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已发表论文
可解释机器学习模型在坏疽性胆囊炎预测中的开发与验证：一项多中心回顾性研究
Received 10 July 2025
Accepted for publication 9 December 2025
Published 17 December 2025 Volume 2025:18 Pages 17747—17758
DOI https://doi.org/10.2147/JIR.S552926
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Fatih Türker
Yilong Hu,1,* Yunfeng Chen,2,* Hailiang Zhao3
1Department of General Surgery of the International Medical Center, The Fourth Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 2Department of Ultrasound, Nanjing Gaochun People’s Hospital, Nanjing, Jiangsu, People’s Republic of China; 3Department of Gastroenterology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Hailiang Zhao, Department of Gastroenterology, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18, Zhongshan 2nd Road, Youjiang District, Baise City, Guangxi, 533000, People’s Republic of China, Email 1120060639@163.com
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.
Keywords: gangrenous cholecystitis, machine learning, risk prediction, model interpretability
1Department of General Surgery of the International Medical Center, The Fourth Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 2Department of Ultrasound, Nanjing Gaochun People’s Hospital, Nanjing, Jiangsu, People’s Republic of China; 3Department of Gastroenterology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Hailiang Zhao, Department of Gastroenterology, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18, Zhongshan 2nd Road, Youjiang District, Baise City, Guangxi, 533000, People’s Republic of China, Email 1120060639@163.com
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.
Keywords: gangrenous cholecystitis, machine learning, risk prediction, model interpretability