ArticleScientific reports2019
Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review.Journal of clinical epidemiology · 2023Pooled it
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies.BMC medical informatics and decision making · 2021Trial
- Development and Validation of an Interpretable Machine Learning Model for StagingJournal of medical Internet research · 2026Article
- Five-Year Unified Risk Prediction Model for Gastric and Esophageal Cancer Based on Nationwide Health Check-Up Data in Japan: J-GESS Score.Journal of gastroenterology and hepatology · 2026Article
- Machine learning-based gastric cancer risk prediction in an asymptomatic screening population: a retrospective cohort study.Journal of gastrointestinal oncology · 2026Article
- Multi-phase hybrid metabolomics framework identifies clinically applicable plasma signatures for early detection of gastric cancer.Nature communications · 2026Article
- Development and validation of a novel blood-based biomarker for gastric cancer triage in chronic dyspepsia.NPJ digital medicine · 2026Article
- Risk prediction for chronic atrophic gastritis using a random forest model: A multicenter study.World journal of gastrointestinal oncology · 2026Article
- Predicting clinical outcomes in Helicobacter pylori-positive patients using supervised learning through the integration of demographic and genomic features.BMC gastroenterology · 2026Article
- A Simple, Interpretable Machine Learning Model Based on Clinical Factors Accurately Predicts Incident Dysplasia or Malignancy in Barrett's Esophagus.Digestive diseases and sciences · 2025Article
- A Machine Learning Risk Prediction Model for Gastric Cancer with SHapley Additive exPlanations.Cancer research and treatment · 2025Article
- Machine Learning to Predict Early Death Despite Pancreaticoduodenectomy.The Journal of surgical research · 2025Article
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer.Scientific reports · 2024Article
- Health expenditure trajectory and gastric cancer incidence in the National Health Insurance Senior Cohort: a nested case-control study.BMC health services research · 2024Article
- Risk prediction model for gastric cancer within 5 years in healthy Korean adults.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024Article
- Machine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality.Diseases (Basel, Switzerland) · 2024Article
- Diagnosis to dissection: AI's role in early detection and surgical intervention for gastric cancer.Journal of robotic surgery · 2024Review
- Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians.BMC emergency medicine · 2024Article
Corrections and comments
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A comprehensive screening method using machine learning and many factors (biological characteristics, Helicobacter pylori infection status, endoscopic findings and blood test results), accumulated daily as data in hospitals, could improve the accuracy of screening to classify patients at high or low risk of developing gastric cancer. We used XGBoost, a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. Longitudinal and comprehensive medical check-up data were collected from 25,942 participants who underwent multiple endoscopies from 2006 to 2017 at a single facility in Japan. The participants were classified into a case group (y = 1) or a control group (y = 0) if gastric cancer was or was not detected, respectively, during a 122-month period. Among 1,431 total participants (89 cases and 1,342 controls), 1,144 (80%) were randomly selected for use in training 10 classification models; the remaining 287 (20%) were used to evaluate the models. The results showed that XGBoost outperformed logistic regression and showed the highest area under the curve value (0.899). Accumulating more data in the facility and performing further analyses including other input variables may help expand the clinical utility.
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