ArticleTechnology in cancer research & treatment
Comparison of Machine Learning and Logic Regression Algorithms for Predicting Lymph Node Metastasis in Patients with Gastric Cancer: A two-Center Study.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development and Internal Validation of an Explainable Machine Learning Model for Predicting Buttock Claudication After EVAR: A Dual-Center Cohort Study.Bioengineering (Basel, Switzerland) · 2026Article
- Machine Learning-Based Survival Prediction Models for Young Patients With Gastric Cancer: Model Development and Validation Study.JMIR cancer · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery.Scientific reports · 2025Article
- Establishment of a machine learning model for predicting splenic hilar lymph node metastasis.NPJ digital medicine · 2025Article
- Retraction note: Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study.World journal of gastrointestinal surgery · 2025Article
- The exploration of surgery and survival prediction in patients with peritoneal metastasis from gastric adenocarcinoma based on the SEER database.Journal of gastrointestinal oncology · 2024Article
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7 authors.
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Abstract
objectivesThis two-center study aimed to establish a model for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) and logistic regression (LR) algorithms, and to evaluate its predictive performance in clinical practice.
methodsData of a total of 369 patients who underwent radical gastrectomy in the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy in the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Besides, 7 ML and logistic models were developed, including decision tree, random forest, support vector machine (SVM), gradient boosting machine (GBM), naive Bayes, neural network, and LR, in order to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML model was established following 10 cross-validation iterations within the training dataset, and subsequently, each model was assessed using the test dataset. The model's performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.
resultsCompared with the traditional logistic model, among the 7 ML algorithms, except for SVM, the other models exhibited higher accuracy and reliability, and the influences of various risk factors on the model were more intuitive.
conclusionFor the prediction of lymph node metastasis in gastric cancer patients, the ML algorithm outperformed traditional LR, and the GBM algorithm exhibited the most robust predictive capability.
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