SlideShare a Scribd company logo
Lymph Node Metastases
of Esophageal Cancer
and Blood Cell Circuit
Kshivets Oleg, MD, PhD
Surgery Department, Roshal Hospital,
Roshal, Moscow, Russia
Abstract
• Lymph Node Metastases of Esophageal Cancer and Blood Cell Circuit
• Kshivets Oleg Surgery Department, Roshal Hospital, Moscow, Russia
• OBJECTIVE: Significance of blood cell circuit in terms of detection of esophageal cancer (EC) patients (ECP)
with lymph node metastases was investigated.
• METHODS: We analyzed data of 543 consecutive ECP (age=56.4±8.8 years; tumor size=6±3.5 cm) radically
operated (R0) and monitored in 1975-2019 (m=405, f=138; 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). Variables
selected for study were input levels of blood cell circuit, sex, age, TNMG. Differences between groups were
evaluated using discriminant analysis, clustering, nonlinear estimation, structural equation modeling, Monte
Carlo, bootstrap simulation and neural networks computing.
• RESULTS: It was revealed that separation of ECP with lymph node metastases (n=268) from ECP without
metastases (n=275) significantly depended on: erythrocytes (abs, total), leucocytes (total), segmented
neutrophils (total), eosinophils (%, abs, total), monocytes (%, abs, total), thrombocytes (abs), coagulation
time, protein, residual nitrogen, cell ratio factors (CRF) (ratio between cancer cells- CC and blood cells
subpopulations), T, G, tumor size, tumor growth, histology (P=0.045-0.000). Neural networks computing,
genetic algorithm selection and bootstrap simulation revealed relationships of lymph node metastases and
CRF: healthy cells/CC (rank=1), erythrocytes/CC (2), monocytes/CC (3), lymphocytes/CC (4), thrombocytes/CC
(5), segmented neutrophils/CC (6), eosinophils/CC (7), leucocytes/CC (8), stick neutrophils/CC (9). Correct
classification N0—N12 was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
• CONCLUSION: Lymph node metastases significantly depended on blood cell circuit.
Data:
• Males…………………………………….….405
• Females…….............................................138
• Age=56.4±8.8 years
• Tumor Size=6.0±3.5 cm
Radical Procedures:
• Esophagogastrectomies Lewis (R0)……………….…….263
• Esophagogastrectomies Garlock (R0)……….................280
• Combined Esophagogastrectomies with Resection of
Pancreas, Liver, Trachea, Lung, Aorta, Vena Cava
Superior, Colon Transversum, Diaphragm, Pericardium,
Splenectomy (R0)………………………………………..…..151
• 2-Field Lymphadenectomy…….……………..…………....374
• 3-Field
Lymphadenectomy.………………………………..…...……169
Staging:
• T1……..126 N0..……275 G1…………157
• T2……..114 N1…........69 G2…………139
• T3……..178 N2…......199 G3…………247
• T4……..125 N1-2…...268 M0…….…...543
• Adenocarcinoma………………………………………………………..308
• Squamous Cell Carcinoma……………………………………..……..225
• Mix………………….....…………………………………………................10
• Early Cancer……………………………...………………………….…..107
• Invasive Cancer…………………………..………………………..…....436
Survival Rate:
• Alive……………………………………....................................284 (52.3%)
• 5-Year Survivors……………………………………………..…183 (33.7%)
• 10-Year Survivors………………………...................................98 (18%)
• Losses……………………..………………………………….….224 (41.3%)
• General Life Span=1892.4±2241 days
• For 5-Year Survivors=4311±2419.7 days
• For 10-Year Survivors=5903.4±2299.4 days
• For Losses=629.2±320.1 days
• Cumulative 5-Year Survival for N0-N12…………………….………51.9%
• Cumulative 5-Year Survival for N0…………..…………….………...73.9%
• Cumulative 5-Year Survival for N12………………………………....27.5%
Results of Univariate Analysis of Phase Transition N0—N1-2 in Prediction of
Esophageal Cancer Patients Survival (n=543):
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
Results of T-testing for Recognition of Lymph Node
Metastases of Esophageal Cancer (n=543):
Variable
Mean
N1-2
Mean
N0
t-value p
Std.Dev.
N1-2
Std.Dev.
N0
Erythrocytes abs 3.9321 4.0480 -2.2878 0.022537 0.6013 0.5793
Eosinophils (%) 2.6119 1.9564 3.3211 0.000957 2.6603 1.8830
Monocytes (%) 3.8657 4.5855 -3.0755 0.002208 2.6670 2.7834
Thrombocytes abs 246.2164 230.4255 2.9488 0.003328 66.1781 58.4573
Coagulation Time 312.5082 267.4299 3.9177 0.000101 142.3578 125.4249
Residual Nitrogen 20.3224 14.5305 9.0438 0.000000 6.7240 8.1151
Protein 70.8358 72.4509 -2.2802 0.022987 8.6025 7.8958
Tumor Size 7.5955 4.4353 11.8861 0.000000 3.0092 3.1813
Eosinophils abs 0.1547 0.1172 2.9204 0.003641 0.1776 0.1169
Monocytes abs 0.2354 0.2933 -2.9455 0.003363 0.2100 0.2461
Erythrocytes tot 18.2003 19.4827 -2.8855 0.004063 4.9583 5.3829
Leucocytes tot 27.4540 29.6694 -2.0117 0.044743 11.8452 13.7212
Eosinophils tot 0.7306 0.5542 2.6457 0.008389 0.9488 0.5603
Segmented Neutrophils tot 17.4354 19.3848 -2.4926 0.012978 7.8233 10.2115
Monocytes tot 1.1030 1.4007 -3.0025 0.002801 1.0883 1.2166
Erythrocytes/Cancer Cells 2.7901 8.4450 -11.1571 0.000000 1.4259 8.1770
Thrombocytes/Cancer Cells 170.5577 466.2988 -10.3004 0.000000 84.0484 462.6386
Leucocytes/Cancer Cells 4.1185 12.4827 -10.0883 0.000000 2.1669 13.4031
Eosinophils/Cancer Cells 0.1048 0.2369 -5.2310 0.000000 0.1228 0.3951
Stick Neutrophils/Cancer Cells 0.0944 0.2351 -5.6168 0.000000 0.1358 0.3874
Segmented Neutrophils/Cancer Cells 2.6124 8.0388 -10.3172 0.000000 1.4583 8.4888
Lymphocytes/Cancer Cells 1.1451 3.3509 -8.0249 0.000000 0.7599 4.4367
Monocytes/Cancer Cells 0.1639 0.6135 -7.2323 0.000000 0.1598 1.0054
Healthy Cells/Cancer Cells 10.1004 28.9642 -11.4397 0.000000 4.6453 26.6020
Weight 65.7948 68.4364 -2.2476 0.025003 12.7561 14.5469
ResuIts of GLZ Analysis in Recognition of
Lymph Node Metastases of Esophageal
Cancer (n=543):
Effect
N0---1_2 - Test of all effects (ECP, n=543)
Distribution : BINOMIAL, Link function: LOGIT
Modeled probability that N0---N1_2 = N0
Degr. of
Freedom
Wald
Stat.
p
Intercept 1 8.54062 0.003473
Leucocytes 1 12.80606 0.000345
Segmented Neutrophils (%) 1 8.33307 0.003893
Residual Nitrogen 1 16.57739 0.000047
Eosinophils abs 1 7.55421 0.005987
Lymphocytes abs 1 8.08126 0.004473
Thrombocytes tot 1 22.93402 0.000002
Thrombocytes/Cancer Cells 1 38.68893 0.000000
Tumor Growth 2 40.52006 0.000000
Results of Neural Networks Computing in
Recognition of Lymph Node Metastases in
Esophageal Cancer Patients (n=543):
Neural Networks: n=543;
Baseline Error=0.000;
Area under ROC Curve=1.000;
Correct Classification Rate=100%
Rank Sensitivity
Healthy Cells/Cancer Cells 1 12920
Erythrocytes/Cancer Cells 2 11262
Monocytes/Cancer Cells 3 7345
Lymphocytes/Cancer Cells 4 6154
Thrombocytes/Cancer Cells 5 6102
Segmented Neutrophils/Cancer Cells 6 4350
Eosinophils/Cancer Cells 7 4268
Leucocytes/Cancer Cells 8 3294
Stick Neutrophils/Cancer Cells 9 3026
Results of Bootstrap Simulation in Recognition of Lymph
Node Metastases in Esophageal Cancer Patients (n=543):
Significant Factors
(Number of Samples=3333)
Ran
k
Kendal
Tau-A
P<
Tumor Size 1 0.273 0.000
T1-4 2 0.268 0.000
Healthy Cells/Cancer Cells 3 -0.265 0.000
Monocytes/Cancer Cells 4 -0.244 0.000
Lymphocytes/Cancer Cells 5 -0.241 0.000
Erythrocytes/Cancer Cells 6 -0.223 0.000
Leucocytes/Cancer Cells 7 -0.219 0.000
Tumor Growth 8 0.218 0.000
Segmented Neutrophils/Cancer Cells 9 -0.215 0.000
Residual Nitrogen 10 0.199 0.000
Phase Transition Early---Invasive Cancer 11 0.195 0.000
Thrombocytes/Cancer Cells 12 -0.177 0.000
Esophageal/Cardioesophageal Cancer 13 0.162 0.000
Procedure Type 14 0.151 0.000
Coagulation Time 15 0.123 0.000
Combined Procedure 16 -0.112 0.000
Histology 17 0.098 0.001
Monocytes tot 18 -0.089 0.01
Monocytes abs 19 -0.083 0.01
Monocytes (%) 20 -0.082 0.01
Hemoglobin 21 -0.069 0.05
Erythrocytes tot 22 -0.067 0.05
Bilirubin 23 -0.063 0.05
Results of Kohonen Self-Organizing Neural Networks
Computing in Recognition of Lymph Node
Metastases of Esophageal Cancer Patients (n=543):
Equation Models in Recognition of Lymph Node
Metastases of Esophageal Cancer Patients (n=543):
SEPATH-Model in Recognition of Lymph Node
Metastases of Esophageal Cancer Patients (n=543):
LYMPH NODE METASTASES OF ESOPHAGEAL
CANCER SIGNIFICANTLY DEPENDED ON:
1) BLOOD CELL CIRCUIT;
2) CELL RATIO FACTORS;
3) BIOCHEMICAL FACTORS;
4) HEMOSTASIS SYSTEM;
5) CANCER CHARACTERISTICS;
6) TUMOR LOCALIZATION;
7) ANTHROPOMETRIC DATA;
8) SURGERY.
Conclusion:
Address:
Oleg Kshivets, M.D., Ph.D.
Consultant Thoracic, Abdominal,
General Surgeon & Surgical Oncologist
• e-mail: okshivets@yahoo.com
• skype: okshivets
• http: //www.ctsnet.org/home/okshivets

More Related Content

What's hot

Kshivets O. Esophageal & Cardioesophageal Cancer Surgery
Kshivets O.  Esophageal & Cardioesophageal Cancer SurgeryKshivets O.  Esophageal & Cardioesophageal Cancer Surgery
Kshivets O. Esophageal & Cardioesophageal Cancer Surgery
Oleg Kshivets
 
Kshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer SurgeryKshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer Surgery
Oleg Kshivets
 
Kshivets sso2013
Kshivets sso2013Kshivets sso2013
Kshivets sso2013
Oleg Kshivets
 
Kshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer SurgeryKshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer Surgery
Oleg Kshivets
 
Kshivets Hong Kong Sydney2020
Kshivets Hong Kong Sydney2020Kshivets Hong Kong Sydney2020
Kshivets Hong Kong Sydney2020
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 barcelona2016
Kshivets barcelona2016Kshivets barcelona2016
Kshivets barcelona2016
Oleg Kshivets
 
Kshivets yokohama iaslc2017
Kshivets yokohama iaslc2017Kshivets yokohama iaslc2017
Kshivets yokohama iaslc2017
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 O. Esophagogastric Cancer Surgery
Kshivets O. Esophagogastric Cancer SurgeryKshivets O. Esophagogastric Cancer Surgery
Kshivets O. Esophagogastric Cancer Surgery
Oleg Kshivets
 
Kshivets iaslc denver2015
Kshivets iaslc denver2015Kshivets iaslc denver2015
Kshivets iaslc denver2015
Oleg Kshivets
 
Kshivets aats new_york2018
Kshivets aats new_york2018Kshivets aats new_york2018
Kshivets aats new_york2018
Oleg Kshivets
 
Kshivets iaslc toronto2018
Kshivets iaslc toronto2018Kshivets iaslc toronto2018
Kshivets iaslc toronto2018
Oleg Kshivets
 
Kshivets wscts2018 ljubljana
Kshivets wscts2018 ljubljanaKshivets wscts2018 ljubljana
Kshivets wscts2018 ljubljana
Oleg Kshivets
 
Kshivets ASCO Chicago2020
Kshivets ASCO Chicago2020Kshivets ASCO Chicago2020
Kshivets ASCO Chicago2020
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
 
Kshivets IASLC 2019
Kshivets IASLC 2019Kshivets IASLC 2019
Kshivets IASLC 2019
Oleg Kshivets
 
Kshivets chicago2016
Kshivets chicago2016Kshivets chicago2016
Kshivets chicago2016
Oleg Kshivets
 
Kshivets wscts2015
Kshivets wscts2015Kshivets wscts2015
Kshivets wscts2015
Oleg Kshivets
 

What's hot (20)

Kshivets O. Esophageal & Cardioesophageal Cancer Surgery
Kshivets O.  Esophageal & Cardioesophageal Cancer SurgeryKshivets O.  Esophageal & Cardioesophageal Cancer Surgery
Kshivets O. Esophageal & Cardioesophageal Cancer Surgery
 
Kshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer SurgeryKshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer Surgery
 
Kshivets sso2013
Kshivets sso2013Kshivets sso2013
Kshivets sso2013
 
Kshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer SurgeryKshivets O. Lung Cancer Surgery
Kshivets O. Lung Cancer Surgery
 
Kshivets Hong Kong Sydney2020
Kshivets Hong Kong Sydney2020Kshivets Hong Kong Sydney2020
Kshivets Hong Kong Sydney2020
 
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 barcelona2016
Kshivets barcelona2016Kshivets barcelona2016
Kshivets barcelona2016
 
Kshivets yokohama iaslc2017
Kshivets yokohama iaslc2017Kshivets yokohama iaslc2017
Kshivets yokohama iaslc2017
 
Lung Cancer: 10-Year Survival
Lung Cancer: 10-Year Survival           Lung Cancer: 10-Year Survival
Lung Cancer: 10-Year Survival
 
Kshivets O. Esophagogastric Cancer Surgery
Kshivets O. Esophagogastric Cancer SurgeryKshivets O. Esophagogastric Cancer Surgery
Kshivets O. Esophagogastric Cancer Surgery
 
Kshivets iaslc denver2015
Kshivets iaslc denver2015Kshivets iaslc denver2015
Kshivets iaslc denver2015
 
Kshivets aats new_york2018
Kshivets aats new_york2018Kshivets aats new_york2018
Kshivets aats new_york2018
 
Kshivets iaslc toronto2018
Kshivets iaslc toronto2018Kshivets iaslc toronto2018
Kshivets iaslc toronto2018
 
Kshivets wscts2018 ljubljana
Kshivets wscts2018 ljubljanaKshivets wscts2018 ljubljana
Kshivets wscts2018 ljubljana
 
Kshivets ASCO Chicago2020
Kshivets ASCO Chicago2020Kshivets ASCO Chicago2020
Kshivets ASCO Chicago2020
 
2021 esmo world_gi_poster_kshivets
2021 esmo world_gi_poster_kshivets2021 esmo world_gi_poster_kshivets
2021 esmo world_gi_poster_kshivets
 
Kshivets IASLC 2019
Kshivets IASLC 2019Kshivets IASLC 2019
Kshivets IASLC 2019
 
Kshivets chicago2016
Kshivets chicago2016Kshivets chicago2016
Kshivets chicago2016
 
Kshivets wscts2015
Kshivets wscts2015Kshivets wscts2015
Kshivets wscts2015
 

Similar to Kshivets wscts2019 sofia

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
 
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
 
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
 
Kshivets astana wscts2017
Kshivets astana wscts2017Kshivets astana wscts2017
Kshivets astana wscts2017
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
 
Kshivets barcelona2019
Kshivets barcelona2019Kshivets barcelona2019
Kshivets barcelona2019
Oleg Kshivets
 
Kshivets barcelona2020
Kshivets barcelona2020Kshivets barcelona2020
Kshivets barcelona2020
Oleg Kshivets
 
Kshivets ASCVTS Moscow2018
Kshivets ASCVTS Moscow2018Kshivets ASCVTS Moscow2018
Kshivets ASCVTS Moscow2018
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 ny2021aats
Kshivets ny2021aatsKshivets ny2021aats
Kshivets ny2021aats
Oleg Kshivets
 
Kshivets elcc2022
Kshivets elcc2022Kshivets elcc2022
Kshivets elcc2022
Oleg Kshivets
 
Kshivets esmo2021
Kshivets esmo2021Kshivets esmo2021
Kshivets esmo2021
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
 
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
 
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
 
• 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
 

Similar to Kshivets wscts2019 sofia (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-...
 
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
 
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...
 
Kshivets astana wscts2017
Kshivets astana wscts2017Kshivets astana wscts2017
Kshivets astana wscts2017
 
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
 
Kshivets barcelona2019
Kshivets barcelona2019Kshivets barcelona2019
Kshivets barcelona2019
 
Kshivets barcelona2020
Kshivets barcelona2020Kshivets barcelona2020
Kshivets barcelona2020
 
Kshivets ASCVTS Moscow2018
Kshivets ASCVTS Moscow2018Kshivets ASCVTS Moscow2018
Kshivets ASCVTS Moscow2018
 
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 ny2021aats
Kshivets ny2021aatsKshivets ny2021aats
Kshivets ny2021aats
 
Kshivets elcc2022
Kshivets elcc2022Kshivets elcc2022
Kshivets elcc2022
 
Kshivets esmo2021
Kshivets esmo2021Kshivets esmo2021
Kshivets esmo2021
 
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
 
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...
 
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...
 
• 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
 

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
 
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 (11)

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...
 
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

Clinical examination of- CRANIAL.- nerves
Clinical examination of- CRANIAL.- nervesClinical examination of- CRANIAL.- nerves
Clinical examination of- CRANIAL.- nerves
DrpoonamHealthclinic
 
Text Book of Nursing Concepts - Fundamental of Nursing
Text Book of Nursing Concepts - Fundamental of NursingText Book of Nursing Concepts - Fundamental of Nursing
Text Book of Nursing Concepts - Fundamental of Nursing
BP KOIRALA INSTITUTE OF HELATH SCIENCS,, NEPAL
 
Safeguarding Reproductive Health- Preventing Fallopian Tube Blockage After a ...
Safeguarding Reproductive Health- Preventing Fallopian Tube Blockage After a ...Safeguarding Reproductive Health- Preventing Fallopian Tube Blockage After a ...
Safeguarding Reproductive Health- Preventing Fallopian Tube Blockage After a ...
FFragrant
 
2nd week of Human development .embryology
2nd week of Human development .embryology2nd week of Human development .embryology
2nd week of Human development .embryology
Mithilesh Chaurasia
 
medical law and ethics presentation .ppt
medical law and ethics presentation .pptmedical law and ethics presentation .ppt
medical law and ethics presentation .ppt
PseudoPocket
 
Types of Hypoxia, Hypercapnia, and Cyanosis
Types of Hypoxia, Hypercapnia, and CyanosisTypes of Hypoxia, Hypercapnia, and Cyanosis
Types of Hypoxia, Hypercapnia, and Cyanosis
MedicoseAcademics
 
PCF-Assessment-Tool_Policy-Guide (1).pdf
PCF-Assessment-Tool_Policy-Guide (1).pdfPCF-Assessment-Tool_Policy-Guide (1).pdf
PCF-Assessment-Tool_Policy-Guide (1).pdf
AbHermoso
 
Article - Design and evaluation of novel inhibitors for the treatment of clea...
Article - Design and evaluation of novel inhibitors for the treatment of clea...Article - Design and evaluation of novel inhibitors for the treatment of clea...
Article - Design and evaluation of novel inhibitors for the treatment of clea...
Trustlife
 
SA Gastro Cure(pancreatic cancer treatment in india).pptx
SA Gastro Cure(pancreatic cancer treatment in india).pptxSA Gastro Cure(pancreatic cancer treatment in india).pptx
SA Gastro Cure(pancreatic cancer treatment in india).pptx
VinothKumar70905
 
Text Book of Critical Care Nursing ICU NURSING
Text Book of Critical Care Nursing  ICU NURSINGText Book of Critical Care Nursing  ICU NURSING
Text Book of Critical Care Nursing ICU NURSING
BP KOIRALA INSTITUTE OF HELATH SCIENCS,, NEPAL
 
THE REVIEW OF THE ENCYCLOPEDIA OF PURE MATERIA MEDICA.BHMS.MATERIA MEDICA.HOM...
THE REVIEW OF THE ENCYCLOPEDIA OF PURE MATERIA MEDICA.BHMS.MATERIA MEDICA.HOM...THE REVIEW OF THE ENCYCLOPEDIA OF PURE MATERIA MEDICA.BHMS.MATERIA MEDICA.HOM...
THE REVIEW OF THE ENCYCLOPEDIA OF PURE MATERIA MEDICA.BHMS.MATERIA MEDICA.HOM...
DRPREETHIJAMESP
 
Text book of comprehensive Medical Surgical Nursing
Text book of comprehensive Medical Surgical NursingText book of comprehensive Medical Surgical Nursing
Text book of comprehensive Medical Surgical Nursing
BP KOIRALA INSTITUTE OF HELATH SCIENCS,, NEPAL
 
2024 07 12 Do you share my autistic traits_ - Google Sheets.pdf
2024 07 12 Do you share my autistic traits_ - Google Sheets.pdf2024 07 12 Do you share my autistic traits_ - Google Sheets.pdf
2024 07 12 Do you share my autistic traits_ - Google Sheets.pdf
CarriePoppy
 
Prostatitis Severity- How to Determine if You Have Mild Symptoms.pptx
Prostatitis Severity- How to Determine if You Have Mild Symptoms.pptxProstatitis Severity- How to Determine if You Have Mild Symptoms.pptx
Prostatitis Severity- How to Determine if You Have Mild Symptoms.pptx
AmandaChou9
 
Stepping Forward to Transform MCL Management: Guidance on the Selection and U...
Stepping Forward to Transform MCL Management: Guidance on the Selection and U...Stepping Forward to Transform MCL Management: Guidance on the Selection and U...
Stepping Forward to Transform MCL Management: Guidance on the Selection and U...
PVI, PeerView Institute for Medical Education
 
Text Book of Operation Theater Nursing OT Nursing
Text Book of Operation Theater Nursing OT NursingText Book of Operation Theater Nursing OT Nursing
Text Book of Operation Theater Nursing OT Nursing
BP KOIRALA INSTITUTE OF HELATH SCIENCS,, NEPAL
 
Approach to Head Injuiry, Intracranial Pressure Measurement and Management.pptx
Approach to Head Injuiry, Intracranial Pressure Measurement and Management.pptxApproach to Head Injuiry, Intracranial Pressure Measurement and Management.pptx
Approach to Head Injuiry, Intracranial Pressure Measurement and Management.pptx
Bipul Thakur
 
STRATEGIES FOR RATIONALISING/REDUCING CAESAREAN SECTION RATE BY USE OF "SION ...
STRATEGIES FOR RATIONALISING/REDUCING CAESAREAN SECTION RATE BY USE OF "SION ...STRATEGIES FOR RATIONALISING/REDUCING CAESAREAN SECTION RATE BY USE OF "SION ...
STRATEGIES FOR RATIONALISING/REDUCING CAESAREAN SECTION RATE BY USE OF "SION ...
Niranjan Chavan
 
Care and Maintenance of Laboratory Equipment in Histotechnology.pptx
Care and Maintenance of Laboratory Equipment in Histotechnology.pptxCare and Maintenance of Laboratory Equipment in Histotechnology.pptx
Care and Maintenance of Laboratory Equipment in Histotechnology.pptx
Dr. Jagroop Singh
 
Lymphoma Made Easy , New Teaching Lectures
Lymphoma Made Easy , New Teaching LecturesLymphoma Made Easy , New Teaching Lectures
Lymphoma Made Easy , New Teaching Lectures
MiadAlsulami
 

Recently uploaded (20)

Clinical examination of- CRANIAL.- nerves
Clinical examination of- CRANIAL.- nervesClinical examination of- CRANIAL.- nerves
Clinical examination of- CRANIAL.- nerves
 
Text Book of Nursing Concepts - Fundamental of Nursing
Text Book of Nursing Concepts - Fundamental of NursingText Book of Nursing Concepts - Fundamental of Nursing
Text Book of Nursing Concepts - Fundamental of Nursing
 
Safeguarding Reproductive Health- Preventing Fallopian Tube Blockage After a ...
Safeguarding Reproductive Health- Preventing Fallopian Tube Blockage After a ...Safeguarding Reproductive Health- Preventing Fallopian Tube Blockage After a ...
Safeguarding Reproductive Health- Preventing Fallopian Tube Blockage After a ...
 
2nd week of Human development .embryology
2nd week of Human development .embryology2nd week of Human development .embryology
2nd week of Human development .embryology
 
medical law and ethics presentation .ppt
medical law and ethics presentation .pptmedical law and ethics presentation .ppt
medical law and ethics presentation .ppt
 
Types of Hypoxia, Hypercapnia, and Cyanosis
Types of Hypoxia, Hypercapnia, and CyanosisTypes of Hypoxia, Hypercapnia, and Cyanosis
Types of Hypoxia, Hypercapnia, and Cyanosis
 
PCF-Assessment-Tool_Policy-Guide (1).pdf
PCF-Assessment-Tool_Policy-Guide (1).pdfPCF-Assessment-Tool_Policy-Guide (1).pdf
PCF-Assessment-Tool_Policy-Guide (1).pdf
 
Article - Design and evaluation of novel inhibitors for the treatment of clea...
Article - Design and evaluation of novel inhibitors for the treatment of clea...Article - Design and evaluation of novel inhibitors for the treatment of clea...
Article - Design and evaluation of novel inhibitors for the treatment of clea...
 
SA Gastro Cure(pancreatic cancer treatment in india).pptx
SA Gastro Cure(pancreatic cancer treatment in india).pptxSA Gastro Cure(pancreatic cancer treatment in india).pptx
SA Gastro Cure(pancreatic cancer treatment in india).pptx
 
Text Book of Critical Care Nursing ICU NURSING
Text Book of Critical Care Nursing  ICU NURSINGText Book of Critical Care Nursing  ICU NURSING
Text Book of Critical Care Nursing ICU NURSING
 
THE REVIEW OF THE ENCYCLOPEDIA OF PURE MATERIA MEDICA.BHMS.MATERIA MEDICA.HOM...
THE REVIEW OF THE ENCYCLOPEDIA OF PURE MATERIA MEDICA.BHMS.MATERIA MEDICA.HOM...THE REVIEW OF THE ENCYCLOPEDIA OF PURE MATERIA MEDICA.BHMS.MATERIA MEDICA.HOM...
THE REVIEW OF THE ENCYCLOPEDIA OF PURE MATERIA MEDICA.BHMS.MATERIA MEDICA.HOM...
 
Text book of comprehensive Medical Surgical Nursing
Text book of comprehensive Medical Surgical NursingText book of comprehensive Medical Surgical Nursing
Text book of comprehensive Medical Surgical Nursing
 
2024 07 12 Do you share my autistic traits_ - Google Sheets.pdf
2024 07 12 Do you share my autistic traits_ - Google Sheets.pdf2024 07 12 Do you share my autistic traits_ - Google Sheets.pdf
2024 07 12 Do you share my autistic traits_ - Google Sheets.pdf
 
Prostatitis Severity- How to Determine if You Have Mild Symptoms.pptx
Prostatitis Severity- How to Determine if You Have Mild Symptoms.pptxProstatitis Severity- How to Determine if You Have Mild Symptoms.pptx
Prostatitis Severity- How to Determine if You Have Mild Symptoms.pptx
 
Stepping Forward to Transform MCL Management: Guidance on the Selection and U...
Stepping Forward to Transform MCL Management: Guidance on the Selection and U...Stepping Forward to Transform MCL Management: Guidance on the Selection and U...
Stepping Forward to Transform MCL Management: Guidance on the Selection and U...
 
Text Book of Operation Theater Nursing OT Nursing
Text Book of Operation Theater Nursing OT NursingText Book of Operation Theater Nursing OT Nursing
Text Book of Operation Theater Nursing OT Nursing
 
Approach to Head Injuiry, Intracranial Pressure Measurement and Management.pptx
Approach to Head Injuiry, Intracranial Pressure Measurement and Management.pptxApproach to Head Injuiry, Intracranial Pressure Measurement and Management.pptx
Approach to Head Injuiry, Intracranial Pressure Measurement and Management.pptx
 
STRATEGIES FOR RATIONALISING/REDUCING CAESAREAN SECTION RATE BY USE OF "SION ...
STRATEGIES FOR RATIONALISING/REDUCING CAESAREAN SECTION RATE BY USE OF "SION ...STRATEGIES FOR RATIONALISING/REDUCING CAESAREAN SECTION RATE BY USE OF "SION ...
STRATEGIES FOR RATIONALISING/REDUCING CAESAREAN SECTION RATE BY USE OF "SION ...
 
Care and Maintenance of Laboratory Equipment in Histotechnology.pptx
Care and Maintenance of Laboratory Equipment in Histotechnology.pptxCare and Maintenance of Laboratory Equipment in Histotechnology.pptx
Care and Maintenance of Laboratory Equipment in Histotechnology.pptx
 
Lymphoma Made Easy , New Teaching Lectures
Lymphoma Made Easy , New Teaching LecturesLymphoma Made Easy , New Teaching Lectures
Lymphoma Made Easy , New Teaching Lectures
 

Kshivets wscts2019 sofia

  • 1. Lymph Node Metastases of Esophageal Cancer and Blood Cell Circuit Kshivets Oleg, MD, PhD Surgery Department, Roshal Hospital, Roshal, Moscow, Russia
  • 2. Abstract • Lymph Node Metastases of Esophageal Cancer and Blood Cell Circuit • Kshivets Oleg Surgery Department, Roshal Hospital, Moscow, Russia • OBJECTIVE: Significance of blood cell circuit in terms of detection of esophageal cancer (EC) patients (ECP) with lymph node metastases was investigated. • METHODS: We analyzed data of 543 consecutive ECP (age=56.4±8.8 years; tumor size=6±3.5 cm) radically operated (R0) and monitored in 1975-2019 (m=405, f=138; 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). Variables selected for study were input levels of blood cell circuit, sex, age, TNMG. Differences between groups were evaluated using discriminant analysis, clustering, nonlinear estimation, structural equation modeling, Monte Carlo, bootstrap simulation and neural networks computing. • RESULTS: It was revealed that separation of ECP with lymph node metastases (n=268) from ECP without metastases (n=275) significantly depended on: erythrocytes (abs, total), leucocytes (total), segmented neutrophils (total), eosinophils (%, abs, total), monocytes (%, abs, total), thrombocytes (abs), coagulation time, protein, residual nitrogen, cell ratio factors (CRF) (ratio between cancer cells- CC and blood cells subpopulations), T, G, tumor size, tumor growth, histology (P=0.045-0.000). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships of lymph node metastases and CRF: healthy cells/CC (rank=1), erythrocytes/CC (2), monocytes/CC (3), lymphocytes/CC (4), thrombocytes/CC (5), segmented neutrophils/CC (6), eosinophils/CC (7), leucocytes/CC (8), stick neutrophils/CC (9). Correct classification N0—N12 was 100% by neural networks computing (area under ROC curve=1.0; error=0.0). • CONCLUSION: Lymph node metastases significantly depended on blood cell circuit.
  • 4. Radical Procedures: • Esophagogastrectomies Lewis (R0)……………….…….263 • Esophagogastrectomies Garlock (R0)……….................280 • Combined Esophagogastrectomies with Resection of Pancreas, Liver, Trachea, Lung, Aorta, Vena Cava Superior, Colon Transversum, Diaphragm, Pericardium, Splenectomy (R0)………………………………………..…..151 • 2-Field Lymphadenectomy…….……………..…………....374 • 3-Field Lymphadenectomy.………………………………..…...……169
  • 5. Staging: • T1……..126 N0..……275 G1…………157 • T2……..114 N1…........69 G2…………139 • T3……..178 N2…......199 G3…………247 • T4……..125 N1-2…...268 M0…….…...543 • Adenocarcinoma………………………………………………………..308 • Squamous Cell Carcinoma……………………………………..……..225 • Mix………………….....…………………………………………................10 • Early Cancer……………………………...………………………….…..107 • Invasive Cancer…………………………..………………………..…....436
  • 6. Survival Rate: • Alive……………………………………....................................284 (52.3%) • 5-Year Survivors……………………………………………..…183 (33.7%) • 10-Year Survivors………………………...................................98 (18%) • Losses……………………..………………………………….….224 (41.3%) • General Life Span=1892.4±2241 days • For 5-Year Survivors=4311±2419.7 days • For 10-Year Survivors=5903.4±2299.4 days • For Losses=629.2±320.1 days • Cumulative 5-Year Survival for N0-N12…………………….………51.9% • Cumulative 5-Year Survival for N0…………..…………….………...73.9% • Cumulative 5-Year Survival for N12………………………………....27.5%
  • 7. Results of Univariate Analysis of Phase Transition N0—N1-2 in Prediction of Esophageal Cancer Patients Survival (n=543): 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
  • 8. Results of T-testing for Recognition of Lymph Node Metastases of Esophageal Cancer (n=543): Variable Mean N1-2 Mean N0 t-value p Std.Dev. N1-2 Std.Dev. N0 Erythrocytes abs 3.9321 4.0480 -2.2878 0.022537 0.6013 0.5793 Eosinophils (%) 2.6119 1.9564 3.3211 0.000957 2.6603 1.8830 Monocytes (%) 3.8657 4.5855 -3.0755 0.002208 2.6670 2.7834 Thrombocytes abs 246.2164 230.4255 2.9488 0.003328 66.1781 58.4573 Coagulation Time 312.5082 267.4299 3.9177 0.000101 142.3578 125.4249 Residual Nitrogen 20.3224 14.5305 9.0438 0.000000 6.7240 8.1151 Protein 70.8358 72.4509 -2.2802 0.022987 8.6025 7.8958 Tumor Size 7.5955 4.4353 11.8861 0.000000 3.0092 3.1813 Eosinophils abs 0.1547 0.1172 2.9204 0.003641 0.1776 0.1169 Monocytes abs 0.2354 0.2933 -2.9455 0.003363 0.2100 0.2461 Erythrocytes tot 18.2003 19.4827 -2.8855 0.004063 4.9583 5.3829 Leucocytes tot 27.4540 29.6694 -2.0117 0.044743 11.8452 13.7212 Eosinophils tot 0.7306 0.5542 2.6457 0.008389 0.9488 0.5603 Segmented Neutrophils tot 17.4354 19.3848 -2.4926 0.012978 7.8233 10.2115 Monocytes tot 1.1030 1.4007 -3.0025 0.002801 1.0883 1.2166 Erythrocytes/Cancer Cells 2.7901 8.4450 -11.1571 0.000000 1.4259 8.1770 Thrombocytes/Cancer Cells 170.5577 466.2988 -10.3004 0.000000 84.0484 462.6386 Leucocytes/Cancer Cells 4.1185 12.4827 -10.0883 0.000000 2.1669 13.4031 Eosinophils/Cancer Cells 0.1048 0.2369 -5.2310 0.000000 0.1228 0.3951 Stick Neutrophils/Cancer Cells 0.0944 0.2351 -5.6168 0.000000 0.1358 0.3874 Segmented Neutrophils/Cancer Cells 2.6124 8.0388 -10.3172 0.000000 1.4583 8.4888 Lymphocytes/Cancer Cells 1.1451 3.3509 -8.0249 0.000000 0.7599 4.4367 Monocytes/Cancer Cells 0.1639 0.6135 -7.2323 0.000000 0.1598 1.0054 Healthy Cells/Cancer Cells 10.1004 28.9642 -11.4397 0.000000 4.6453 26.6020 Weight 65.7948 68.4364 -2.2476 0.025003 12.7561 14.5469
  • 9. ResuIts of GLZ Analysis in Recognition of Lymph Node Metastases of Esophageal Cancer (n=543): Effect N0---1_2 - Test of all effects (ECP, n=543) Distribution : BINOMIAL, Link function: LOGIT Modeled probability that N0---N1_2 = N0 Degr. of Freedom Wald Stat. p Intercept 1 8.54062 0.003473 Leucocytes 1 12.80606 0.000345 Segmented Neutrophils (%) 1 8.33307 0.003893 Residual Nitrogen 1 16.57739 0.000047 Eosinophils abs 1 7.55421 0.005987 Lymphocytes abs 1 8.08126 0.004473 Thrombocytes tot 1 22.93402 0.000002 Thrombocytes/Cancer Cells 1 38.68893 0.000000 Tumor Growth 2 40.52006 0.000000
  • 10. Results of Neural Networks Computing in Recognition of Lymph Node Metastases in Esophageal Cancer Patients (n=543): Neural Networks: n=543; Baseline Error=0.000; Area under ROC Curve=1.000; Correct Classification Rate=100% Rank Sensitivity Healthy Cells/Cancer Cells 1 12920 Erythrocytes/Cancer Cells 2 11262 Monocytes/Cancer Cells 3 7345 Lymphocytes/Cancer Cells 4 6154 Thrombocytes/Cancer Cells 5 6102 Segmented Neutrophils/Cancer Cells 6 4350 Eosinophils/Cancer Cells 7 4268 Leucocytes/Cancer Cells 8 3294 Stick Neutrophils/Cancer Cells 9 3026
  • 11. Results of Bootstrap Simulation in Recognition of Lymph Node Metastases in Esophageal Cancer Patients (n=543): Significant Factors (Number of Samples=3333) Ran k Kendal Tau-A P< Tumor Size 1 0.273 0.000 T1-4 2 0.268 0.000 Healthy Cells/Cancer Cells 3 -0.265 0.000 Monocytes/Cancer Cells 4 -0.244 0.000 Lymphocytes/Cancer Cells 5 -0.241 0.000 Erythrocytes/Cancer Cells 6 -0.223 0.000 Leucocytes/Cancer Cells 7 -0.219 0.000 Tumor Growth 8 0.218 0.000 Segmented Neutrophils/Cancer Cells 9 -0.215 0.000 Residual Nitrogen 10 0.199 0.000 Phase Transition Early---Invasive Cancer 11 0.195 0.000 Thrombocytes/Cancer Cells 12 -0.177 0.000 Esophageal/Cardioesophageal Cancer 13 0.162 0.000 Procedure Type 14 0.151 0.000 Coagulation Time 15 0.123 0.000 Combined Procedure 16 -0.112 0.000 Histology 17 0.098 0.001 Monocytes tot 18 -0.089 0.01 Monocytes abs 19 -0.083 0.01 Monocytes (%) 20 -0.082 0.01 Hemoglobin 21 -0.069 0.05 Erythrocytes tot 22 -0.067 0.05 Bilirubin 23 -0.063 0.05
  • 12. Results of Kohonen Self-Organizing Neural Networks Computing in Recognition of Lymph Node Metastases of Esophageal Cancer Patients (n=543):
  • 13. Equation Models in Recognition of Lymph Node Metastases of Esophageal Cancer Patients (n=543):
  • 14. SEPATH-Model in Recognition of Lymph Node Metastases of Esophageal Cancer Patients (n=543):
  • 15. LYMPH NODE METASTASES OF ESOPHAGEAL CANCER SIGNIFICANTLY DEPENDED ON: 1) BLOOD CELL CIRCUIT; 2) CELL RATIO FACTORS; 3) BIOCHEMICAL FACTORS; 4) HEMOSTASIS SYSTEM; 5) CANCER CHARACTERISTICS; 6) TUMOR LOCALIZATION; 7) ANTHROPOMETRIC DATA; 8) SURGERY. Conclusion:
  • 16. Address: Oleg Kshivets, M.D., Ph.D. Consultant Thoracic, Abdominal, General Surgeon & Surgical Oncologist • e-mail: okshivets@yahoo.com • skype: okshivets • http: //www.ctsnet.org/home/okshivets