Risk prediction for chronic atrophic gastritis using a random forest model: A multicenter study.

backgroundChronic atrophic gastritis (CAG) is a significant precancerous condition of gastric cancer (GC). CAG often lacks typical symptoms in its early stages, and clinical diagnosis relies on gastroscopy and pathological examination, which are invasive and have limitations such as poor patient compliance. Therefore, developing a noninvasive, simple, and generalizable prediction tool is crucial for the early identification of CAG.

aimTo construct and validate a CAG risk prediction model to achieve noninvasive and accurate identification of high-risk patients.

methodsThis study included 1268 subjects from a GC screening program. Multimodal data, including serological marker, demographic, lifestyle, and family history data, were collected. Subjects were grouped by pathological biopsy results. Least absolute shrinkage and selection operator regression was used for feature selection. A model was constructed using the random forest algorithm, evaluated with metrics such as the area under the curve (AUC), and interpreted using the SHapley Additive exPlanation (SHAP) method. The model was validated in an independent external cohort, and a web-based prediction platform was developed using Shiny.

resultsSix key features were ultimately included: Age,

conclusionThe random forest-based CAG prediction model is a highly accurate and interpretable tool with significant clinical utility in early screening and identifying high-risk patients.