Lymph node metastases of gastric cancer and blood cell circuit.

e16024 Background: Significance of blood cell circuit in terms of detection of gastric cancer (GC) patients (GCP) with lymph node metastases was investigated. Methods: We analyzed data of 793 consecutive GCP (age = 57±9.4 years; tumor size = 5.4±3.1 cm) radically operated (R0) and monitored in 1975-2021 (m = 555, f = 238; distal gastrectomies = 460, proximal gastrectomies = 163, total gastrectomies = 170, combined gastrectomies with resection of pancreas, liver, diaphragm, colon transversum, esophagus, duodenum, splenectomy, small intestine, kidney, adrenal gland = 244; D2-lymphadenectomy = 513, D3-4 = 280; T1 = 235, T2 = 220, T3 = 182, T4 = 156; N0 = 433, N1 = 109, N2 = 251; G1 = 222, G2 = 162, G3 = 409; early GC = 162, invasive = 631; only surgery = 621, adjuvant chemoimmunotherapy-AT = 172 (5-FU+thymalin/taktivin). 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 GCP with lymph node metastases (n = 360) from GCP without metastases (n = 433) significantly depended on: eosinophils (%, abs, total), thrombocytes (abs, total), ESS, Hb, erythrocytes (abs), residual nitrogen, protein, cell ratio factors (CRF) (ratio between cancer cells- CC and blood cells subpopulations), T, G, tumor size, histology, tumor growth, blood group, procedures type (P = 0.043-0.000). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships of lymph node metastases and CRF: thrombocytes/CC (rank = 1), healthy cells/CC (2), erythrocytes/CC (3), monocytes/CC (4), segmented neutrophils/CC (5), lymphocytes/CC (6), leucocytes/CC (7), eosinophils/CC (8), stick neutrophils/CC (9). Correct classification N0—N12 was 99.9% by neural networks computing (area under ROC curve = 1.0; error = 0.0). Conclusions: Lymph node metastases of gastric cancer significantly depended on blood cell circuit.

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Precise early detection of gastric cancer and blood cell circuit.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e16512 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e16512-e16512

Author(s):

Oleg Kshivets

Keyword(s):

Gastric Cancer ◽

Neural Networks ◽

Early Detection ◽

Blood Cell ◽

Structural Equation ◽

Equation Modeling ◽

Algorithm Selection ◽

Bootstrap Simulation ◽

Area Under Roc Curve ◽

Cell Circuit

e16512 Background: 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). Conclusions: Early detection of GC from HD significantly depended on blood cell circuit.

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Gastric Cancer: 10-Year Survival after Surgery

World Journal of Advanced Research and Reviews ◽

10.30574/wjarr.2021.12.3.0659 ◽

2021 ◽

Vol 12 (3) ◽

pp. 020-032

Author(s):

Kshivets Oleg

Keyword(s):

Neural Networks ◽

Lymph Node ◽

Blood Cell ◽

Lymph Node Dissection ◽

Tumor Size ◽

Rank Test ◽

Node Dissection ◽

Cell Ratio ◽

Log Rank Test ◽

Cell Circuit

Methods: We analyzed data of 796 consecutive GCP (age=57.1±9.4 years; tumor size=5.4±3.1 cm) radically operated (R0) and monitored in 1975-2021 (m=556, f=240; distal gastrectomies-G=461, proximal G=165, total G=170, D2 lymph node dissection=551; combined G with resection of 1-7 adjacent organs (pancreas, liver, diaphragm, esophagus, colon transversum, splenectomy, small intestine, kidney, adrenal gland, etc.)=245; D3-4 lymph node dissection=245; only surgery-S=623, adjuvant chemoimmunotherapy-AT=173: 5FU+thymalin/taktivin; T1=237, T2=220, T3=182, T4=157; N0=435, N1=109, N2=252, M0=796; G1=222, G2=164, G3=410; early GC=164, invasive GC=632; Variables selected for 10YS study were input levels of 45 blood parameters, sex, age, TNMG, cell type, tumor size. Survival curves were estimated by the Kaplan-Meier method. Differences in curves between groups of GCP were evaluated using a log-rank test. Multivariate Cox modeling, discriminant analysis, clustering, SEPATH, Monte Carlo, bootstrap and neural networks computing were used to determine any significant dependence. Results: Overall life span (LS) was 2130.8±2304.3 days and cumulative 5-year survival (5YS) reached 58.4%, 10 years – 52.4%, 20 years – 40.4%. 316 GCP lived more than 5 years (LS=4316.1±2292.9 days), 169 GCP – more than 10 years (LS=5919.5±2020 days). 294 GCP died because of GC (LS=640.6±347.1 days). AT significantly improved 10YS (62.3% vs. 50.5%) (P=0.0228 by log-rank test) for GCP. Cox modeling displayed that 10YS of LCP significantly depended on: phase transition (PT) early-invasive GC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, AT, blood cell circuit, prothrombin index, hemorrhage time, residual nitrogen, age, sex, procedure type (P=0.000-0.039). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 10YS and healthy cells/CC (rank=1), PT early-invasive GC (rank=2), PT N0—N12(rank=3), erythrocytes/CC (4), thrombocytes/CC (5), monocytes/CC (6), segmented neutrophils/CC (7), eosinophils/CC (8), leucocytes/CC (9), lymphocytes/CC (10), stick neutrophils/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0). Conclusions: 10-Year survival of GCP 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) GC characteristics; 9) anthropometric data; 10) surgery type. Optimal diagnosis and treatment strategies for GC are: 1) screening and early detection of GC; 2) availability of experienced abdominal surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunotherapy for GCP with unfavorable prognosis.

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Early gastric cancer - excellent prognosis after curative resection in 87 patients irrespective of submucosal infiltration, lymph-node metastases or tumor size

Langenbeck s Archives of Surgery ◽

10.1007/s004230000180 ◽

2001 ◽

Vol 386 (1) ◽

pp. 26-30 ◽

Cited By ~ 5

Author(s):

Piso P. ◽

Werner U. ◽

Benten D. ◽

Bektas H. ◽

Meuer U. ◽

...

Keyword(s):

Gastric Cancer ◽

Lymph Node ◽

Early Gastric Cancer ◽

Lymph Node Metastases ◽

Tumor Size ◽

Curative Resection ◽

Node Metastases ◽

Submucosal Infiltration

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Artificial intelligence, complex system analysis and simulation modeling in recognition of N1–2 lymph node metastases of non-small cell lung cancer

Journal of Clinical Oncology ◽

10.1200/jco.2006.24.18_suppl.17123 ◽

2006 ◽

Vol 24 (18_suppl) ◽

pp. 17123-17123

Author(s):

O. Kshivets

Keyword(s):

Lung Cancer ◽

Neural Networks ◽

Logistic Regression ◽

Lymph Node ◽

Discriminant Analysis ◽

Lymph Node Metastases ◽

Small Cell ◽

Small Cell Lung ◽

Bootstrap Simulation ◽

Node Metastases

17123 Background: Precise recognition of N1–2 lymph node metastases (RLN) of non-small cell lung cancer (LC) means great importance in prediction LC patients (LCP) survival after surgery. We examined the immunologic factors associated with LCP with N0 and N1–2. Methods: In trial (1987–2005) the data of consecutive 289 LCP after complete pneumonectomies/lobectomies and mediastinal lymph node dissection (age = 58.1 ± 0.5 years; tumor size = 4.4 ± 0.1 cm; m = 260, f = 29) with pathologic stage I-III (T1–4N0–2M0) (squamous = 169, adenocarcinoma = 102, large cell = 18; G1 = 67, G2 = 110, G3 = 112; T1 = 100, T2 = 114, T3 = 54, T4 = 21; N0 = 147, N1 = 70, N2 = 72; pneumonectomies = 135, bi/lobectomies = 154) was reviewed. Variables selected for ED study were input levels of 64 immunity blood parameters, sex, age. Representativeness of samplings was reached by means of randomisation based on unrepeated random selection. Logistic regression, clustering, discriminant analysis, neural networks computing, structural equation modeling, Monte Carlo and bootstrap simulation were used to determine any significant regularity. Results: Logistic regression modeling displayed that RLN of LC significantly depended on: CDw26, CD16, phagocytic number, ratio of lymphocytes, T-lymphocytes, CD4+2H to LC cells (P = 0.003–0.043). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships between N1–2 and blood IgM (rank = 1), eosinophils (2), T-lymphocytes (3), CD8 (4), phagocytic number (5), CD8+VV (6), CDw26 (7), B-lymphocytes (8), IS2 (9), CD4 (10), CD4+2H (11), NST-A2 (12), monocytes (13), index of thymus function (14), IgA (15). Conclusions: Correct RLN of LC was 79.2% by logistic regression (odds ratio = 15.65), 82.0% by discriminant analysis and 99.6% by neural networks computing (area under ROC curve = 0.99; error = 0.059). No significant financial relationships to disclose.

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P63.01 Lymph Node Metastases of Lung Cancer and Blood Cell Circuit

Journal of Thoracic Oncology ◽

10.1016/j.jtho.2021.01.987 ◽

2021 ◽

Vol 16 (3) ◽

pp. S552

Author(s):

O. Kshivets

Keyword(s):

Lung Cancer ◽

Lymph Node ◽

Blood Cell ◽

Lymph Node Metastases ◽

Node Metastases ◽

Cell Circuit

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Prediction of Lymph Node Metastases in Gastric Cancer by Serum APE1 Expression

Journal of Cancer ◽

10.7150/jca.18615 ◽

2017 ◽

Vol 8 (8) ◽

pp. 1492-1497 ◽

Cited By ~ 12

Author(s):

Xi Wei ◽

Yi-bo Li ◽

Ying Li ◽

Ben-cheng Lin ◽

Xiao-Min Shen ◽

...

Keyword(s):

Gastric Cancer ◽

Lymph Node ◽

Lymph Node Metastases ◽

Node Metastases

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18F-FDG PET/CT Radiomics for Preoperative Prediction of Lymph Node Metastases and Nodal Staging in Gastric Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.723345 ◽

2021 ◽

Vol 11 ◽

Author(s):

Qiufang Liu ◽

Jiaru Li ◽

Bowen Xin ◽

Yuyun Sun ◽

Dagan Feng ◽

...

Keyword(s):

Gastric Cancer ◽

Lymph Node ◽

Lymph Node Metastases ◽

Fdg Pet ◽

Fixed Ratio ◽

Preoperative Prediction ◽

Pet Ct ◽

N Stage ◽

Node Metastases ◽

Fdg Pet Ct

ObjectivesThe accurate assessment of lymph node metastases (LNMs) and the preoperative nodal (N) stage are critical for the precise treatment of patients with gastric cancer (GC). The diagnostic performance, however, of current imaging procedures used for this assessment is sub-optimal. Our aim was to investigate the value of preoperative 18F-FDG PET/CT radiomic features to predict LNMs and the N stage.MethodsWe retrospectively collected clinical and 18F-FDG PET/CT imaging data of 185 patients with GC who underwent total or partial radical gastrectomy. Patients were allocated to training and validation sets using the stratified method at a fixed ratio (8:2). There were 2,100 radiomic features extracted from the 18F-FDG PET/CT scans. After selecting radiomic features by the random forest, relevancy-based, and sequential forward selection methods, the BalancedBagging ensemble classifier was established for the preoperative prediction of LNMs, and the OneVsRest classifier for the N stage. The performance of the models was primarily evaluated by the AUC and accuracy, and validated by the independent validation methods. Analysis of the feature importance and the correlation were also conducted. We also compared the predictive performance of our radiomic models to that with the contrast-enhanced CT (CECT) and 18F-FDG PET/CT.ResultsThere were 185 patients—127 men, 58 women, with the median age of 62, and an age range of 22–86 years. One CT feature and one PET feature were selected to predict LNMs and achieved the best performance (AUC: 82.2%, accuracy: 85.2%). This radiomic model also detected some LNMs that were missed in CECT (19.6%) and 18F-FDG PET/CT (35.7%). For predicting the N stage, four CT features and one PET feature were selected (AUC: 73.7%, accuracy: 62.3%). Of note, a proportion of patients in the validation set whose LNMs were incorrectly staged by CECT (57.4%) and 18F-FDG PET/CT (55%) were diagnosed correctly by our radiomic model.ConclusionWe developed and validated two machine learning models based on the preoperative 18F-FDG PET/CT images that have a predictive value for LNMs and the N stage in GC. These predictive models show a promise to offer a potentially useful adjunct to current staging approaches for patients with GC.

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