Xanthogranulomatous cholecystitis: Diagnosis and management

Successful Treatment of Xanthogranulomatous Cholecystitis Following Total Knee Replacement

Xanthogranulomatous cholecystitis (XGC) is a rare form of chronic gallbladder inflammation that most commonly presents as acute cholecystitis and is often mistaken for carcinoma of the gallbladder. This case details the hospital course and follow-up of a 77-year-old male who developed suspected acute acalculous cholecystitis (AAC) resulting in severe sepsis after elective left total knee arthroplasty (TKA). Histopathological findings after elective cholecystectomy later revealed XGC as the underlying etiology.

The incidental finding of xanthogranulomatous cholecystitis: a report of 10 cases

There is insufficient clinical knowledge about xanthogranulomatous cholecystitis (XGC) due to biased reporting. This study aims to investigate the incidence of XGC and evaluate the clinical outcome regarding operative time, rate of conversion and intraoperative or postoperative complications. We included 1141 patients who underwent surgery for gallbladder disease between January 2019 and December 2021. Of 1141 patients who underwent cholecystectomy, XGC was seen in 10 (0.87%). The average age is 47 (24–81 years old) with a male to female ratio of 3:2. Biliary pancreatitis and obstructive jaundice are seen in two patients who did ERCP and stenting before surgery. One patient presented with a 4 cm necrotizing soft-tissue granuloma. The BMI was high, with class I obesity in five patients. Symptoms last from 1 to 12 weeks. One patient was only diagnosed preoperatively as XGC. Four out of 10 (40%) required more than 72 h of hospitalization. All patients underwent elective sitting surgery, with eight successfully managed by laparoscopy and one converted to open. The average operative time was 90 min (43–193 min), and a postoperative drain was inserted in four patients. The median follow-up is after 24 months (11–30 months), with no postoperative collection, bleeding, complication or readmission. XGC is a rare benign entity requiring no further action upon incidental discovery. Surgical resection is the cornerstone of management, with the laparoscopic approach considered feasible and safe. Four out of 10 patients might need more than 3 days of hospitalization. In the presence of mass, the frozen section can help guide the management.

Machine Learning-Based Radiological Features and Diagnostic Predictive Model of Xanthogranulomatous Cholecystitis

Background

Xanthogranulomatous cholecystitis (XGC) is a rare benign chronic inflammatory disease of the gallbladder that is sometimes indistinguishable from gallbladder cancer (GBC), thereby affecting the decision of the choice of treatment. Thus, this study aimed to analyse the radiological characteristics of XGC and GBC to establish a diagnostic prediction model for differential diagnosis and clinical decision-making.

Methods

We investigated radiological characteristics confirmed by the RandomForest and Logistic regression to establish computed tomography (CT), magnetic resonance imaging (MRI), CT/MRI models and diagnostic prediction model, and performed receiver operating characteristic curve (ROC) analysis to prove the effectiveness of the diagnostic prediction model.

Results

Based on the optimal features confirmed by the RandomForest method, the mean area under the curve (AUC) of the ROC of the CT and MRI models was 0.817 (mean accuracy = 0.837) and 0.839 (mean accuracy = 0.842), respectively, whereas the CT/MRI model had a considerable predictive performance with the mean AUC of 0.897 (mean accuracy = 0.906). The diagnostic prediction model established for the convenience of clinical application was similar to the CT/MRI model with the mean AUC and accuracy of 0.888 and 0.898, respectively, indicating a preferable diagnostic efficiency in distinguishing XGC from GBC.

Conclusions

The diagnostic prediction model showed good diagnostic accuracy for the preoperative discrimination of XGC and GBC, which might aid in clinical decision-making.