World journal of gastrointestinal oncology · 2026
Risk prediction for chronic atrophic gastritis using a random forest model: A multicenter study.
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Cao Hui, Han Jing-Lue, Wu Hao, Si Shu-Ping, Ding Li-Jia, Ji Lin, Zhang Hua-Zhen, Yin Jie, Zhou Zhi-Yi, Zhang Yu-Nan, Lv Zhi-Fa, Tian Wen-Ying, Zhan Qiang, Wang Hui, An Fang-Mei
Аннотация
BACKGROUND: Chronic 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.
AIM: To construct and validate a CAG risk prediction model to achieve noninvasive and accurate identification of high-risk patients.
METHODS: This 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.
RESULTS: Six key features were ultimately included: Age, Helicobacter pylori (H. pylori) infection status, pepsinogen I/II ratio (PGR), smoking history, alcohol consumption history, and family history of GC. The model achieved AUCs of 0.8542 and 0.8073 in the training and testing sets, respectively, and an AUC of 0.8505 in the external validation cohort, demonstrating good generalizability and stability. SHAP analysis indicated that H. pylori infection, age, and PGR were the most important variables influencing CAG risk. The final model was successfully embedded into a web-based platform for convenient clinical application.
CONCLUSION: The 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.
AIM: To construct and validate a CAG risk prediction model to achieve noninvasive and accurate identification of high-risk patients.
METHODS: This 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.
RESULTS: Six key features were ultimately included: Age, Helicobacter pylori (H. pylori) infection status, pepsinogen I/II ratio (PGR), smoking history, alcohol consumption history, and family history of GC. The model achieved AUCs of 0.8542 and 0.8073 in the training and testing sets, respectively, and an AUC of 0.8505 in the external validation cohort, demonstrating good generalizability and stability. SHAP analysis indicated that H. pylori infection, age, and PGR were the most important variables influencing CAG risk. The final model was successfully embedded into a web-based platform for convenient clinical application.
CONCLUSION: The 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.