ArticleFrontiers in medicine2021
Application of Machine Learning Algorithms to Predict Lymph Node Metastasis in Early Gastric Cancer.
Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Artificial intelligence models in predicting lymph node metastasis in early gastric cancer: a systematic review and meta -analysis.Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques · 2026Pooled it
- Risk prediction models for lymph node metastasis in early gastric cancer patients: a systematic review and meta-analysis.BMC gastroenterology · 2025Pooled it
- Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges.Cancer reports (Hoboken, N.J.) · 2026Review
- Establishment and Validation of Prediction Models for Lymph Node Metastasis and Long-Term Survival in Patients with T1b Early Gastric Cancer: A Retrospective Cohort Study with 10-Year Follow-Up.Annals of surgical oncology · 2026Article
- Machine Learning-Based Diagnostic Models for Early Gastric Cancer Using Clinical Laboratory Indicators.International journal of general medicine · 2026Article
- Development and validation of machine learning nomograms for predicting survival in stage IV pancreatic cancer: A retrospective study.World journal of gastrointestinal oncology · 2025Article
- An ensemble learning model to predict lymph node metastasis in early gastric cancer.Scientific reports · 2025Article
- Machine learning models based on log odds of positive lymph nodes for predicting survival in TFrontiers in oncology · 2025Article
- Risk factors and nomogram development for lymph node metastasis in early-onset early-stage gastric cancer: a retrospective cohort study.Frontiers in oncology · 2025Article
- Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study.Journal of cancer research and clinical oncology · 2024Article
- A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024Article
- Prediction of hepatic metastasis in esophageal cancer based on machine learning.Scientific reports · 2024Article
- Article
- Interpretable deep learning model to predict lymph node metastasis in early gastric cancer using whole slide images.American journal of cancer research · 2024Article
- Construction and validation of a nomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms.Frontiers in oncology · 2024Article
- A discrimination model by machine learning to avoid gastrectomy for early gastric cancer.Annals of gastroenterological surgery · 2023Article
- Application of machine learning algorithm in prediction of lymph node metastasis in patients with intermediate and high-risk prostate cancer.Journal of cancer research and clinical oncology · 2023Article
- Predicting the influence ofFrontiers in genetics · 2022Article
- Prediction of bone metastasis in non-small cell lung cancer based on machine learning.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to establish the best early gastric cancer lymph node metastasis (LNM) prediction model through machine learning (ML) to better guide clinical diagnosis and treatment decisions.
methodsWe screened gastric cancer patients with T1a and T1b stages from 2010 to 2015 in the Surveillance, Epidemiology and End Results (SEER) database and collected the clinicopathological data of patients with early gastric cancer who were treated with surgery at the Second Affiliated Hospital of Nanchang University from January 2014 to December 2016. At the same time, we applied 7 ML algorithms-the generalized linear model (GLM), RPART, random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), regularized dual averaging (RDA), and the neural network (NNET)-and combined them with patient pathological information to develop the best prediction model for early gastric cancer lymph node metastasis. Among the SEER set, 80% were randomly selected to train the models, while the remaining 20% were used for testing. The data from the Second Affiliated Hospital were considered as the external verification set. Finally, we used the AUROC, F1-score value, sensitivity, and specificity to evaluate the performance of the model.
resultsThe tumour size, tumour grade, and depth of tumour invasion were independent risk factors for early gastric cancer LNM. Comprehensive comparison of the prediction model performance of the training set and test set showed that the RDA model had the best prediction performance (F1-score = 0.773; AUROC = 0.742). The AUROC of the external validation set was 0.73.
conclusionsTumour size, tumour grade, and depth of tumour invasion were independent risk factors for early gastric cancer LNM. ML predicted LNM risk more accurately, and the RDA model had the best predictive performance and could better guide clinical diagnosis and treatment decisions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.