Analysis of Risk Factors for Acute Gangrenous Cholecystitis: A Single-Center Retrospective Study

Background: Acute gangrenous cholecystitis (AGC) is a serious clinical condition associated with high morbidity and mortality rates. To improve the diagnostic accuracy of AGC has great signifcance, Methods : In this study, a retrospective analysis was conducted on preoperative clinical and MR imaging data from 119 AGC patients and 198 non-AGC patients admitted to the Department of Hepatobiliary Surgery, Puren Hospital Affiliated to Wuhan University of Science and Technology. Logistic regression analysis was used to identify independent risk factors for AGC occurrence. Five predictive models were constructed based on the area under the ROC curve (AUC) of each independent risk factor. The optimal predictive model was selected using the DeLong test, and its histogram and fitted curve were plotted. Results : Five MR findings were identified as independent predictors of AGC: discontinuous enhancement of the gallbladder wall（DEGW）, maximum thickness of the gallbladder wall（MTGW），pericholecystitis（PS）, neutrophil-to-lymphocyte ratio（NLR）, and gallbladder neck stone impaction（GNS）. Model 3 (DEGW+MTGW+PS) demonstrated an AUC of 0.947. Delong's test revealed that Model 3 as the optimal diagnostic model. Calibration curves demonstrated the prediction model's excellent predictive performance. Conclusion : The non-invasive MR imaging prediction model for acute gallbladder cholecystitis (AGC), constructed using three combined indicators—discontinuous enhancement of the gallbladder wall, pericholecystitis, and maximum gallbladder wall thickness—provides reliable diagnostic evidence.