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Lymph Node Metastases of Lung
Cancer and Blood Cell Circuit
Kshivets Oleg
Roshal Hospital, Roshal, Moscow
Russia
Kshivets Oleg, Roshal Hospital, Russia
DISCLOSURES
Commercial Interest Relationship(s)
No No
Abstract
Lymph Node Metastases of Lung Cancer and Blood Cell Circuit
Kshivets Oleg Surgery Department, Roshal Hospital, Moscow, Russia
OBJECTIVE: Significance of blood cell circuit in terms of detection of non-small cell lung cancer (LC) patients (LCP) with lymph node
metastases was investigated.
METHODS: We analyzed data of 757 consecutive LCP (age=57.6±8.2 years; tumor size=4.1±2.4 cm) radically operated (R0) and
monitored in 1985-2020 (m=654, f=103; upper lobectomies=272, lower lobectomies=176, middle lobectomies=18, bilobectomies=42,
pneumonectomies=249, mediastinal lymph node dissection=757; combined procedures with resection of trachea, carina, atrium, aorta,
VCS, vena azygos, pericardium, liver, diaphragm, ribs, esophagus=192; T1=317, T2=251, T3=132, T4=57; N0=509, N1=130, N2=118,
M0=757; G1=194, G2=238, G3=325; squamous=415, adenocarcinoma=292, large cell=50. 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 LCP with lymph node metastases (n=248) from LCP without metastases (n=509)
significantly depended on: leucocytes (abs, total), segmented neutrophils (%, abs, total), lymphocytes (%), ESS, Rh, coagulation time,
prothrombin index, fibrinogen, heparin tolerance, cell ratio factors (CRF) (ratio between cancer cells- CC and blood cells
subpopulations), T, G, tumor size (P=0.047-0.000). Neural networks computing, genetic algorithm selection and bootstrap simulation
revealed relationships of lymph node metastases and CRF: healthy cells/CC (rank=1), segmented neutrophils/CC (2), leucocytes/CC (3),
erythrocytes/CC (4), lymphocytes/CC (5), thrombocytes/CC (6), eosinophils/CC (7), monocytes/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…………………………………….….654
Females…….............................................103
Age=57.6±8.2 years
Tumor Size=4.1±2.4 cm
Radical Procedures (R0):
Upper Lobectomies……………….……………………………………...272
Lower Lobectomies.………................................................................176
Middle Lobectomies.……………………………………………………….18
Bilobectomies.……………………………………………………………....42
Pneumonectomies…………………………………………………..……249
Combined Procedures with Resection of Trachea, Carina, Atrium,
Aorta, Vena Cava Superior, Vena Azygos, Pericardium, Liver,
Diaphragm, Ribs, Esophagus…………………………………..…..192
Mediastinal Lymphadenectomy.……………………………..…...……757
Staging:
T1……..317 N0..……509 G1…………194
T2……..251 N1…......130 G2…………238
T3……..132 N2…......118 G3…………325
T4………57 N1-2…...248 M0…….…...757
Adenocarcinoma………………………………………………………..292
Squamous Cell Carcinoma……………………………………..……..415
Large Cell Carcinoma………………………………………..................50
Results of Univariate Analysis of Phase Transition N0—N1-2 in Prediction of Lung Cancer Patients Survival (n=757):
Cumulative Proportion Surviving (Kaplan-Meier)
5-Year Survival of LCP with N0=86.9%;
5-Year Survival of LCP with N1-2=43.5%;
P=0.00000 by Log-Rank Test
Complete Censored
0 5 10 15 20 25 30 35
Years after Lobectomies/Pneumonectomies
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
CumulativeProportionSurviving
LCP with N1-2, n=248
LCP with N0, n=509
Results of T-testing for Recognition of Lymph Node Metastases of Lung Cancer (n=757):
Variable
Mean
N12
Mean
N0
t-value p
Std.Dev.
N12
Std.Dev.
N0
Rh-factor 1.0766 1.1415 -2.58260.009993 0.2665 0.3488
Tumor Grouth 1.4637 1.7073 -6.68630.000000 0.4997 0.4555
Histology 1.4556 1.6464 -3.09570.002036 0.7515 0.8161
G1-3 2.3589 2.0825 4.45900.000009 0.7610 0.8187
T1-4 2.3790 1.6758 10.29060.000000 0.9052 0.8711
Tumor Size 5.3391 3.5006 10.56150.000000 2.5529 2.0836
Segmented Neutrophils 67.4153 64.1454 3.81450.000148 10.6349 11.2749
Lymphocytes 23.2500 26.1218 -3.68940.000241 9.2763 10.4077
Segmented Neutrophils (abs) 4.4840 3.8665 4.26340.000023 2.1987 1.6878
ESS 20.7379 16.2397 3.94980.000086 16.5471 13.7226
Coagulation Time 245.9876224.4051 2.84550.004555 95.8202 98.9624
Prothrombin Index 97.0847 94.3124 3.54230.000421 10.3476 9.9867
Fibrinogen-B 1.2782 1.1768 2.42650.015478 0.6420 0.4821
Heparin Tolerance 208.6863192.1418 2.36650.018211107.6826 80.4693
Erythrocytes/Cancer Cells 4.5499 8.5338-10.02160.000000 2.1211 6.0810
Thrombocytes/Cancer Cells 254.9144457.7578 -9.30610.000000118.4503333.0530
Lecocytes/ Cancer Cells 6.9059 12.0244 -7.40410.000000 3.5381 10.5996
Eosinophils/ Cancer Cells 0.1640 0.3068 -5.18880.000000 0.1977 0.4106
Stick Neutrophils/ Cancer Cells 0.1609 0.2394 -2.76950.005752 0.2531 0.4097
Segmented Neutrophils/ Cancer Cells 4.6072 7.6439 -6.94330.000000 2.4058 6.6776
Lymphocytes/ Cancer Cells 1.6449 3.2188 -6.43440.000000 1.1776 3.7623
Monocytes/ Cancer Cells 0.3304 0.6129 -4.82820.000002 0.2580 0.9032
Healthy Cells/ Cancer Cells 15.6358 28.8233-10.06510.000000 6.7118 20.0884
Leucocytes (tot) 32.3707 30.2255 1.98100.047958 15.1142 13.3994
Segmented Neutrophils (tot) 21.7454 19.3681 3.07730.002164 11.0167 9.4285
Pneumonectomies/Lobectomies 0.5524 0.2200 9.67220.000000 0.4983 0.4147
Leucocytes (abs) 6.6464 6.0434 3.01160.002686 2.9214 2.4051
ResuIts of GLZ Analysis in Recognition of Lymph Node Metastases of Lung Cancer (n=757):
Effect
N0__N12 - Test of all effects (LCP, n=757)
Distribution : BINOMIAL, Link function: LOGIT
Modeled probability that N0---N12 = N0
Degr. of
Freedom
Wald
Stat.
p
Intercept 1 5.86265 0.015466
Erythrocytes 1 7.06920 0.007842
HB 1 4.45577 0.034783
Bilirubin 1 4.39263 0.036095
Prothrombin Index 1 6.52619 0.010630
Recalcification Time 1 5.60046 0.017956
Fibrinogen-B 1 5.49891 0.019028
Heparin Tolerance 1 10.06298 0.001513
Healthy Cells/Cancer Cells 1 58.80532 0.000000
Erythrocytes (tot) 1 8.14667 0.004314
Rh-factor 1 4.87828 0.027197
G1-3 2 6.47001 0.039360
Pneumonectomies/Lobectomies 4 28.15701 0.000012
Results of Neural Networks Computing in Recognition of Lymph Node Metastases in Lung Cancer Patients (n=757):
Neural Networks: n=757;
Baseline Error=0.000;
Area under ROC Curve=1.000;
Correct Classification Rate=100%
Rank Sensitivity
Healthy Cells/Cancer Cells 1 16675
Segmented Neutrophils/Cancer Cells 2 15999
Leucocytes/Cancer Cells 3 15094
Erythrocytes/Cancer Cells 4 14189
Lymphocytes/Cancer Cells 5 12506
Thrombocytes/Cancer Cells 6 10095
Eosinophils/Cancer Cells 7 7762
Monocytes/Cancer Cells 8 7028
Stick Neutrophils/Cancer Cells 9 6785
Results of Bootstrap Simulation in Recognition of Lymph Node Metastases in Lung Cancer Patients (n=757):
Significant Factors (Number of Samples=3333) Rank Kendal Tau-A P<
Healthy Cells/Cancer Cells 1 -0.324 0.000
Erythrocytes/Cancer Cells 2 -0.323 0.000
Thrombocytes/Cancer Cells 3 -0.307 0.000
Lymphocytes/Cancer Cells 4 -0.251 0.000
Leucocytes/Cancer Cells 5 -0.249 0.000
Segmented Neutrophils/Cancer Cells 6 -0.214 0.000
Tumor Size 7 0.214 0.000
T1-4 8 0.189 0.000
Eosinophils/Cancer Cells 9 -0.153 0.000
Pneumonectomies/Lobectomies 10 -0.151 0.000
Tumor Growth 11 -0.107 0.000
Adjuvant Treatment 12 0.097 0.000
Monocytes/Cancer Cells 13 -0.92 0.000
G1-3 14 0.081 0.01
Segmented Neutrophils (%) 15 0.073 0.01
Prothrombin Index 16 0.073 0.01
ESS 17 0.069 0.01
Lymphocytes (%) 18 -0.067 0.01
Histology 19 -0.066 0.01
Leucocytes 20 0.052 0.05
Results of Kohonen Self-Organizing Neural Networks Computing in Recognition of Lymph
Node Metastases of Lung Cancer Patients (n=757):
Equation Models in Recognition of Lymph Node Metastases of Lung Cancer Patients (n=757):
Equation Models in Recognition of Lymph Node Metastases of Lung Cancer Patients (n=757):
Equation Models in Recognition of Lymph Node Metastases of Lung Cancer Patients (n=757):
SEPATH-Model in Recognition of Lymph Node Metastases of Lung Cancer Patients (n=757):
LYMPH NODE METASTASES OF LUNG
CANCER SIGNIFICANTLY DEPENDED ON:
1) BLOOD CELL CIRCUIT;
2) CELL RATIO FACTORS;
3) BIOCHEMICAL FACTORS;
4) HEMOSTASIS SYSTEM;
5) CANCER CHARACTERISTICS.
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

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Kshivets iaslc singapore2020

  • 1. Lymph Node Metastases of Lung Cancer and Blood Cell Circuit Kshivets Oleg Roshal Hospital, Roshal, Moscow Russia Kshivets Oleg, Roshal Hospital, Russia
  • 3. Abstract Lymph Node Metastases of Lung Cancer and Blood Cell Circuit Kshivets Oleg Surgery Department, Roshal Hospital, Moscow, Russia OBJECTIVE: Significance of blood cell circuit in terms of detection of non-small cell lung cancer (LC) patients (LCP) with lymph node metastases was investigated. METHODS: We analyzed data of 757 consecutive LCP (age=57.6±8.2 years; tumor size=4.1±2.4 cm) radically operated (R0) and monitored in 1985-2020 (m=654, f=103; upper lobectomies=272, lower lobectomies=176, middle lobectomies=18, bilobectomies=42, pneumonectomies=249, mediastinal lymph node dissection=757; combined procedures with resection of trachea, carina, atrium, aorta, VCS, vena azygos, pericardium, liver, diaphragm, ribs, esophagus=192; T1=317, T2=251, T3=132, T4=57; N0=509, N1=130, N2=118, M0=757; G1=194, G2=238, G3=325; squamous=415, adenocarcinoma=292, large cell=50. 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 LCP with lymph node metastases (n=248) from LCP without metastases (n=509) significantly depended on: leucocytes (abs, total), segmented neutrophils (%, abs, total), lymphocytes (%), ESS, Rh, coagulation time, prothrombin index, fibrinogen, heparin tolerance, cell ratio factors (CRF) (ratio between cancer cells- CC and blood cells subpopulations), T, G, tumor size (P=0.047-0.000). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships of lymph node metastases and CRF: healthy cells/CC (rank=1), segmented neutrophils/CC (2), leucocytes/CC (3), erythrocytes/CC (4), lymphocytes/CC (5), thrombocytes/CC (6), eosinophils/CC (7), monocytes/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.
  • 5. Radical Procedures (R0): Upper Lobectomies……………….……………………………………...272 Lower Lobectomies.………................................................................176 Middle Lobectomies.……………………………………………………….18 Bilobectomies.……………………………………………………………....42 Pneumonectomies…………………………………………………..……249 Combined Procedures with Resection of Trachea, Carina, Atrium, Aorta, Vena Cava Superior, Vena Azygos, Pericardium, Liver, Diaphragm, Ribs, Esophagus…………………………………..…..192 Mediastinal Lymphadenectomy.……………………………..…...……757
  • 6. Staging: T1……..317 N0..……509 G1…………194 T2……..251 N1…......130 G2…………238 T3……..132 N2…......118 G3…………325 T4………57 N1-2…...248 M0…….…...757 Adenocarcinoma………………………………………………………..292 Squamous Cell Carcinoma……………………………………..……..415 Large Cell Carcinoma………………………………………..................50
  • 7. Results of Univariate Analysis of Phase Transition N0—N1-2 in Prediction of Lung Cancer Patients Survival (n=757): Cumulative Proportion Surviving (Kaplan-Meier) 5-Year Survival of LCP with N0=86.9%; 5-Year Survival of LCP with N1-2=43.5%; P=0.00000 by Log-Rank Test Complete Censored 0 5 10 15 20 25 30 35 Years after Lobectomies/Pneumonectomies 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 CumulativeProportionSurviving LCP with N1-2, n=248 LCP with N0, n=509
  • 8. Results of T-testing for Recognition of Lymph Node Metastases of Lung Cancer (n=757): Variable Mean N12 Mean N0 t-value p Std.Dev. N12 Std.Dev. N0 Rh-factor 1.0766 1.1415 -2.58260.009993 0.2665 0.3488 Tumor Grouth 1.4637 1.7073 -6.68630.000000 0.4997 0.4555 Histology 1.4556 1.6464 -3.09570.002036 0.7515 0.8161 G1-3 2.3589 2.0825 4.45900.000009 0.7610 0.8187 T1-4 2.3790 1.6758 10.29060.000000 0.9052 0.8711 Tumor Size 5.3391 3.5006 10.56150.000000 2.5529 2.0836 Segmented Neutrophils 67.4153 64.1454 3.81450.000148 10.6349 11.2749 Lymphocytes 23.2500 26.1218 -3.68940.000241 9.2763 10.4077 Segmented Neutrophils (abs) 4.4840 3.8665 4.26340.000023 2.1987 1.6878 ESS 20.7379 16.2397 3.94980.000086 16.5471 13.7226 Coagulation Time 245.9876224.4051 2.84550.004555 95.8202 98.9624 Prothrombin Index 97.0847 94.3124 3.54230.000421 10.3476 9.9867 Fibrinogen-B 1.2782 1.1768 2.42650.015478 0.6420 0.4821 Heparin Tolerance 208.6863192.1418 2.36650.018211107.6826 80.4693 Erythrocytes/Cancer Cells 4.5499 8.5338-10.02160.000000 2.1211 6.0810 Thrombocytes/Cancer Cells 254.9144457.7578 -9.30610.000000118.4503333.0530 Lecocytes/ Cancer Cells 6.9059 12.0244 -7.40410.000000 3.5381 10.5996 Eosinophils/ Cancer Cells 0.1640 0.3068 -5.18880.000000 0.1977 0.4106 Stick Neutrophils/ Cancer Cells 0.1609 0.2394 -2.76950.005752 0.2531 0.4097 Segmented Neutrophils/ Cancer Cells 4.6072 7.6439 -6.94330.000000 2.4058 6.6776 Lymphocytes/ Cancer Cells 1.6449 3.2188 -6.43440.000000 1.1776 3.7623 Monocytes/ Cancer Cells 0.3304 0.6129 -4.82820.000002 0.2580 0.9032 Healthy Cells/ Cancer Cells 15.6358 28.8233-10.06510.000000 6.7118 20.0884 Leucocytes (tot) 32.3707 30.2255 1.98100.047958 15.1142 13.3994 Segmented Neutrophils (tot) 21.7454 19.3681 3.07730.002164 11.0167 9.4285 Pneumonectomies/Lobectomies 0.5524 0.2200 9.67220.000000 0.4983 0.4147 Leucocytes (abs) 6.6464 6.0434 3.01160.002686 2.9214 2.4051
  • 9. ResuIts of GLZ Analysis in Recognition of Lymph Node Metastases of Lung Cancer (n=757): Effect N0__N12 - Test of all effects (LCP, n=757) Distribution : BINOMIAL, Link function: LOGIT Modeled probability that N0---N12 = N0 Degr. of Freedom Wald Stat. p Intercept 1 5.86265 0.015466 Erythrocytes 1 7.06920 0.007842 HB 1 4.45577 0.034783 Bilirubin 1 4.39263 0.036095 Prothrombin Index 1 6.52619 0.010630 Recalcification Time 1 5.60046 0.017956 Fibrinogen-B 1 5.49891 0.019028 Heparin Tolerance 1 10.06298 0.001513 Healthy Cells/Cancer Cells 1 58.80532 0.000000 Erythrocytes (tot) 1 8.14667 0.004314 Rh-factor 1 4.87828 0.027197 G1-3 2 6.47001 0.039360 Pneumonectomies/Lobectomies 4 28.15701 0.000012
  • 10. Results of Neural Networks Computing in Recognition of Lymph Node Metastases in Lung Cancer Patients (n=757): Neural Networks: n=757; Baseline Error=0.000; Area under ROC Curve=1.000; Correct Classification Rate=100% Rank Sensitivity Healthy Cells/Cancer Cells 1 16675 Segmented Neutrophils/Cancer Cells 2 15999 Leucocytes/Cancer Cells 3 15094 Erythrocytes/Cancer Cells 4 14189 Lymphocytes/Cancer Cells 5 12506 Thrombocytes/Cancer Cells 6 10095 Eosinophils/Cancer Cells 7 7762 Monocytes/Cancer Cells 8 7028 Stick Neutrophils/Cancer Cells 9 6785
  • 11. Results of Bootstrap Simulation in Recognition of Lymph Node Metastases in Lung Cancer Patients (n=757): Significant Factors (Number of Samples=3333) Rank Kendal Tau-A P< Healthy Cells/Cancer Cells 1 -0.324 0.000 Erythrocytes/Cancer Cells 2 -0.323 0.000 Thrombocytes/Cancer Cells 3 -0.307 0.000 Lymphocytes/Cancer Cells 4 -0.251 0.000 Leucocytes/Cancer Cells 5 -0.249 0.000 Segmented Neutrophils/Cancer Cells 6 -0.214 0.000 Tumor Size 7 0.214 0.000 T1-4 8 0.189 0.000 Eosinophils/Cancer Cells 9 -0.153 0.000 Pneumonectomies/Lobectomies 10 -0.151 0.000 Tumor Growth 11 -0.107 0.000 Adjuvant Treatment 12 0.097 0.000 Monocytes/Cancer Cells 13 -0.92 0.000 G1-3 14 0.081 0.01 Segmented Neutrophils (%) 15 0.073 0.01 Prothrombin Index 16 0.073 0.01 ESS 17 0.069 0.01 Lymphocytes (%) 18 -0.067 0.01 Histology 19 -0.066 0.01 Leucocytes 20 0.052 0.05
  • 12. Results of Kohonen Self-Organizing Neural Networks Computing in Recognition of Lymph Node Metastases of Lung Cancer Patients (n=757):
  • 13. Equation Models in Recognition of Lymph Node Metastases of Lung Cancer Patients (n=757):
  • 14. Equation Models in Recognition of Lymph Node Metastases of Lung Cancer Patients (n=757):
  • 15. Equation Models in Recognition of Lymph Node Metastases of Lung Cancer Patients (n=757):
  • 16. SEPATH-Model in Recognition of Lymph Node Metastases of Lung Cancer Patients (n=757):
  • 17. LYMPH NODE METASTASES OF LUNG CANCER SIGNIFICANTLY DEPENDED ON: 1) BLOOD CELL CIRCUIT; 2) CELL RATIO FACTORS; 3) BIOCHEMICAL FACTORS; 4) HEMOSTASIS SYSTEM; 5) CANCER CHARACTERISTICS. Conclusion:
  • 18. 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