Development and Validation of an Explainable Machine Learning Model for Gangrenous Cholecystitis Prediction: A Multicenter Retrospective Study.

Purpose: To develop and externally validate an interpretable machine learning model for preoperative prediction of Gangrenous cholecystitis (GC) using multicenter clinical data. Patients and Methods: This retrospective multicenter study included 744 patients with cholecystitis who underwent cholecystectomy at one institution, split into training (n=521) and testing (n=223) cohorts, and a temporal external validation cohort of 300 patients from a second center. Twenty preoperative variables were screened by LASSO regression and Boruta algorithm; predictors selected by both were used to construct six machine learning models. Model performance was assessed via AUC, calibration, and decision curve analysis. SHAP analysis provided model interpretability. Results: The Random Forest (RF) model demonstrated superior predictive performance, achieving an AUC of 0.893 in the training set, 0.875 in the testing set, and 0.818 in external validation. Calibration and decision curve analyses indicated excellent agreement and clinical benefit. SHAP analysis identified gallbladder wall thickening, C-reactive protein, pericholecystic fluid, white blood cell count, and impacted stone as the most influential predictors, ensuring transparency of model decisions. Conclusion: In our multicenter cohorts, this interpretable machine learning model showed good discrimination for preoperative risk stratification of gangrenous cholecystitis and acceptable generalizability between centers. By integrating clinical, laboratory, and imaging features and providing explainability, the approach may assist perioperative decision-making when used alongside clinical judgment. Prospective, multicenter evaluations and clinical impact studies are warranted before routine clinical adoption.