ArticleFrontiers in immunology2026
A machine-learning model for staging autoimmune gastritis based on endoscopic and serological features.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: Autoimmune gastritis (AIG) lacks a unified, practical staging system. The AIG-atrophic stage (AIG-AS) scheme, based on the proportional area of remnant oxyntic mucosa (ROM), is promising but observer-dependent. We developed machine-learning models to stage AIG using endoscopic and serological features. Methods: This single-center cross-sectional study enrolled 203 patients with AIG confirmed by integrated endoscopic, histological, and serological criteria between December 2023 and December 2025. White-light endoscopy (WLE), magnifying endoscopy with narrow-band imaging (ME-NBI), and a serum panel were collected. Patients were classified under AIG-AS as Stage 1 (50%<ROM ≤ 100%), Stage 2 (10%<ROM ≤ 50%), or Stage 3 (ROM ≤ 10%), and additionally as early-to-intermediate (Stages 1 + 2) versus advanced (Stage 3). Seven algorithms-LASSO logistic regression, elastic net, random forest, XGBoost, support vector machine, gradient boosting, and stacking-were trained on five feature sets using stratified 5-fold nested cross-validation, with SMOTE applied only within training folds. SHAP analysis provided interpretability. Results: Stages 1, 2, and 3 included 87 (42.9%), 36 (17.7%), and 80 (39.4%) patients. The combined endoscopy+serology random forest achieved the highest three-class macro-AUC (0.878 ± 0.051; accuracy 74.9%), surpassing WLE stacking (0.853 ± 0.056) and serology-only models (≤0.693). For binary staging, the WLE+ME-NBI random forest reached an AUC of 0.913 ± 0.046. Loss of gastric folds, gastrin-17, pepsinogen I, crypt-opening grade, and cast-off skin appearance were the dominant predictors. Barrett esophagus prevalence was 66.8%, with long-segment Barrett esophagus 3.3-fold more common in advanced AIG than in earlier disease. Conclusions: Multi-modal machine learning enables accurate, interpretable AIG staging. Endoscopic features outperform serology, and combined models offer the best three-class performance. The high prevalence of Barrett esophagus supports routine esophageal surveillance in AIG.
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