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已发表论文
基于临床实验室指标的早期胃癌机器学习诊断模型
Authors Ji R , Yang R, Yao J, Dai S, Zhu X , Ye Q
Received 7 August 2025
Accepted for publication 14 January 2026
Published 28 January 2026 Volume 2026:19 559103
DOI https://doi.org/10.2147/IJGM.S559103
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Prof. Dr. Gopal Krishna Dhali
Runbi Ji,1,2 Ruoyu Yang,1,2 Jun Yao,1 Shenglan Dai,1 Xin Zhu,1 Qiang Ye1
1The Affiliated People’s Hospital of Jiangsu University, Zhenjiang, Jiangsu, 212002, People’s Republic of China; 2Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, 212013, People’s Republic of China
Correspondence: Runbi Ji, The Affiliated People’s Hospital of Jiangsu University, Zhenjiang, Jiangsu, 212002, People’s Republic of China, Tel +86 511 88915575, Fax +86 511 85234387, Email runbiji@163.com
Background: The occurrence of gastric cancer is a complex pathological process leading to multiple abnormalities in clinical laboratory indicators. Machine learning techniques can make it easy to handle millions of variables to make more accurate predictions and diagnoses of diseases.
Methods: Clinical data from gastric cancer patients in a single-center who underwent surgery between 2016 and 2023 were collected. Five machine learning algorithms (extreme gradient boosting, XGBoost; random forest, RF; support vector machine-recursive feature elimination, SVM-RFE; light gradient boosting machine, LGBM; and recursive partitioning, rpart) were utilized to develop diagnostic models. Among the date, 60% were randomly selected to train the models, while the remaining 40% were used for testing. We used the area under the receiver operating characteristic curve (AUROC), F1-score value, sensitivity, and specificity to evaluate the performance of models.
Results: The XGBoost algorithm showed the best performance in gastric cancer diagnosis, with significantly higher area under curve (AUC) (combining blood indicators and pathological parameters, AUC=0.9909) value than other models. Glutathione reductase (GR), carbohydrate antigen 724 (CA724), erythrocytes (RBC), carbohydrate antigen 242 (CA242), and albumin (ALB) contributed the most to the diagnosis. The tumor size were independent risk factors for early gastric cancer.
Conclusion: Machine learning combined blood indicators and pathological parameters could predict gastric cancer risk more accurately. The XGBoost model had the best diagnostic performance. The study provides confirmatory data support for the preclinical implementation of the model.
Keywords: early gastric cancer, machine learning, diagnostic model, clinical laboratory indicators, glutathione reductase
1The Affiliated People’s Hospital of Jiangsu University, Zhenjiang, Jiangsu, 212002, People’s Republic of China; 2Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, 212013, People’s Republic of China
Correspondence: Runbi Ji, The Affiliated People’s Hospital of Jiangsu University, Zhenjiang, Jiangsu, 212002, People’s Republic of China, Tel +86 511 88915575, Fax +86 511 85234387, Email runbiji@163.com
Background: The occurrence of gastric cancer is a complex pathological process leading to multiple abnormalities in clinical laboratory indicators. Machine learning techniques can make it easy to handle millions of variables to make more accurate predictions and diagnoses of diseases.
Methods: Clinical data from gastric cancer patients in a single-center who underwent surgery between 2016 and 2023 were collected. Five machine learning algorithms (extreme gradient boosting, XGBoost; random forest, RF; support vector machine-recursive feature elimination, SVM-RFE; light gradient boosting machine, LGBM; and recursive partitioning, rpart) were utilized to develop diagnostic models. Among the date, 60% were randomly selected to train the models, while the remaining 40% were used for testing. We used the area under the receiver operating characteristic curve (AUROC), F1-score value, sensitivity, and specificity to evaluate the performance of models.
Results: The XGBoost algorithm showed the best performance in gastric cancer diagnosis, with significantly higher area under curve (AUC) (combining blood indicators and pathological parameters, AUC=0.9909) value than other models. Glutathione reductase (GR), carbohydrate antigen 724 (CA724), erythrocytes (RBC), carbohydrate antigen 242 (CA242), and albumin (ALB) contributed the most to the diagnosis. The tumor size were independent risk factors for early gastric cancer.
Conclusion: Machine learning combined blood indicators and pathological parameters could predict gastric cancer risk more accurately. The XGBoost model had the best diagnostic performance. The study provides confirmatory data support for the preclinical implementation of the model.
Keywords: early gastric cancer, machine learning, diagnostic model, clinical laboratory indicators, glutathione reductase