Partial
Lambda
F-remove
(1,374)
p-value Toler.
1-Toler.
(R-Sqr.)
Hemoglobin 0.405250 0.975248 9.49213 0.002216 0.359799 0.640201
Erythrocytes 0.407808 0.969131 11.91296 0.000621 0.024344 0.975657
Leucocytes 0.413786 0.955129 17.57030 0.000035 0.000405 0.999595
Stick Neutrophils (%) 0.401817 0.983580 6.24345 0.012894 0.081515 0.918486
Segmented Neutrophils (%) 0.412991 0.956969 16.81737 0.000051 0.010315 0.989685
Lymphocytes (%) 0.404156 0.977887 8.45715 0.003853 0.010516 0.989484
Hemorrhage Blood 0.404068 0.978100 8.37382 0.004030 0.843012 0.156988
Residual Nitrogen 0.438737 0.900811 41.18147 0.000000 0.742369 0.257631
Prothrombin Index 0.399773 0.988609 4.30916 0.038590 0.836103 0.163898
Segmented Neutrophils (abs) 0.417926 0.945668 21.48770 0.000005 0.000587 0.999413
Lymphocytes (abs) 0.407544 0.969759 11.66284 0.000708 0.002901 0.997099
T1-4 0.436492 0.905444 39.05685 0.000000 0.541731 0.458269
N0--N12 0.424456 0.931120 27.66664 0.000000 0.704162 0.295838
Weight 0.407512 0.969834 11.63306 0.000719 0.011910 0.988090
G1-3 0.407735 0.969303 11.84421 0.000644 0.835999 0.164001
Tumor Growth 0.407466 0.969943 11.58955 0.000735 0.653263 0.346737
Adjuvant Chemoimmunoradiotherapy 0.400524 0.986755 5.02014 0.025641 0.911147 0.088853
Combined Procedures 0.405038 0.975759 9.29132 0.002466 0.720317 0.279683
Erythrocytes (tot) 0.405575 0.974467 9.79976 0.001883 0.008919 0.991081
Leucocytes (tot) 0.409886 0.964218 13.87926 0.000225 0.000036 0.999964
Eosinophils (tot) 0.405589 0.974434 9.81255 0.001870 0.010102 0.989898
Stick Neutrophils (tot) 0.400922 0.985776 5.39657 0.020712 0.003166 0.996834
Segmented Neutrophils (tot) 0.411970 0.959341 15.85091 0.000082 0.000068 0.999933
Lymphocytes (tot) 0.408867 0.966622 12.91450 0.000370 0.000381 0.999619
Monocytes (tot) 0.407706 0.969373 11.81657 0.000653 0.005423 0.994577
Leucocytes/Cancer Cells 0.401196 0.985102 5.65605 0.017897 0.000009 0.999991
Eosinophils/Cancer Cells 0.401077 0.985396 5.54290 0.019072 0.009880 0.990120
Stick Neutrophils/Cancer Cells 0.401480 0.984406 5.92440 0.015400 0.008813 0.991187
Segmented Neutrophils/Cancer Cells 0.401377 0.984659 5.82676 0.016264 0.000025 0.999975
Lymphocytes/Cancer Cells 0.401133 0.985257 5.59635 0.018507 0.000082 0.999918
Monocytes/Cancer Cells 0.400441 0.986961 4.94101 0.026824 0.001509 0.998491
Localization: EC vs. CEC 0.400283 0.987351 4.79143 0.029219 0.929113 0.070887
Neural Networks: n=407;
Baseline Error=0.000;
Area under ROC Curve=1.000;
Correct Classification Rate=100%
Rank Sensitivity
Healthy Cells/Cancer Cells
Phase Transition Early---Invasive Cancer
Phase Transition N0---N12
Erythrocytes/Cancer Cells
Thrombocytes/Cancer Cells
Stick Neutrophils/Cancer Cells
Lymphocytes/Cancer Cells
Segmented Neutrophils/Cancer Cells
Eosinophils/Cancer Cells
Leucocytes/Cancer Cells
Monocytes/Cancer Cells
1
2
3
4
5
6
7
8
9
10
11
28794
20554
16562
8666
7464
7425
5836
5771
4024
3734
3230
Significant Factors Rank Kendal Tau-A P
Tumor Size 1 -0.316 0.000
Healthy Cells/Cancer Cells 2 0.315 0.000
T1-4 3 -0.307 0.000
Erythrocytes/Cancer Cells 4 0.307 0.000
Leucocytes/Cancer Cells 5 0.298 0.000
Thrombocytes/Cancer Cells 6 0.293 0.000
Lymphocytes/Cancer Cells 7 0.289 0.000
Segmented Neutrophils/Cancer Cells 8 0.280 0.000
Residual Nitrogen 9 -0.277 0.000
Phase Transition N0---N12 10 -0.248 0.000
Monocytes/Cancer Cells 11 0.240 0.000
Hemorrhage Time 12 -0.233 0.000
Phase Transition Early---Invasive Cancer 13 -0.225 0.000
Esophageal---Cardioesophageal Cancer 14 -0.194 0.000
Procedure Type 15 -0.192 0.000
Eosinophils/Cancer Cells 16 0.163 0.000
Chlorides 17 0.163 0.000
G1-3 18 -0.140 0.000
Tumor Growth 19 -0.117 0.001
Stick Neutrophils/Cancer Cells 20 0.105 0.01
Erythrocytes 21 0.103 0.01
Weight 22 0.100 0.01
Combined Procedure 23 0.098 0.01

More Related Content

What's hot

Kshivets barcelona2016
Kshivets barcelona2016Kshivets barcelona2016
Kshivets barcelona2016
Oleg Kshivets
 
Kshivets wscts2018 ljubljana
Kshivets wscts2018 ljubljanaKshivets wscts2018 ljubljana
Kshivets wscts2018 ljubljana
Oleg Kshivets
 
Kshivets sso2013
Kshivets sso2013Kshivets sso2013
Kshivets sso2013
Oleg Kshivets
 
Kshivets O. Cardioesophageal Cancer Surgery
Kshivets O. Cardioesophageal Cancer SurgeryKshivets O. Cardioesophageal Cancer Surgery
Kshivets O. Cardioesophageal Cancer Surgery
Oleg Kshivets
 
Kshivets iaslc toronto2018
Kshivets iaslc toronto2018Kshivets iaslc toronto2018
Kshivets iaslc toronto2018
Oleg Kshivets
 
Kshivets Hong Kong Sydney2020
Kshivets Hong Kong Sydney2020Kshivets Hong Kong Sydney2020
Kshivets Hong Kong Sydney2020
Oleg Kshivets
 
Kshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer SurgeryKshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer Surgery
Oleg Kshivets
 
Lung Cancer: 10-Year Survival
Lung Cancer: 10-Year Survival           Lung Cancer: 10-Year Survival
Lung Cancer: 10-Year Survival
Oleg Kshivets
 
Kshivets IASLC 2019
Kshivets IASLC 2019Kshivets IASLC 2019
Kshivets IASLC 2019
Oleg Kshivets
 
Kshivets iaslc denver2015
Kshivets iaslc denver2015Kshivets iaslc denver2015
Kshivets iaslc denver2015
Oleg Kshivets
 
Kshivets iaslc singapore2020
Kshivets iaslc singapore2020Kshivets iaslc singapore2020
Kshivets iaslc singapore2020
Oleg Kshivets
 
Kshivets O. Lung Cancer Surgery: Prognosis
Kshivets O. Lung Cancer Surgery: PrognosisKshivets O. Lung Cancer Surgery: Prognosis
Kshivets O. Lung Cancer Surgery: Prognosis
Oleg Kshivets
 
Kshivets wscts2015
Kshivets wscts2015Kshivets wscts2015
Kshivets wscts2015
Oleg Kshivets
 
Kshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer SurgeryKshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer Surgery
Oleg Kshivets
 
Kshivets O. Esophagogastric Cancer Surgery
Kshivets O. Esophagogastric Cancer SurgeryKshivets O. Esophagogastric Cancer Surgery
Kshivets O. Esophagogastric Cancer Surgery
Oleg Kshivets
 
Kshivets yokohama iaslc2017
Kshivets yokohama iaslc2017Kshivets yokohama iaslc2017
Kshivets yokohama iaslc2017
Oleg Kshivets
 
Kshivets O. Lung Cancer: Early Detection and Diagnosis
Kshivets O. Lung Cancer: Early Detection and Diagnosis Kshivets O. Lung Cancer: Early Detection and Diagnosis
Kshivets O. Lung Cancer: Early Detection and Diagnosis
Oleg Kshivets
 
Combined Esophagogastrectomies: Survival Outcomes in Patients with Local Adva...
Combined Esophagogastrectomies: Survival Outcomes in Patients with Local Adva...Combined Esophagogastrectomies: Survival Outcomes in Patients with Local Adva...
Combined Esophagogastrectomies: Survival Outcomes in Patients with Local Adva...
Oleg Kshivets
 
Kshivets milan2014
Kshivets milan2014Kshivets milan2014
Kshivets milan2014
Oleg Kshivets
 
Kshivets barcelona2019
Kshivets barcelona2019Kshivets barcelona2019
Kshivets barcelona2019
Oleg Kshivets
 

What's hot (20)

Kshivets barcelona2016
Kshivets barcelona2016Kshivets barcelona2016
Kshivets barcelona2016
 
Kshivets wscts2018 ljubljana
Kshivets wscts2018 ljubljanaKshivets wscts2018 ljubljana
Kshivets wscts2018 ljubljana
 
Kshivets sso2013
Kshivets sso2013Kshivets sso2013
Kshivets sso2013
 
Kshivets O. Cardioesophageal Cancer Surgery
Kshivets O. Cardioesophageal Cancer SurgeryKshivets O. Cardioesophageal Cancer Surgery
Kshivets O. Cardioesophageal Cancer Surgery
 
Kshivets iaslc toronto2018
Kshivets iaslc toronto2018Kshivets iaslc toronto2018
Kshivets iaslc toronto2018
 
Kshivets Hong Kong Sydney2020
Kshivets Hong Kong Sydney2020Kshivets Hong Kong Sydney2020
Kshivets Hong Kong Sydney2020
 
Kshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer SurgeryKshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer Surgery
 
Lung Cancer: 10-Year Survival
Lung Cancer: 10-Year Survival           Lung Cancer: 10-Year Survival
Lung Cancer: 10-Year Survival
 
Kshivets IASLC 2019
Kshivets IASLC 2019Kshivets IASLC 2019
Kshivets IASLC 2019
 
Kshivets iaslc denver2015
Kshivets iaslc denver2015Kshivets iaslc denver2015
Kshivets iaslc denver2015
 
Kshivets iaslc singapore2020
Kshivets iaslc singapore2020Kshivets iaslc singapore2020
Kshivets iaslc singapore2020
 
Kshivets O. Lung Cancer Surgery: Prognosis
Kshivets O. Lung Cancer Surgery: PrognosisKshivets O. Lung Cancer Surgery: Prognosis
Kshivets O. Lung Cancer Surgery: Prognosis
 
Kshivets wscts2015
Kshivets wscts2015Kshivets wscts2015
Kshivets wscts2015
 
Kshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer SurgeryKshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer Surgery
 
Kshivets O. Esophagogastric Cancer Surgery
Kshivets O. Esophagogastric Cancer SurgeryKshivets O. Esophagogastric Cancer Surgery
Kshivets O. Esophagogastric Cancer Surgery
 
Kshivets yokohama iaslc2017
Kshivets yokohama iaslc2017Kshivets yokohama iaslc2017
Kshivets yokohama iaslc2017
 
Kshivets O. Lung Cancer: Early Detection and Diagnosis
Kshivets O. Lung Cancer: Early Detection and Diagnosis Kshivets O. Lung Cancer: Early Detection and Diagnosis
Kshivets O. Lung Cancer: Early Detection and Diagnosis
 
Combined Esophagogastrectomies: Survival Outcomes in Patients with Local Adva...
Combined Esophagogastrectomies: Survival Outcomes in Patients with Local Adva...Combined Esophagogastrectomies: Survival Outcomes in Patients with Local Adva...
Combined Esophagogastrectomies: Survival Outcomes in Patients with Local Adva...
 
Kshivets milan2014
Kshivets milan2014Kshivets milan2014
Kshivets milan2014
 
Kshivets barcelona2019
Kshivets barcelona2019Kshivets barcelona2019
Kshivets barcelona2019
 

Similar to Kshivets aats new_york2019

Kshivets Oleg Optimization of Management for Esophageal Cancer Patients (T1-...
Kshivets Oleg  Optimization of Management for Esophageal Cancer Patients (T1-...Kshivets Oleg  Optimization of Management for Esophageal Cancer Patients (T1-...
Kshivets Oleg Optimization of Management for Esophageal Cancer Patients (T1-...
Oleg Kshivets
 
Esophageal Cancer: Artificial Intelligence, Synergetics, Complex System Analy...
Esophageal Cancer: Artificial Intelligence, Synergetics, Complex System Analy...Esophageal Cancer: Artificial Intelligence, Synergetics, Complex System Analy...
Esophageal Cancer: Artificial Intelligence, Synergetics, Complex System Analy...
Oleg Kshivets
 
Lung Cancer: Precise Prediction
Lung Cancer: Precise PredictionLung Cancer: Precise Prediction
Lung Cancer: Precise Prediction
Oleg Kshivets
 
Kshivets_ELCC2023.pdf
Kshivets_ELCC2023.pdfKshivets_ELCC2023.pdf
Kshivets_ELCC2023.pdf
Oleg Kshivets
 
Kshivets_ELCC2023.pdf
Kshivets_ELCC2023.pdfKshivets_ELCC2023.pdf
Kshivets_ELCC2023.pdf
Oleg Kshivets
 
Kshivets ASCVTS Moscow2018
Kshivets ASCVTS Moscow2018Kshivets ASCVTS Moscow2018
Kshivets ASCVTS Moscow2018
Oleg Kshivets
 
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...
Oleg Kshivets
 
2021 esmo world_gi_poster_kshivets
2021 esmo world_gi_poster_kshivets2021 esmo world_gi_poster_kshivets
2021 esmo world_gi_poster_kshivets
Oleg Kshivets
 
Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Syner...
Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Syner...Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Syner...
Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Syner...
Oleg Kshivets
 
Kshivets O. Esophageal and Cardioesophageal Cancer Surgery
Kshivets O. Esophageal and Cardioesophageal Cancer SurgeryKshivets O. Esophageal and Cardioesophageal Cancer Surgery
Kshivets O. Esophageal and Cardioesophageal Cancer Surgery
Oleg Kshivets
 
RNA (gene expression) analysis of Prostate cancers and non-cancerous tissues t
RNA (gene expression) analysis of Prostate cancers and non-cancerous tissues tRNA (gene expression) analysis of Prostate cancers and non-cancerous tissues t
RNA (gene expression) analysis of Prostate cancers and non-cancerous tissues t
Tsukuba GeneTechnology Laboratories, Ibaraki, JPN
 
Kshivets aats new_york2018
Kshivets aats new_york2018Kshivets aats new_york2018
Kshivets aats new_york2018
Oleg Kshivets
 
Kshivets_SPB_WSCTS2022Eso.pdf
Kshivets_SPB_WSCTS2022Eso.pdfKshivets_SPB_WSCTS2022Eso.pdf
Kshivets_SPB_WSCTS2022Eso.pdf
Oleg Kshivets
 
Esophageal Cancer: Precise Prediction
Esophageal Cancer: Precise Prediction      Esophageal Cancer: Precise Prediction
Esophageal Cancer: Precise Prediction
Oleg Kshivets
 
Kshivets barcelona2020
Kshivets barcelona2020Kshivets barcelona2020
Kshivets barcelona2020
Oleg Kshivets
 
Kshivets ny2021aats
Kshivets ny2021aatsKshivets ny2021aats
Kshivets ny2021aats
Oleg Kshivets
 
Kshivets barcelona2017
Kshivets barcelona2017Kshivets barcelona2017
Kshivets barcelona2017
Oleg Kshivets
 
Kshivets_SPB_WSCTS2022Lung.pdf
Kshivets_SPB_WSCTS2022Lung.pdfKshivets_SPB_WSCTS2022Lung.pdf
Kshivets_SPB_WSCTS2022Lung.pdf
Oleg Kshivets
 
Survival of Lung Cancer Patients after Lobectomies was Significantly Superior...
Survival of Lung Cancer Patients after Lobectomies was Significantly Superior...Survival of Lung Cancer Patients after Lobectomies was Significantly Superior...
Survival of Lung Cancer Patients after Lobectomies was Significantly Superior...
Oleg Kshivets
 
Kshivets esmo2021
Kshivets esmo2021Kshivets esmo2021
Kshivets esmo2021
Oleg Kshivets
 

Similar to Kshivets aats new_york2019 (20)

Kshivets Oleg Optimization of Management for Esophageal Cancer Patients (T1-...
Kshivets Oleg  Optimization of Management for Esophageal Cancer Patients (T1-...Kshivets Oleg  Optimization of Management for Esophageal Cancer Patients (T1-...
Kshivets Oleg Optimization of Management for Esophageal Cancer Patients (T1-...
 
Esophageal Cancer: Artificial Intelligence, Synergetics, Complex System Analy...
Esophageal Cancer: Artificial Intelligence, Synergetics, Complex System Analy...Esophageal Cancer: Artificial Intelligence, Synergetics, Complex System Analy...
Esophageal Cancer: Artificial Intelligence, Synergetics, Complex System Analy...
 
Lung Cancer: Precise Prediction
Lung Cancer: Precise PredictionLung Cancer: Precise Prediction
Lung Cancer: Precise Prediction
 
Kshivets_ELCC2023.pdf
Kshivets_ELCC2023.pdfKshivets_ELCC2023.pdf
Kshivets_ELCC2023.pdf
 
Kshivets_ELCC2023.pdf
Kshivets_ELCC2023.pdfKshivets_ELCC2023.pdf
Kshivets_ELCC2023.pdf
 
Kshivets ASCVTS Moscow2018
Kshivets ASCVTS Moscow2018Kshivets ASCVTS Moscow2018
Kshivets ASCVTS Moscow2018
 
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...
 
2021 esmo world_gi_poster_kshivets
2021 esmo world_gi_poster_kshivets2021 esmo world_gi_poster_kshivets
2021 esmo world_gi_poster_kshivets
 
Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Syner...
Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Syner...Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Syner...
Local Advanced Esophageal Cancer (T3-4N0-2M0): Artificial Intelligence, Syner...
 
Kshivets O. Esophageal and Cardioesophageal Cancer Surgery
Kshivets O. Esophageal and Cardioesophageal Cancer SurgeryKshivets O. Esophageal and Cardioesophageal Cancer Surgery
Kshivets O. Esophageal and Cardioesophageal Cancer Surgery
 
RNA (gene expression) analysis of Prostate cancers and non-cancerous tissues t
RNA (gene expression) analysis of Prostate cancers and non-cancerous tissues tRNA (gene expression) analysis of Prostate cancers and non-cancerous tissues t
RNA (gene expression) analysis of Prostate cancers and non-cancerous tissues t
 
Kshivets aats new_york2018
Kshivets aats new_york2018Kshivets aats new_york2018
Kshivets aats new_york2018
 
Kshivets_SPB_WSCTS2022Eso.pdf
Kshivets_SPB_WSCTS2022Eso.pdfKshivets_SPB_WSCTS2022Eso.pdf
Kshivets_SPB_WSCTS2022Eso.pdf
 
Esophageal Cancer: Precise Prediction
Esophageal Cancer: Precise Prediction      Esophageal Cancer: Precise Prediction
Esophageal Cancer: Precise Prediction
 
Kshivets barcelona2020
Kshivets barcelona2020Kshivets barcelona2020
Kshivets barcelona2020
 
Kshivets ny2021aats
Kshivets ny2021aatsKshivets ny2021aats
Kshivets ny2021aats
 
Kshivets barcelona2017
Kshivets barcelona2017Kshivets barcelona2017
Kshivets barcelona2017
 
Kshivets_SPB_WSCTS2022Lung.pdf
Kshivets_SPB_WSCTS2022Lung.pdfKshivets_SPB_WSCTS2022Lung.pdf
Kshivets_SPB_WSCTS2022Lung.pdf
 
Survival of Lung Cancer Patients after Lobectomies was Significantly Superior...
Survival of Lung Cancer Patients after Lobectomies was Significantly Superior...Survival of Lung Cancer Patients after Lobectomies was Significantly Superior...
Survival of Lung Cancer Patients after Lobectomies was Significantly Superior...
 
Kshivets esmo2021
Kshivets esmo2021Kshivets esmo2021
Kshivets esmo2021
 

More from Oleg Kshivets

Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...
Oleg Kshivets
 
Gastric Cancer: Сlinical Implementation of Artificial Intelligence, Synergeti...
Gastric Cancer: Сlinical Implementation of Artificial Intelligence, Synergeti...Gastric Cancer: Сlinical Implementation of Artificial Intelligence, Synergeti...
Gastric Cancer: Сlinical Implementation of Artificial Intelligence, Synergeti...
Oleg Kshivets
 
Kshivets_IASLC_Singapore2023.pdf
Kshivets_IASLC_Singapore2023.pdfKshivets_IASLC_Singapore2023.pdf
Kshivets_IASLC_Singapore2023.pdf
Oleg Kshivets
 
KshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdfKshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdf
Oleg Kshivets
 
KshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdfKshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdf
Oleg Kshivets
 
KshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdfKshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdf
Oleg Kshivets
 
Kshivets_WCGIC2023.pdf
Kshivets_WCGIC2023.pdfKshivets_WCGIC2023.pdf
Kshivets_WCGIC2023.pdf
Oleg Kshivets
 
Lung cancer cell dynamics significantly depended on blood cell circuit, bioch...
Lung cancer cell dynamics significantly depended on blood cell circuit, bioch...Lung cancer cell dynamics significantly depended on blood cell circuit, bioch...
Lung cancer cell dynamics significantly depended on blood cell circuit, bioch...
Oleg Kshivets
 
• Gastric cancer prognosis and cell ratio factors
•	Gastric cancer prognosis and cell ratio factors           •	Gastric cancer prognosis and cell ratio factors
• Gastric cancer prognosis and cell ratio factors
Oleg Kshivets
 
Kshivets elcc2022
Kshivets elcc2022Kshivets elcc2022
Kshivets elcc2022
Oleg Kshivets
 
Kshivets gc 10_ys_wjarr-2021-0659
Kshivets gc 10_ys_wjarr-2021-0659Kshivets gc 10_ys_wjarr-2021-0659
Kshivets gc 10_ys_wjarr-2021-0659
Oleg Kshivets
 
Kshivets lc10 ys_wjarr
Kshivets lc10 ys_wjarrKshivets lc10 ys_wjarr
Kshivets lc10 ys_wjarr
Oleg Kshivets
 
Kshivets eso10 y2021
Kshivets eso10 y2021Kshivets eso10 y2021
Kshivets eso10 y2021
Oleg Kshivets
 

More from Oleg Kshivets (13)

Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...
 
Gastric Cancer: Сlinical Implementation of Artificial Intelligence, Synergeti...
Gastric Cancer: Сlinical Implementation of Artificial Intelligence, Synergeti...Gastric Cancer: Сlinical Implementation of Artificial Intelligence, Synergeti...
Gastric Cancer: Сlinical Implementation of Artificial Intelligence, Synergeti...
 
Kshivets_IASLC_Singapore2023.pdf
Kshivets_IASLC_Singapore2023.pdfKshivets_IASLC_Singapore2023.pdf
Kshivets_IASLC_Singapore2023.pdf
 
KshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdfKshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdf
 
KshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdfKshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdf
 
KshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdfKshivetsWSCTS2023_Brazil.pdf
KshivetsWSCTS2023_Brazil.pdf
 
Kshivets_WCGIC2023.pdf
Kshivets_WCGIC2023.pdfKshivets_WCGIC2023.pdf
Kshivets_WCGIC2023.pdf
 
Lung cancer cell dynamics significantly depended on blood cell circuit, bioch...
Lung cancer cell dynamics significantly depended on blood cell circuit, bioch...Lung cancer cell dynamics significantly depended on blood cell circuit, bioch...
Lung cancer cell dynamics significantly depended on blood cell circuit, bioch...
 
• Gastric cancer prognosis and cell ratio factors
•	Gastric cancer prognosis and cell ratio factors           •	Gastric cancer prognosis and cell ratio factors
• Gastric cancer prognosis and cell ratio factors
 
Kshivets elcc2022
Kshivets elcc2022Kshivets elcc2022
Kshivets elcc2022
 
Kshivets gc 10_ys_wjarr-2021-0659
Kshivets gc 10_ys_wjarr-2021-0659Kshivets gc 10_ys_wjarr-2021-0659
Kshivets gc 10_ys_wjarr-2021-0659
 
Kshivets lc10 ys_wjarr
Kshivets lc10 ys_wjarrKshivets lc10 ys_wjarr
Kshivets lc10 ys_wjarr
 
Kshivets eso10 y2021
Kshivets eso10 y2021Kshivets eso10 y2021
Kshivets eso10 y2021
 

Recently uploaded

Call Girl Pune 7339748667 Vip Call Girls Pune
Call Girl Pune 7339748667 Vip Call Girls PuneCall Girl Pune 7339748667 Vip Call Girls Pune
Call Girl Pune 7339748667 Vip Call Girls Pune
Mobile Problem
 
Demystifying Fallopian Tube Blockage- Grading the Differences and Implication...
Demystifying Fallopian Tube Blockage- Grading the Differences and Implication...Demystifying Fallopian Tube Blockage- Grading the Differences and Implication...
Demystifying Fallopian Tube Blockage- Grading the Differences and Implication...
FFragrant
 
anatomy of submandibular region presentation
anatomy of submandibular region presentationanatomy of submandibular region presentation
anatomy of submandibular region presentation
MalaM67
 
Call Girls In Mumbai +91-7426014248 High Profile Call Girl Mumbai
Call Girls In Mumbai +91-7426014248 High Profile Call Girl MumbaiCall Girls In Mumbai +91-7426014248 High Profile Call Girl Mumbai
Call Girls In Mumbai +91-7426014248 High Profile Call Girl Mumbai
Mobile Problem
 
13. PROM premature rupture of membranes
13.  PROM premature rupture of membranes13.  PROM premature rupture of membranes
13. PROM premature rupture of membranes
TigistuMelak
 
PGx Analysis in VarSeq: A User’s Perspective
PGx Analysis in VarSeq: A User’s PerspectivePGx Analysis in VarSeq: A User’s Perspective
PGx Analysis in VarSeq: A User’s Perspective
Golden Helix
 
Tele Optometry (kunj'sppt) / Basics of tele optometry.
Tele Optometry (kunj'sppt) / Basics of tele optometry.Tele Optometry (kunj'sppt) / Basics of tele optometry.
Tele Optometry (kunj'sppt) / Basics of tele optometry.
Kunj Vihari
 
Allopurinol (Anti-gout drug).pptx
Allopurinol (Anti-gout drug).pptxAllopurinol (Anti-gout drug).pptx
Allopurinol (Anti-gout drug).pptx
Madhumita Dixit
 
pharmacology for dummies free pdf download.pdf
pharmacology for dummies free pdf download.pdfpharmacology for dummies free pdf download.pdf
pharmacology for dummies free pdf download.pdf
KerlynIgnacio
 
STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7
STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7
STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7
shruti jagirdar
 
Helminthiasis or Worm infestation in Children for Nursing students
Helminthiasis or Worm infestation in Children for Nursing studentsHelminthiasis or Worm infestation in Children for Nursing students
Helminthiasis or Worm infestation in Children for Nursing students
RAJU B N
 
Brain specific drug delivery.pptx -Mpharm
Brain specific drug delivery.pptx -MpharmBrain specific drug delivery.pptx -Mpharm
Brain specific drug delivery.pptx -Mpharm
MuskanShingari
 
Nutritional deficiency disorder in Child
Nutritional deficiency disorder in ChildNutritional deficiency disorder in Child
Nutritional deficiency disorder in Child
Bhavyakelawadiya
 
Call Girls Electronic City 🥰 Bangalore Call Girl No Advance Book Now
Call Girls Electronic City 🥰 Bangalore Call Girl No Advance Book NowCall Girls Electronic City 🥰 Bangalore Call Girl No Advance Book Now
Call Girls Electronic City 🥰 Bangalore Call Girl No Advance Book Now
saftyhealth48
 
biomechanics of running. Dr.dhwani.pptx
biomechanics of running.   Dr.dhwani.pptxbiomechanics of running.   Dr.dhwani.pptx
biomechanics of running. Dr.dhwani.pptx
Dr. Dhwani kawedia
 
acne vulgaris -Mpharm (2nd semester) Cosmetics and cosmeceuticals
acne vulgaris -Mpharm (2nd semester) Cosmetics and cosmeceuticalsacne vulgaris -Mpharm (2nd semester) Cosmetics and cosmeceuticals
acne vulgaris -Mpharm (2nd semester) Cosmetics and cosmeceuticals
MuskanShingari
 
Nano-gold for Cancer Therapy chemistry investigatory project
Nano-gold for Cancer Therapy chemistry investigatory projectNano-gold for Cancer Therapy chemistry investigatory project
Nano-gold for Cancer Therapy chemistry investigatory project
SIVAVINAYAKPK
 
Ageing, the Elderly, Gerontology and Public Health
Ageing, the Elderly, Gerontology and Public HealthAgeing, the Elderly, Gerontology and Public Health
Ageing, the Elderly, Gerontology and Public Health
phuakl
 
Pharmacology of Prostaglandins, Thromboxanes and Leukotrienes
Pharmacology of Prostaglandins, Thromboxanes and LeukotrienesPharmacology of Prostaglandins, Thromboxanes and Leukotrienes
Pharmacology of Prostaglandins, Thromboxanes and Leukotrienes
Dr. Nikhilkumar Sakle
 
Storyboard on Skin- Innovative Learning (M-pharm) 2nd sem. (Cosmetics)
Storyboard on Skin- Innovative Learning (M-pharm) 2nd sem. (Cosmetics)Storyboard on Skin- Innovative Learning (M-pharm) 2nd sem. (Cosmetics)
Storyboard on Skin- Innovative Learning (M-pharm) 2nd sem. (Cosmetics)
MuskanShingari
 

Recently uploaded (20)

Call Girl Pune 7339748667 Vip Call Girls Pune
Call Girl Pune 7339748667 Vip Call Girls PuneCall Girl Pune 7339748667 Vip Call Girls Pune
Call Girl Pune 7339748667 Vip Call Girls Pune
 
Demystifying Fallopian Tube Blockage- Grading the Differences and Implication...
Demystifying Fallopian Tube Blockage- Grading the Differences and Implication...Demystifying Fallopian Tube Blockage- Grading the Differences and Implication...
Demystifying Fallopian Tube Blockage- Grading the Differences and Implication...
 
anatomy of submandibular region presentation
anatomy of submandibular region presentationanatomy of submandibular region presentation
anatomy of submandibular region presentation
 
Call Girls In Mumbai +91-7426014248 High Profile Call Girl Mumbai
Call Girls In Mumbai +91-7426014248 High Profile Call Girl MumbaiCall Girls In Mumbai +91-7426014248 High Profile Call Girl Mumbai
Call Girls In Mumbai +91-7426014248 High Profile Call Girl Mumbai
 
13. PROM premature rupture of membranes
13.  PROM premature rupture of membranes13.  PROM premature rupture of membranes
13. PROM premature rupture of membranes
 
PGx Analysis in VarSeq: A User’s Perspective
PGx Analysis in VarSeq: A User’s PerspectivePGx Analysis in VarSeq: A User’s Perspective
PGx Analysis in VarSeq: A User’s Perspective
 
Tele Optometry (kunj'sppt) / Basics of tele optometry.
Tele Optometry (kunj'sppt) / Basics of tele optometry.Tele Optometry (kunj'sppt) / Basics of tele optometry.
Tele Optometry (kunj'sppt) / Basics of tele optometry.
 
Allopurinol (Anti-gout drug).pptx
Allopurinol (Anti-gout drug).pptxAllopurinol (Anti-gout drug).pptx
Allopurinol (Anti-gout drug).pptx
 
pharmacology for dummies free pdf download.pdf
pharmacology for dummies free pdf download.pdfpharmacology for dummies free pdf download.pdf
pharmacology for dummies free pdf download.pdf
 
STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7
STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7
STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7
 
Helminthiasis or Worm infestation in Children for Nursing students
Helminthiasis or Worm infestation in Children for Nursing studentsHelminthiasis or Worm infestation in Children for Nursing students
Helminthiasis or Worm infestation in Children for Nursing students
 
Brain specific drug delivery.pptx -Mpharm
Brain specific drug delivery.pptx -MpharmBrain specific drug delivery.pptx -Mpharm
Brain specific drug delivery.pptx -Mpharm
 
Nutritional deficiency disorder in Child
Nutritional deficiency disorder in ChildNutritional deficiency disorder in Child
Nutritional deficiency disorder in Child
 
Call Girls Electronic City 🥰 Bangalore Call Girl No Advance Book Now
Call Girls Electronic City 🥰 Bangalore Call Girl No Advance Book NowCall Girls Electronic City 🥰 Bangalore Call Girl No Advance Book Now
Call Girls Electronic City 🥰 Bangalore Call Girl No Advance Book Now
 
biomechanics of running. Dr.dhwani.pptx
biomechanics of running.   Dr.dhwani.pptxbiomechanics of running.   Dr.dhwani.pptx
biomechanics of running. Dr.dhwani.pptx
 
acne vulgaris -Mpharm (2nd semester) Cosmetics and cosmeceuticals
acne vulgaris -Mpharm (2nd semester) Cosmetics and cosmeceuticalsacne vulgaris -Mpharm (2nd semester) Cosmetics and cosmeceuticals
acne vulgaris -Mpharm (2nd semester) Cosmetics and cosmeceuticals
 
Nano-gold for Cancer Therapy chemistry investigatory project
Nano-gold for Cancer Therapy chemistry investigatory projectNano-gold for Cancer Therapy chemistry investigatory project
Nano-gold for Cancer Therapy chemistry investigatory project
 
Ageing, the Elderly, Gerontology and Public Health
Ageing, the Elderly, Gerontology and Public HealthAgeing, the Elderly, Gerontology and Public Health
Ageing, the Elderly, Gerontology and Public Health
 
Pharmacology of Prostaglandins, Thromboxanes and Leukotrienes
Pharmacology of Prostaglandins, Thromboxanes and LeukotrienesPharmacology of Prostaglandins, Thromboxanes and Leukotrienes
Pharmacology of Prostaglandins, Thromboxanes and Leukotrienes
 
Storyboard on Skin- Innovative Learning (M-pharm) 2nd sem. (Cosmetics)
Storyboard on Skin- Innovative Learning (M-pharm) 2nd sem. (Cosmetics)Storyboard on Skin- Innovative Learning (M-pharm) 2nd sem. (Cosmetics)
Storyboard on Skin- Innovative Learning (M-pharm) 2nd sem. (Cosmetics)
 

Kshivets aats new_york2019

  • 1. Survival of Esophageal Cancer Patients was Significantly Superior in Comparison with Cardioesophageal Cancer Patients after Surgery P39 Oleg Kshivets, MD, PhD Surgery Department, Roshal Hospital, Roshal, Moscow, Russia OBJECTIVE: This study aimed to determine localization influence of tumor for 5-year survival (5YS) of esophageal (EC) or cardioesophageal (CC) cancer patients (ECP, CEP) after complete en block (R0) esophagogastrectomies (EG) through left/right thoracoabdominal incision. METHODS: We analyzed data of 543 consecutive patients (age=56.4±8.8 years; tumor size=6±3.5 cm) radically operated (R0) and monitored in 1975-2019 (m=405, f=138; ECP=259, CEP=284; esophagogastrectomies (EG) Garlock=280, EG Lewis=263, combined EG with resection of pancreas, liver, diaphragm, aorta, VCS, colon transversum, lung, trachea, pericardium, splenectomy=151; adenocarcinoma=308, squamous=225, mix=10; T1=126, T2=114, T3=178, T4=125; N0=275, N1=69, N2=199; G1=157, G2=139, G3=247; early EC=107, invasive=436; only surgery=420, adjuvant chemoimmunoradiotherapy-AT=123: 5-FU+thymalin/taktivin+radiotherapy 45-50Gy). Multivariate Cox modeling, clustering, SEPATH, Monte Carlo, bootstrap and neural networks computing were used to determine any significant dependence. RESULTS: Overall life span (LS) was 1892.4±2241 days and cumulative 5-year survival (5YS) reached 51.9%, 10 years – 45.7%, 20 years – 33.5%. 183 ECP lived more than 5 years (LS=4311±2419.7 days), 98 ECP – more than 10 years (LS=5903.4±2299.4 days). 224 died because of EC/CC (LS=629.2±320.1 days). 5YS of ECP (67.3%, LS=2605±2628.9 days) was significantly superior in comparison with CEP (36.4%, LS=1242.6±1558.5 days) (P=0.00000 by log-rank test). AT significantly improved 5YS (68.2% vs. 48.5%) (P=0.00033 by log-rank test). Cox modeling displayed that 5YS of ECP/CEP significantly depended on: phase transition (PT) N0— N12 in terms of synergetics, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), T, G, histology, age, AT, localization, blood cells, prothrombin index, coagulation time, residual nitrogen, blood group, Rh, glucose, protein (P=0.000-0.008). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and healthy cells/CC (rank=1), PT early-invasive EC (rank=2), PT N0—N12 (rank=3), erythrocytes/CC (4), thrombocytes/CC (5), stick neutrophils/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), eosinophils/CC (9), leucocytes/CC (10), monocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0). CONCLUSIONS: Survival after radical procedures significantly depended on: 1) PT “early—invasive cancer”; 2) PT N0—N12; 3) Cell Ratio Factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) EC characteristics; 9) localization: esophageal cancer— cardioesophageal cancer; 10) anthropometric data. Survival Function General Esophageal Cancer Patients Survival, n=543 5-Year Survival=51.9%; 10-Year Survival=45.7%; 20-Year Survival=33.5% Complete Censored -5 0 5 10 15 20 25 30 35 40 Years after Esophagogastrectomies 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 CumulativeProportionSurviving Cumulative Proportion Surviving (Kaplan-Meier) 5-Year Survival of Early ECP=100; 5-Year Survival of Invasive ECP=38.5%, P=0.00000 by Log-Rank Test Complete Censored 0 5 10 15 20 25 30 35 40 Years after Esophagogastrectomies 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 CumulativeProportionSurviving Invasive ECP, n=436 Early ECP, n=107 Cumulative Proportion Surviving (Kaplan-Meier) 5-Year Survival of ECP with N0=73.9%; 5-Year Survival of ECP with N1-2=27.5% P=0.00000 by Log-Rank Test Complete Censored 0 5 10 15 20 25 30 35 40 Years after Esophagogastrectomies 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 CumulativeProportionSurviving ECP with N1-2, n=268 ECP with N0, n=275 Cumulative Proportion Surviving (Kaplan-Meier) 5-Year Survival of ECP after Adjuvant Treatment=68.2%; 5-Year Survival of ECP after Surgery alone=48.5%; P=0.00033 by Log-Rank Test Complete Censored 0 5 10 15 20 25 30 35 40 Years after Esophagogastrectomies 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 CumulativeProportionSurviving Adjuvant Chemoimmunoradiotherapy, n=123 Only Surgery, n=420 Cox Proportional Hazards Results; ECP/CECP=543; Factors: Parameter Estimate Standard Error Chi- square P value 95% Lower CL 95% Upper CL Hazard Ratio 95% Hazard Ratio Lower CL 95% Hazard Ratio Upper CL Blood Group 0.24084 0.072842 10.93155 0.000945 0.09807 0.383607 1.272315 1.103040 1.467569 Rh-Factor -0.56749 0.179791 9.96289 0.001597 -0.91988 -0.215110 0.566944 0.398567 0.806453 Hemorrhage Blood 0.00137 0.000411 11.17880 0.000827 0.00057 0.002177 1.001374 1.000568 1.002180 Glucose -0.22903 0.083815 7.46693 0.006284 -0.39330 -0.064756 0.795305 0.674823 0.937296 Residual Nitrogen 0.05126 0.012004 18.23193 0.000020 0.02773 0.074786 1.052594 1.028117 1.077653 Protein 0.02465 0.008997 7.50688 0.006146 0.00702 0.042284 1.024956 1.007041 1.043190 Prothrombin Index 0.01879 0.006672 7.93214 0.004856 0.00571 0.031867 1.018968 1.005730 1.032380 T1-4 0.40958 0.095588 18.35954 0.000018 0.22223 0.596926 1.506179 1.248855 1.816525 N0---N12 0.65015 0.163700 15.77344 0.000071 0.32930 0.970993 1.915823 1.389996 2.640565 Age 0.03171 0.007996 15.72396 0.000073 0.01603 0.047377 1.032214 1.016164 1.048517 Histology -0.34331 0.130570 6.91333 0.008556 -0.59922 -0.087398 0.709419 0.549239 0.916313 G1-3 0.39994 0.089500 19.96816 0.000008 0.22452 0.575354 1.491732 1.251723 1.777760 Adjuvant Chemoimmunoradiotherapy -0.98414 0.202411 23.64005 0.000001 -1.38086 -0.587424 0.373760 0.251362 0.555757 Leucocytes tot -1.40161 0.356959 15.41769 0.000086 -2.10124 -0.701985 0.246200 0.122305 0.495601 Eosinophils tot 1.42230 0.366785 15.03692 0.000105 0.70341 2.141185 4.146644 2.020639 8.509513 Stick Neutrophils tot 1.41661 0.359711 15.50931 0.000082 0.71159 2.121628 4.123112 2.037224 8.344714 Segmented Neutrophils tot 1.42786 0.356560 16.03634 0.000062 0.72901 2.126702 4.169756 2.073034 8.387157 Lymphocytes tot 1.37115 0.358701 14.61190 0.000132 0.66811 2.074194 3.939891 1.950551 7.958130 Monocytes tot 1.31857 0.370179 12.68771 0.000368 0.59303 2.044109 3.738076 1.809469 7.722272 Segmented Neutrophils/Cancer Cells -0.13490 0.045343 8.85118 0.002929 -0.22377 -0.046029 0.873803 0.799498 0.955014 Esophageal Cancer---Cardioesophageal Cancer 0.31674 0.140910 5.05257 0.024589 0.04056 0.592914 1.372640 1.041392 1.809253 Cumulative Proportion Surviving (Kaplan-Meier) 5-Year Survival of ECP=67.3%; 5-Year Survival of CECP=36.4%; P=0.000 by Log-Rank Test Complete Censored 0 5 10 15 20 25 30 35 40 Years after Esophagogastrectomies 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 CumulativeProportionSurviving ECP=259 CECP=284 Discriminant Function Analysis Summary: No. of vars in model=32; n=407 Wilks' Lambda: .39522 approx. F (32,374)=17.885 p<0.0000 Wilks' Lambda Partial Lambda F-remove (1,374) p-value Toler. 1-Toler. (R-Sqr.) Hemoglobin 0.405250 0.975248 9.49213 0.002216 0.359799 0.640201 Erythrocytes 0.407808 0.969131 11.91296 0.000621 0.024344 0.975657 Leucocytes 0.413786 0.955129 17.57030 0.000035 0.000405 0.999595 Stick Neutrophils (%) 0.401817 0.983580 6.24345 0.012894 0.081515 0.918486 Segmented Neutrophils (%) 0.412991 0.956969 16.81737 0.000051 0.010315 0.989685 Lymphocytes (%) 0.404156 0.977887 8.45715 0.003853 0.010516 0.989484 Hemorrhage Blood 0.404068 0.978100 8.37382 0.004030 0.843012 0.156988 Residual Nitrogen 0.438737 0.900811 41.18147 0.000000 0.742369 0.257631 Prothrombin Index 0.399773 0.988609 4.30916 0.038590 0.836103 0.163898 Segmented Neutrophils (abs) 0.417926 0.945668 21.48770 0.000005 0.000587 0.999413 Lymphocytes (abs) 0.407544 0.969759 11.66284 0.000708 0.002901 0.997099 T1-4 0.436492 0.905444 39.05685 0.000000 0.541731 0.458269 N0--N12 0.424456 0.931120 27.66664 0.000000 0.704162 0.295838 Weight 0.407512 0.969834 11.63306 0.000719 0.011910 0.988090 G1-3 0.407735 0.969303 11.84421 0.000644 0.835999 0.164001 Tumor Growth 0.407466 0.969943 11.58955 0.000735 0.653263 0.346737 Adjuvant Chemoimmunoradiotherapy 0.400524 0.986755 5.02014 0.025641 0.911147 0.088853 Combined Procedures 0.405038 0.975759 9.29132 0.002466 0.720317 0.279683 Erythrocytes (tot) 0.405575 0.974467 9.79976 0.001883 0.008919 0.991081 Leucocytes (tot) 0.409886 0.964218 13.87926 0.000225 0.000036 0.999964 Eosinophils (tot) 0.405589 0.974434 9.81255 0.001870 0.010102 0.989898 Stick Neutrophils (tot) 0.400922 0.985776 5.39657 0.020712 0.003166 0.996834 Segmented Neutrophils (tot) 0.411970 0.959341 15.85091 0.000082 0.000068 0.999933 Lymphocytes (tot) 0.408867 0.966622 12.91450 0.000370 0.000381 0.999619 Monocytes (tot) 0.407706 0.969373 11.81657 0.000653 0.005423 0.994577 Leucocytes/Cancer Cells 0.401196 0.985102 5.65605 0.017897 0.000009 0.999991 Eosinophils/Cancer Cells 0.401077 0.985396 5.54290 0.019072 0.009880 0.990120 Stick Neutrophils/Cancer Cells 0.401480 0.984406 5.92440 0.015400 0.008813 0.991187 Segmented Neutrophils/Cancer Cells 0.401377 0.984659 5.82676 0.016264 0.000025 0.999975 Lymphocytes/Cancer Cells 0.401133 0.985257 5.59635 0.018507 0.000082 0.999918 Monocytes/Cancer Cells 0.400441 0.986961 4.94101 0.026824 0.001509 0.998491 Localization: EC vs. CEC 0.400283 0.987351 4.79143 0.029219 0.929113 0.070887 Neural Networks: n=407; Baseline Error=0.000; Area under ROC Curve=1.000; Correct Classification Rate=100% Rank Sensitivity Healthy Cells/Cancer Cells Phase Transition Early---Invasive Cancer Phase Transition N0---N12 Erythrocytes/Cancer Cells Thrombocytes/Cancer Cells Stick Neutrophils/Cancer Cells Lymphocytes/Cancer Cells Segmented Neutrophils/Cancer Cells Eosinophils/Cancer Cells Leucocytes/Cancer Cells Monocytes/Cancer Cells 1 2 3 4 5 6 7 8 9 10 11 28794 20554 16562 8666 7464 7425 5836 5771 4024 3734 3230 Significant Factors Rank Kendal Tau-A P Tumor Size 1 -0.316 0.000 Healthy Cells/Cancer Cells 2 0.315 0.000 T1-4 3 -0.307 0.000 Erythrocytes/Cancer Cells 4 0.307 0.000 Leucocytes/Cancer Cells 5 0.298 0.000 Thrombocytes/Cancer Cells 6 0.293 0.000 Lymphocytes/Cancer Cells 7 0.289 0.000 Segmented Neutrophils/Cancer Cells 8 0.280 0.000 Residual Nitrogen 9 -0.277 0.000 Phase Transition N0---N12 10 -0.248 0.000 Monocytes/Cancer Cells 11 0.240 0.000 Hemorrhage Time 12 -0.233 0.000 Phase Transition Early---Invasive Cancer 13 -0.225 0.000 Esophageal---Cardioesophageal Cancer 14 -0.194 0.000 Procedure Type 15 -0.192 0.000 Eosinophils/Cancer Cells 16 0.163 0.000 Chlorides 17 0.163 0.000 G1-3 18 -0.140 0.000 Tumor Growth 19 -0.117 0.001 Stick Neutrophils/Cancer Cells 20 0.105 0.01 Erythrocytes 21 0.103 0.01 Weight 22 0.100 0.01 Combined Procedure 23 0.098 0.01