Background: Significance of blood immune cell circuit for start of phase transition (PT) “norm---early lung cancer” (LC) was investigated. Material and methods: In trial (1987-2012) consecutive cases after radical surgery (R0, bi/lobectomies=48, N2-lymphadenectomies=48; squamous=21, adenocarcinoma=25, large cell=2; G1=16, G2=21, G3=11), monitored 48 early LC patients (LCP) (age=59±6.5 years, m=40, f=8; T1AN0M0=48, tumor size=1.7±0.3 cm, 5-year survival=100%) and 120 healthy donors (HD) (m=69, f=51) were reviewed. Variables selected for study were input levels of immunity blood parameters. The percentage, absolute count and total population number (per human organism) of T, B, CD4, CD8, CD16, CD1, CDw26, monocytes, CD4+2H, CD8+VV, leukocytes, lymphocytes, monocytes, eosinophils, stick and segmented neutrophils were estimated. The laboratory blood studies also included input levels of NST (tests of oxygen dependent metabolism of neutrophils spontaneous and stimulated by Staphylococcus aureus or by Streptococcus pyogenes), index of stimulation of leukocytes by Staphylococcus aureus or Streptococcus pyogenes, index of thymus function, phagocytic number, phagocyte index, index of complete phagocytosis. Differences between groups were evaluated using discriminant analysis, clustering, structural equation modeling, Monte Carlo, bootstrap simulation and neural networks computing. Results: It was revealed that start of PT “norm---early lung cancer” significantly depended on T-, B-, CD16-, CD8- cell circuit, neutrophils, monocyte circuit (P=0.000-0.027). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships of PT “norm---early lung cancer” and neutrophils (rank=1), CD16 (rank=2), monocytes (3), lymphocytes (4), CD4 (5), CD8 (6), B-cells (7), T-cells (8), eosinophils (9). Correct detection of start of PT “norm---early lung cancer” was 100% by neural networks computing (error=0.000; urea under ROC curve=1.0). Conclusions: Start of phase transition “norm---early lung cancer” significantly depended on blood immune cell circuit.

1. Background: Significance of blood
immune cell circuit for start of phase transition
(PT) “norm---early lung cancer” (LC) was
investigated.
Material and methods: In trial
(1987-2012) consecutive cases after radical
surgery (R0, bi/lobectomies=48, N2-
lymphadenectomies=48; squamous=21,
adenocarcinoma=25, large cell=2; G1=16,
G2=21, G3=11), monitored 48 early LC
patients (LCP) (age=59±6.5 years, m=40, f=8;
T1AN0M0=48, tumor size=1.7±0.3 cm, 5-year
survival=100%) and 120 healthy donors (HD)
(m=69, f=51) were reviewed. Variables
selected for study were input levels of
immunity blood parameters. The percentage,
absolute count and total population number
(per human organism) of T, B, CD4, CD8,
CD16, CD1, CDw26, monocytes, CD4+2H,
CD8+VV, leukocytes, lymphocytes,
monocytes, eosinophils, stick and segmented
neutrophils were estimated. The laboratory
blood studies also included input levels of NST
(tests of oxygen dependent metabolism of
neutrophils spontaneous and stimulated by
Staphylococcus aureus or by Streptococcus
pyogenes), index of stimulation of leukocytes
by Staphylococcus aureus or Streptococcus
pyogenes, index of thymus function,
phagocytic number, phagocyte index, index of
complete phagocytosis. Differences between
groups were evaluated using discriminant
analysis, clustering, structural equation
modeling, Monte Carlo, bootstrap simulation
and neural networks computing.
Results: It was revealed that start of PT
“norm---early lung cancer” significantly
depended on T-, B-, CD16-, CD8- cell circuit,
neutrophils, monocyte circuit (P=0.000-0.027).
Neural networks computing, genetic algorithm
selection and bootstrap simulation revealed
relationships of PT “norm---early lung cancer”
and neutrophils (rank=1), CD16 (rank=2),
monocytes (3), lymphocytes (4), CD4 (5), CD8
(6), B-cells (7), T-cells (8), eosinophils (9).
Correct detection of start of PT “norm---early
lung cancer” was 100% by neural networks
computing (error=0.000; area under ROC
curve=1.0).
START OF PHASE TRANSITION “NORM---EARLY LUNG CANCER” SIGNIFICANTLY DEPENDED ON
BLOOD IMMUNE CELL CIRCUIT
KSHIVETS OLEG SURGERY DEPARTMENT, KALUGA CANCER CENTER, KALUGA, RUSSIA
Conclusions: Start of phase transition
“norm---early lung cancer” significantly depended
on blood immune cell circuit.
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Significant Factors:
Norm, n=120---Early Lung Cancer, n=48
t-value p
T-lymphocytes (%) 2.27612 0.024116
CD4+2H+ (%) 2.51792 0.012752
CDw26+ (%) 3.51127 0.000574
CDw26+ (abs) 3.16388 0.001852
CD1+ (%) 3.19223 0.001689
CD1+ (abs) 2.10134 0.037121
CD8+ (abs) -2.02769 0.044191
B-lymphocytes (abs) -2.51135 0.012983
CD16+ (%) -3.17310 0.001797
CD16+ (abs) -3.36676 0.000945
Index Complete Phagocytosis 3.41913 0.000791
Phagocyte Number 2.29011 0.023273
Phagocyte Index 2.80538 0.005626
Leucocytes (abs) -3.36661 0.000946
Segmented Neutrophils (abs) -3.70224 0.000291
Monocytes (abs) -2.25531 0.025421
Segmented Neutrophils (tot) -4.12532 0.000058
Monocytes (tot) -2.80480 0.005636
B-lymphocytes (tot) -2.74665 0.006685
CD16+ (tot) -3.49627 0.000605
CDw26+ (tot) 3.18346 0.001738
CD8+ (tot) -2.28863 0.023362
Leucocytes (tot) -3.89109 0.000144
Segmented Neutrophils (%) -2.37483 0.018699
Lymphocytes (%) 2.39982 0.017510
Circulating Immune Complexes -3.45602 0.000696
Bootstrap Simulation
Significant Factors:
Norm=120; Early Lung Cancer=48;
Number of Samples=3333
Rank Kendall Tau-A P<
CD16+Cells 1 -0.174 0.001
Segmented Neutrophils 2 -0.159 0.01
Leucocytes 3 -0.142 0.01
CDw26+Cells 4 0.140 0.05
B-Cells 5 -0.102 0.05
Neural Networks Simulation Factors (Correct
Classification=100%; error=0.0: area under ROC curve=1.0):
Rank Sensitivity
Segmented Neutrophils 1 336.276
CD16+ Cells 2 319.895
Monocytes 3 221.115
Lymphocytes 4 188.612
CD4+Cells 5 183.271
CD8+Cells 6 173.591
B-Cells
T-Cells
Eosinophils
Kohonen Self-Organizing Neural Networks Computing
7
8
9
155.698
138.340
82.668