Annals of medicine · 2026
Construction of an XGBoost-SHAP-based malignant transformation risk prediction model for gallbladder polyps.
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Luo Wen-Hui, Cai Meng-Han, Wang Yu, Wu Ying-Jun, Yang Jun-Fan, Li Shao-Jun
Аннотация
BACKGROUND: To 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.
METHODS: A retrospective cohort of 1,027 surgical patients was enrolled from Yantai Yuhuangding Hospital (training set: n = 933) and Shanghai Eastern Hepatobiliary Surgery Hospital (validation set: n = 94). Feature selection for the training set was performed using the least absolute shrinkage and selection operator (LASSO) regression method. A predictive model was constructed with the XGBoost machine learning algorithm and evaluated using Shapley Additive exPlanation (SHAP).
RESULTS: LASSO 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.
CONCLUSION: This 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.
METHODS: A retrospective cohort of 1,027 surgical patients was enrolled from Yantai Yuhuangding Hospital (training set: n = 933) and Shanghai Eastern Hepatobiliary Surgery Hospital (validation set: n = 94). Feature selection for the training set was performed using the least absolute shrinkage and selection operator (LASSO) regression method. A predictive model was constructed with the XGBoost machine learning algorithm and evaluated using Shapley Additive exPlanation (SHAP).
RESULTS: LASSO 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.
CONCLUSION: This 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.