Precise Early Detection of Gastric Cancer and Blood Cell Circuit

1. Precise Early Detection of Gastric
Cancer and Blood Cell Circuit
Oleg Kshivets, MD, PhD Surgery Department, Roshal Hospital, Roshal, Moscow, Russia
Objective: Significance of blood cell circuit in terms of early detection of gastric cancer
(GC) was investigated.
Methods: In trial (1975-2020) consecutive cases after surgery, monitored 136 GC
patients (GCP) (m=90, f=46; distal gastrectomies=95, proximal gastrectomies=34, total
gastrectomies=7) with pathologic stage IA (tumor size=1.81±0.70 cm;
adenocarcinoma=136; T1N0M0=136; G1=67, G2=26, G3=43, 5-year survival=100%) and 120
healthy donors (HD) (m=69, f=51) were reviewed. 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 early detection of GC from HD (n=256) significantly
depended on: leucocytes (abs, total), segmented neutrophils (%, abs, total), lymphocytes
(%), monocytes (%, abs, total), stick neutrophils (%, abs, total), eosinophils (%, abs, total)
(P=0.007-0.000). Neural networks computing, genetic algorithm selection and bootstrap
simulation revealed relationships of early detection of GC and lymphocytes (rank=1),
segmented neutrophils (rank=2), monocytes (3), stick neutrophils (4), leucocytes (5),
eosinophils (6). Correct detection of early GCP was 100% by neural networks computing
(error=0.000; area under ROC curve=1.0).
Conclusion: Early detection of GC from HD significantly depended on blood cell circuit.
e16512
Neural Networks: n=256;
Baseline Error=0.000;
Area under ROC Curve=1.000;
Correct Classification Rate=100%
Rank Sensitivity
Lymphocytes 1 3581
Segmented Neutrophils 2 1475
Monocytes 3 817
Stick Neutrophils 4 737
Leucocytes 5 703
Eosinophils 6 685
Blood
Factors
Mean
norm
Mean
Cancer
t-value
p Std.Dev.
norm
Std.Dev.
Cancer
Leucocytes 4.96500 5.86618-4.044770.000070 1.58240 1.93568
Stick Neutrophils 1.05833 1.61765-2.731940.006738 1.32396 1.86611
Segmented Neutrophils 55.8583364.64706-6.330510.00000010.6899011.42157
Eosinophils 3.22500 1.86029 3.751090.000218 3.66556 2.00803
Monocytes 3.11667 4.33824-3.983380.000089 2.38406 2.50398
Lymphocytes 36.8250027.50000 7.185430.00000010.3271410.39230
Eosinophils abs 0.16319 0.09863 2.893100.004146 0.23783 0.09930
Stick Neutrophils abs 0.05908 0.09768-2.909910.003936 0.08058 0.12406
Segmented Neutrophils abs 2.81104 3.81033-5.981630.000000 1.17819 1.45738
Lymphocytes abs 1.78054 1.60163 1.859940.064049 0.68395 0.83517
Monocytes abs 0.15323 0.25512-5.309020.000000 0.13311 0.16899
Eosinophils tot 0.78646 0.47996 2.778310.005872 1.17369 0.49549
Stick Neutrophils tot 0.28479 0.46715-2.877550.004349 0.40818 0.57865
Segmented Neutrophils tot 13.4859118.59339-5.443050.000000 6.00216 8.59387
Lymphocytes tot 8.52408 7.64274 1.836880.067397 3.38285 4.18631
Monocytes tot 0.73147 1.23458-5.145050.000001 0.65846 0.87446
Leucocytes tot 23.8068528.43292-3.638250.000332 8.3036611.53861
N=256
Discriminant Function Analysis Summary (ED_N_GC) No. of vars in
model: 9; Grouping: NORM_CA (2 grps) Wilks' Lambda: .70485
approx. F (9,246)=11.446 p< .0000
Wilks'
Lambda
Partial
Lambda
F-remove
(1,246)
p-value Toler.
1-Toler.
(R-Sqr.)
Segmented Neutrophils 0.735002 0.958976 10.52371 0.001342 0.002534 0.997466
Eosinophils 0.728629 0.967364 8.29935 0.004316 0.027613 0.972387
Monocytes 0.719726 0.979330 5.19228 0.023545 0.031399 0.968601
Lymphocytes 0.744286 0.947015 13.76371 0.000256 0.002902 0.997098
Eosinophils tot 0.716097 0.984293 3.92569 0.048667 0.026257 0.973743
Segmented Neutrophils
tot
0.727793 0.968475 8.00762 0.005043 0.000483 0.999517
Lymphocytes tot 0.730496 0.964891 8.95108 0.003055 0.001886 0.998114
Monocytes tot 0.724065 0.973461 6.70662 0.010178 0.026071 0.973929
Leucocytes tot 0.728309 0.967789 8.18765 0.004581 0.000272 0.999728
Effect
NORM_CA - Test of all effects
(ED_N_GC) Distribution :
BINOMIAL, Link function: LOGIT
Modeled probability that
NORM_CA = norm
Degr. of
Freedo
m
Wald
Stat.
p
Intercept 1 9.41076 0.002157
Leucocytes 1 4.34699 0.037074
Segmented Neutrophils 1 8.40957 0.003733
Eosinophils 1 11.43033 0.000723
Monocytes 1 4.60794 0.031824
Lymphocytes 1 11.77407 0.000601
Segmented Neutrophils abs 1 3.91261 0.047925
Segmented Neutrophils tot 1 11.23654 0.000802
Lymphocytes tot 1 8.03578 0.004586
Monocytes tot 1 6.50135 0.010779
Leucocytes tot 1 10.88927 0.000967