ArticleAnnals of medicine2026
Construction of an XGBoost-SHAP-based malignant transformation risk prediction model for gallbladder polyps.
Article in Annals of medicine, 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
backgroundTo develop and validate a risk prediction model for malignant transformation in patients with gallbladder polyps (GBPs) using an interpretable machine learning framework and evaluate its predictive accuracy.
methodsA retrospective cohort of 1,027 surgical patients was enrolled from Yantai Yuhuangding Hospital (training set:
resultsLASSO regression identified five significant risk factors for malignant transformation in GBPs: presence of concomitant cholecystitis, polyp count, polyp base width, age, and maximum polyp diameter. The area under the receiver operating characteristic curve (AUC) was 0.862 (95% confidence interval [CI]: 0.8342-0.8893) in the training set and 0.777 (95% CI: 0.6804-0.8737) in the validation set. SHAP analysis illustrated the contribution of each factor.
conclusionThis study developed and validated a risk prediction model for malignant transformation in patients with GBPs. The model demonstrated favorable discrimination, calibration, accuracy, and clinical applicability. Integration with SHAP technology may assist clinicians in optimizing treatment and management strategies.
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