Value of preoperative abdominal CT-based scoring system for predicting difficulty in laparoscopic cholecystectomy
10.3760/cma.j.cn115396-20250719-00188
- VernacularTitle:术前腹部CT评分系统对腹腔镜胆囊切除手术难度的预测价值
- Author:
Jingtao BI
1
;
Yaqi LIU
;
Zhixue ZHENG
;
Xuan CAI
;
Quan WU
Author Information
1. 首都医科大学附属北京积水潭医院普外科，北京 102208
- Keywords:
Cholecystectomy, laparoscopic;
Cholecystitis;
Cholelithiasis;
Risk assessment
- From:
International Journal of Surgery
2025;52(10):694-699
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of a scoring system based on preoperative abdominal computed tomography (CT) for predicting the difficulty of laparoscopic cholecystectomy (LC).Methods:A retrospective analysis was conducted on 105 patients diagnosed with gallstones or cholecystitis who underwent LC at Beijing Jishuitan Hospital, Capital Medical University from January 2021 and February 2022. Based on surgical video reviews, patients were divided into the easy group ( n=58) and the difficult group ( n=47) according to the intraoperative Parkland Grading Scale (PGS), with PGS grades 1-2 assigned to the easy group, and PGS grades 3-5 assigned to the difficult group. The normally distributed measurement data were expressed as mean±standard deviation ( ± s), and compared using independent samples t-test; the non-normally distributed measurement data were expressed as median (interquartile range) [ M ( Q1, Q3)], and compared using the rank-sum test. The count data were expressed as the number of cases and percentage, and compared using the Chi-square test or Fisher exact probability method. Univariate analysis and cut-off value determination: for continuous CT variables, univariate Logistic regression and stepwise regression analyses (with surgical difficulty grouping as the dependent variable) were performed to identify the optimal combination of predictive variables and establish a scoring system. For each significantly associated continuous variable or important CT image feature from a clinical perspective, receiver operating characteristic (ROC) curve analysis was used to evaluate its predictive performance for difficult surgery. The area under the curve (AUC) was calculated, and the optimal cut-off value was determined using the Youden index to maximize the sum of sensitivity and specificity. The categorical CT image features were scored according to their original groups. The Kappa consistency test was used to assess the strength of agreement between the preoperative abdominal CT score (grouped by the optimal cut-off) and PGS grades (easy/difficult). Decision curve analysis (DCA) was employed to validate the predictive performance of the model. Results:Stepwise Logistic regression identified seven key imaging features as the optimal predictive variables for constructing the preoperative abdominal CT scoring system: maximum gallbladder cross-sectional diameter, maximum gallbladder cross-sectional width, gallbladder wall thickness, common bile duct diameter, pericholecystic fat stranding, periductal fat stranding, and impacted cystic duct stones. Each case was scored after assigning scores based on the optimal cut-off values. The total score of the preoperative abdominal CT scoring system was ≥3 points predicted difficult LC with an AUC of 0.745 (95% CI: 0.650-0.839), sensitivity of 66.0%, and specificity of 75.9%. DCA confirmed the model′s reliable predictive performance, and the preoperative abdominal CT scoring system showed good agreement with PGS grades ( Kappa value was 0.420, P<0.001). Conclusions:The preoperative abdominal CT scoring system based on pericholecystic imaging features can effectively predict the difficulty of LC with good discriminative ability. It provides a quantitative tool for preoperative assessment, surgical scheduling, and ambulatory surgery management.
