Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, Statistics and Simulation of Alive Supersystem for Optimal Management.

Lung Cancer: Artificial Intelligence,
Synergetics, Complex System
Analysis, Statistics and Simulation
of Alive Supersystems for Optimal
Management.
Kshivets Oleg, MD, PhD
Bagrationovsk Hospital, Kaliningrad, Russia
No Disclosures

OBJECTIVE: 5-survival (5YS) and life span after radical surgery for non-small cell lung cancer (LC) patients (LCP) (T1-4N0-2M0) – alive supersysems was analyzed. The importance
must be stressed of using complex system analysis, artificial intelligence (neural networks computing), simulation modeling and statistical methods in combination, because the
different approaches yield complementary pieces of prognostic information.
METHODS: We analyzed data of 782 consecutive LCP (age=57.6±8.3 years; tumor size=4.1±2.4 cm) radically operated and monitored in 1985-2024 (m=670, f=112; upper
lobectomies=282, lower lobectomies=179, middle lobectomies=18, bilobectomies=46, pneumonectomies=257, mediastinal lymph node dissection=782; combined procedures with
resection of trachea, carina, atrium, aorta, VCS, vena azygos, pericardium, liver, diaphragm, ribs, esophagus=198; only surgery-S=626, adjuvant chemoimmunoradiotherapy-AT=156:
CAV/gemzar + cisplatin + thymalin/taktivin + radiotherapy 45-50Gy; T1=326, T2=258, T3=137, T4=61; N0=525, N1=133, N2=124, M0=782; G1=199, G2=248, G3=335; squamous=422,
adenocarcinoma=310, large cell=50; early LC=218, invasive LC=564; right LC=420, left LC=362; central=294; peripheral=488. Variables selected for study were input levels of 45 blood
parameters, sex, age, TNMG, cell type, tumor size. Regression modeling, clustering, SEPATH, Monte Carlo, bootstrap and neural networks computing were used to determine
significant dependence.
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years
(LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was
significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by
log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12,
cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification
time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12
(rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9),
monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP 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) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and
treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en
block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Abstract

Data:
Males…………………………………….………………………………..…………........670
Females……...........................................................................................................112
Age=57.6±8.3 years; Tumor Size=4.1±2.4 cm
Upper Lobectomies……………………….……………………………..……….........282
Lower Lobectomies.………...................................................................................179
Middle Lobectomies.……………………….…….………………………………..…….18
Bilobectomies.……….…………………………….……………………………….........46
Pneumonectomies…………………………..………………..………………..……….257
Combined Procedures with Resection of Trachea, Carina, Atrium, Aorta,
Vena Cava Superior, Vena Azygos, Pericardium, Liver, Diaphragm, Ribs,
Esophagus……………………………………………………………………………….198
Mediastinal Lymphadenectomy.……….………..…...………………………………782

T1……..326 N0..……525 G1…………195
T2……..258 N1…......133 G2…………243
T3……..137 N2…......124 G3…………333
T4………61 N1-2…...257 M0…….…...782
Adenocarcinoma………………………………………………………..310
Squamous Cell Carcinoma……………………………………..……..422
Large Cell Carcinoma………………………………………..................50
Early LC…………………………………………………………………..218
Invasive LC………………………………………………………………564
Staging:

Alive………………………………………...................527(67.4%)
5-Year Survivors…………..………………………....513 (65.6%)
10-Year Survivors………………………...………….148 (18.9%)
Losses……………………………………………..…..199 (25.4%)
General Life Span=2521.1±1742.5 days
For 5-Year Survivors=3124.6±1525.6 days
For 10-Year Survivors=5054.4±1504.1 days
For Losses=562.7±374.5 days
Cumulative 5-Year Survival……………………………….73.2%
Cumulative 10-Year Survival………………….................64.8%
Cumulative 20-Year Survival………………….................42.5%
Survival Rate:

General Lung Cancer Patients Survival after
Complete Lobectomies/Pneumonectomies (Kaplan-Meier) (n=782):
Survival Function
5YS=73.2%; 10YS=64.8%; 20YS=42.5%
Complete Censored
-5 0 5 10 15 20 25 30
Years after Surgery
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
Cumulative
Proportion
Surviving

Cumulative Proportion Surviving (Kaplan-Meier)
p=0.0000
Complete Censored
0 5 10 15 20 25 30 35
Years after Surgery
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Cumulative
Proportion
Surviving
Invasive LCP
Early LCP
Results of Univariate Analysis of Phase
Transition Early—Invasive Cancer in Prediction
of Lung Cancer Patients Survival (n=782):

Results of Univariate Analysis of Phase
Transition N0—N12 in Prediction of Lung Cancer Patients Survival
(n=782):
p=0.0000
Complete Censored
0 5 10 15 20 25 30 35
Years afer Surgery
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Cumulative
Proportion
Surviving
N12
N0

Results of Univariate Analysis of Adjuvant Treatment (Adjuvant
Chemoimmunotherapy vs Surgery along) in Prediction of Lung Cancer
Patients Survival with N1-2 (n=257):
P=0.0000
Complete Censored
0 5 10 15 20 25 30
Time
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Cumulative
Proportion
Surviving
only Surgery
Adjuvant Chemoimmunoradiotherapy

Results of Univariate Analysis of Gender (Males vs. Females) in Prediction
of Lung Cancer Patients Survival (n=782):
p=0.018
Complete Censored
0 5 10 15 20 25 30 35
Years after Surgery
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Cumulative
Proportion
Surviving
Male
Female

Cox Regression, n=782
Parameter
Estimate
Standard
Error
Chi-
square
P value
95%
Lower CL
95%
Upper CL
Hazard
Ratio
G1-3 0.35321 0.086772 16.56945 0.000047 0.18314 0.52328 1.423630
Histology 0.34441 0.084904 16.45430 0.000050 0.17800 0.51081 1.411151
Lymphocytes (abs) 0.65707 0.316865 4.30006 0.038111 0.03603 1.27811 1.929131
Glucose -0.29099 0.077132 14.23237 0.000162 -0.44216 -0.13981 0.747525
Prothrombin Index 0.02645 0.006705 15.56527 0.000080 0.01331 0.03959 1.026806
Recalcification Time -0.00475 0.001691 7.89697 0.004952 -0.00806 -0.00144 0.995260
Heparin Tolerance 0.00353 0.000662 28.52148 0.000000 0.00224 0.00483 1.003541
Phase Transition Early-Invasive Lung Cancer -1.24631 0.322482 14.93623 0.000111 -1.87836 -0.61426 0.287564
Phase Transition N0__N12 1.09716 0.148598 54.51439 0.000000 0.80591 1.38840 2.995632
Thrombocytes/Cancer Cells -0.00239 0.000652 13.43305 0.000247 -0.00367 -0.00111 0.997612
Lymphocytes/Cancer Cells 0.19931 0.061719 10.42876 0.001241 0.07835 0.32028 1.220563
Adjuvant Chemoimmunoradiotherapy -1.78893 0.347252 26.53966 0.000000 -2.46953 -1.10833 0.167139
Treatment 0.36250 0.168222 4.64354 0.031171 0.03279 0.69221 1.436916
Thrombocytes (tot) 0.00119 0.000235 25.58519 0.000000 0.00073 0.00165 1.001191
Eosinophils (tot) -0.23734 0.098707 5.78138 0.016197 -0.43080 -0.04387 0.788726
Lymphocytes (tot) -0.24714 0.072780 11.53075 0.000685 -0.38978 -0.10449 0.781034
Cox Regression in Prediction of Lung Cancer
Patients Survival (n=782):

Results of Neural Networks and Monte Carlo Computing in Prediction of Lung Cancer
Patients Survival after Complete Lobectomies/Pneumonectomies (n=713):

Results of Bootstrap Simulation in Prediction of
Lung Cancer Patients Survival after Complete
Lobectomies/Pneumonectomies(n=713):

Results of Kohonen Self-Organizing Neural
Networks Computing in Prediction of Lung
Cancer Patients Survival (n=713):

SEPATH Modeling in Prediction of Lung
Cancer Patients Survival after Complete
Lobectomies/Pneumonectomies (n=713):

Prognostic Equation Models of Lung Cancer
Patients Survival after Surgery (n=713):

Conclusion:
5-YEAR SUVIVAL OF NON-SMALL CELL LUNG
CANCER PATIENTS AFTER RADICAL
PROCEDURES (R0) SIGNIFICANTLY DEPENDED
ON:
1) PHASE TRANSITION EARLY-INVASIVE LUNG
CANCER;
2) PHASE TRANSITION N0---N12;
3) CELL RATIO FACTORS;
4) BLOOD CELL CIRCUIT;
5) BIOCHEMICAL FACTORS;
6) HEMOSTASIS SYSTEM;
7) ADJUVANT CHEMOIMMUNORADIOTHERAPY;
8) LUNG CANCER CHARACTERISTICS;
9) LUNG CANCER CELL DYNAMICS;
10) SURGERY TYPE
(LOBECTOMY/PNEUMONECTOMY);
11) ANTHROPOMETRIC DATA.

Conclusion:
BEST MANAGEMENT FOR LCP IS:
1) SCREENING AND EARLY DETECTION OF
LC;
2) THE PRESENCE OF A SUFFICIENT
NUMBER OF OF EXPERIENCED THORACIC
SURGEONS BECAUSE OF COMPLEXITY OF
RADICAL PROCEDURES ESPECIALLY WITH
LOCALLY ADVANCED LC;
3) AGGRESSIVE EN BLOCK SURGERY AND
ADEQUATE LYMPH NODE DISSECTION FOR
COMPLETENESS;
4) PRECISE PREDICTION;
5) ADJUVANT
CHEMOIMMUNORADIOTHERAPY FOR LCP
WITH UNFAVORABLE PROGNOSIS.

e-mail: okshivets@yahoo.com
skype: olegks001
Address:
Oleg Kshivets, M.D., Ph.D.
Consultant Thoracic,
Abdominal, General Surgeon &
Surgical Oncologist

Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, Statistics and Simulation of Alive Supersystem for Optimal Management.

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