Diagnostic significance of plasma lipid markers and machine learning‑based algorithm for gastric cancer

AbstractTo explore the predictive performance of machine learning on the recurrence of patients with gastric cancer after the operation. The available data is divided into two parts. In particular, the first part is used as a training set (such as 80% of the original data), and the second part is used as a test set (the remaining 20% of the data). And we use fivefold cross-validation. The weight of recurrence factors shows the top four factors are BMI, Operation time, WGT and age in order. In training group:among the 5 machine learning models, the accuracy of gbm was 0.891, followed by gbm algorithm was 0.876; The AUC values of the five machine learning algorithms are from high to low as forest (0.962), gbm (0.922), GradientBoosting (0.898), DecisionTree (0.790) and Logistic (0.748). And the precision of the forest is the highest 0.957, followed by the GradientBoosting algorithm (0.878). At the same time, in the test group is as follows: the highest accuracy of Logistic was 0.801, followed by forest algorithm and gbm; the AUC values of the five algorithms are forest (0.795), GradientBoosting (0.774), DecisionTree (0.773), Logistic (0.771) and gbm (0.771), from high to low. Among the five machine learning algorithms, the highest precision rate of Logistic is 1.000, followed by the gbm (0.487). Machine learning can predict the recurrence of gastric cancer patients after an operation. Besides, the first four factors affecting postoperative recurrence of gastric cancer were BMI, Operation time, WGT and age.

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Two Staged Prediction of Gastric Cancer Patient’s Survival Via Machine Learning Techniques

7th International Conference on Artificial Intelligence and Applications ◽

10.5121/csit.2020.100308 ◽

2020 ◽

Author(s):

Peng Liu ◽

Liuwen Li ◽

Chen Yu ◽

Shumin Fei

Keyword(s):

Machine Learning ◽

Gastric Cancer ◽

Machine Learning Techniques ◽

Learning Techniques

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DIFFERENTIAL DIAGNOSTICS OF GASTRIC CANCER AND PRECANCEROUS CHANGES OF THE GASTRIC MUCOSA USING ANALYSIS OF EXPRESSION OF SIX MICRORNAS

Russian Clinical Laboratory Diagnostics ◽

10.18821/0869-2084-2020-65-2-131-136 ◽

2020 ◽

Vol 65 (2) ◽

pp. 131-136 ◽

Cited By ~ 1

Author(s):

S. E. Titov ◽

V. V. Anishchenko ◽

T. L. Poloz ◽

Yu. A. Veryaskina ◽

A. A. Arkhipova ◽

...

Keyword(s):

Gastric Cancer ◽

Gastric Mucosa ◽

Decision Tree ◽

Diagnostic Procedures ◽

Normal Mucosa ◽

Gastric Ulcers ◽

Differential Diagnostics ◽

Diagnostic Significance ◽

Diagnostic Characteristics ◽

Specific Symptoms

The lack of specific symptoms for the early detection of gastric cancer leads to the fact that it is often diagnosed at a late stage, when the prognosis is unfavorable. The analysis of molecular markers in addition to standard diagnostic procedures is a promising approach for improving the preoperative diagnosis of both gastric cancer and precancerous changes in the mucosa. Therefore, the aim of our study was to analyze the diagnostic significance of using miRNA expression to diagnosis gastric cancer and precancerous conditions (dysplasia) in histological material. In this work, 122 samples of archival histological material in the form of paraffin blocks were used: 34 samples of gastric adenocarcinoma, 54 samples of gastric ulcers with dysplasia and 34 samples of normal gastric mucosa obtained from patients after bariatric surgery. The expression level of miRNA-145-5p, -150-5p, -20a-5p, -21-5p, -31-5p, -34a-5p, -375 was determined using real-time RT-PCR. Samples were stratified into different groups using the C-RT decision tree algorithm. All miRNAs, except miRNA-20a, were included in the decision tree, which allows stratification of samples for normal mucosa, dysplasia, and gastric cancer. Normal mucosa can be distinguished from gastric cancer only by miRNA-34a, -21, -375. Diagnostic characteristics for the detection of dysplasia: specificity - 97%, sensitivity - 87%; for the detection of gastric cancer: specificity - 91%, sensitivity - 93%. The sufficiently high values of the diagnostic characteristics for detecting dysplasia of the gastric mucosa and gastric cancer obtained in our study indicate the possibility of using expression data of a small amount of miRNAs for the effective separation of samples with tumor and precancerous changes in the stomach tissue.

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Detection of sarcopenic obesity and prediction of long‐term survival in patients with gastric cancer using preoperative computed tomography and machine learning

Journal of Surgical Oncology ◽

10.1002/jso.26668 ◽

2021 ◽

Author(s):

Jaehyuk Kim ◽

Seung Hee Han ◽

Hyoung‐Il Kim

Keyword(s):

Machine Learning ◽

Gastric Cancer ◽

Computed Tomography ◽

Sarcopenic Obesity ◽

Term Survival ◽

Long Term Survival

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Evaluation of CSTB and DMBT1 Expression in Saliva of Gastric Cancer Patients and Controls

10.21203/rs.3.rs-847012/v1 ◽

2021 ◽

Author(s):

Maryam Koopaie ◽

Marjan Ghafourian ◽

Soheila Manifar ◽

Shima Younespour ◽

Mansour Davoudi ◽

...

Keyword(s):

Machine Learning ◽

Gastric Cancer ◽

Statistical Analysis ◽

Food Intake ◽

Roc Curve ◽

Clinical Characteristics ◽

Demographic Data ◽

Healthy Individuals ◽

Diagnostic Algorithms ◽

The Mean

Abstract Background: Gastric cancer (GC) is the fifth most common cancer and the third cause of cancer deaths globally with late diagnosis, low survival rate and poor prognosis. This case-control study aimed to evaluate the expression of cystatin B (CSTB) and deleted in malignant brain tumor 1 (DMBT1) in the saliva of GC patients with healthy individuals to construct diagnostic algorithms using statistical analysis and machine learning methods.Methods: Demographic data, clinical characteristics and food intake habits of the case and control group were gathered through a standard checklist. Unstimulated whole saliva samples were taken from 31 healthy individuals and 31 GC patients. Through ELISA test and statistical analysis, the expression of salivary CSTB and DMBT1 proteins were evaluated. To construct diagnostic algorithms, we used the machine learning method.Results: The mean salivary expression of CSTB in GC patients was significantly lower (115.55±7.06, p=0.001) and the mean salivary expression of DMBT1 in GC patients was significantly higher (171.88±39.67, p=0.002) than the control. Multiple linear regression analysis demonstrated that GC was significantly correlated with high levels of DMBT1 after controlling the effects of age of participants (R2=0.20, p<0.001). Considering salivary CSTB greater than 119.06 ng/mL as an optimal cut-off value, the sensitivity and specificity of CSTB in the diagnosis of GC was 83.87% and 70.97%, respectively The area under the ROC curve was calculated as 0.728. The optimal cut-off value of DMBT1 for differentiating GC patients from controls was greater than 146.33 ng/mL (sensitivity=80.65% and specificity=64.52%). The area under the ROC curve was up to 0.741. As a result of the machine learning method, the area under the receiver-operating characteristic curve for the diagnostic ability of CSTB, DMBT1, demographic data, clinical characteristics and food intake habits was 0.95. The machine learning model's sensitivity, specificity, and accuracy were 100%, 70.8%, and 80.5%, respectively. Conclusion: Salivary levels of DMBT1 and CSTB may be accurate in diagnosing GCs. Machine learning analyses using salivary biomarkers, demographic, clinical and nutrition habits data simultaneously could provide affordability models with acceptable accuracy for differentiation of GC by a cost-effective and non-invasive method.

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