{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:36:22Z","timestamp":1792629382445,"version":"4.5.0"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T00:00:00Z","timestamp":1743724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>This study aimed to construct and validate diagnostic models for the Operative Link on Gastritis Assessment (OLGA) and Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) staging systems using three different methodologies based on magnifying endoscopy with narrow-band imaging (ME-NBI) features, to evaluate model performance, and to analyse risk factors for high-risk OLGA\/OLGIM stages.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We enrolled 356 patients who underwent white-light endoscopy and ME-NBI at the Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine, between January 2022 and September 2023. Clinical data were recorded. Chi-square or Fisher\u2019s exact tests were used to analyse differences in endoscopic features between OLGA\/OLGIM stages. Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. Receiver operating characteristic (ROC) curves were plotted using Python 3.12.4. Model accuracy, area under the ROC curve (AUC), sensitivity, and specificity were calculated for comprehensive validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>All three models demonstrated excellent diagnostic performance, with random forest and XGBoost models showing marginally superior accuracy, AUC values, and sensitivity compared with the Bayesian stepwise discrimination model. For OLGA staging, the AUC values were 0.928, 0.958, and 0.966, with accuracies of 0.854, 0.902, and 0.918 for Bayesian, random forest, and XGBoost models, respectively. For OLGIM staging, the corresponding AUC values were 0.924, 0.975, and 0.979, with accuracies of 0.910, 0.938, and 0.927. Risk factors for high-risk OLGA included lesion location (subcardial and lower body greater curvature), intestinal metaplasia patches, lesion size, demarcation line (DL), and margin regularity of micro-capillary demarcation line (MCDL). Risk factors for high-risk OLGIM included <jats:italic>Helicobacter pylori<\/jats:italic> infection status, mucosal condition, lesion location (lesser curvature and lower body greater curvature), erosion, lesion size, DL, vessel and epithelial classification (VEC), white globe appearance (WGA), and MCDL margin regularity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>All three models demonstrated robust accuracy and predictive capability, confirming that conventional white-light endoscopy combined with ME-NBI features provides valuable diagnostic reference for clinical risk assessment of precancerous gastric lesions.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2025.1554523","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T10:32:30Z","timestamp":1743762750000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Risk prediction model for precancerous 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