Background: Xanthogranulomatous cholecystitis (XGC) is a rare, chronic gallbladder inflammation often mistaken for gallbladder cancer (GBC) on imaging. Accurate differentiation is vital for appropriate treatment. This study aims to enhance computed tomography (CT) scan diagnostic accuracy for distinguishing XGC from GBC. Methods: This retrospective study included patients diagnosed with XGC and GBC between 2014 and 2023. CT images of 70 patients (16 GBC, 54 XGC) were reviewed. Radiologists assessed CT parameters: gallbladder wall thickening, intramural hypoattenuating nodules, enhancement characteristics, mucosal line continuity, pericholecystic fat stranding, presence of stones, bile duct dilatation, hepatic invasion, invasion to adjacent structures, and lymph node size. Results: Among 70 patients, there were 38 males (54%) and 32 females (46%), with a median age of 62 years. GBC patients were significantly older (median age 72 years) compared to XGC patients (60 years) (P=0.001). Diffuse gallbladder wall thickening was more frequent in XGC (70%) than GBC (12.5%) (P<0.001). Continuous mucosal lines and intramural hypoattenuating nodules were more common in XGC (P<0.001 and P=0.010, respectively). Intrahepatic bile duct dilatation and invasion to adjacent structures were significantly linked with GBC (P8 mm indicated GBC (P<0.001), with a cutoff providing 71.4% sensitivity and 84% specificity (AUC: 0.843, P<0.001). CT showed 75% sensitivity (95% CI: 48-93%), 74% specificity (95% CI: 60%-85%), and 74% accuracy (95% CI: 62%-84%). Conclusion: CT imaging can effectively differentiate XGC from GBC, and larger studies can further improve diagnostic accuracy.

Background: Cholelithiasis can lead to significant complications if not diagnosed and treated promptly. Recent advances in deep learning and the improved ability of computer systems to detect clinically significant textural and morphological patterns in magnetic resonance imaging (MRI) can help reduce the time and resources required for the radiological evaluation of the gallbladder and cholelithiasis. Objective: To detect cholelithiasis, a support system with a graphical user interface for magnetic resonance (MR) images of the gallbladder was implemented to reduce the manual effort and time required to identify gallstones. Method: A commonly used deep learning model for pixel-level mask generation and instance segmentation, Mask Region Based Convolutional Neural Network (Mask R-CNN), was modified, trained, and evaluated to provide a robust pipeline for automated analysis. The primary aim was to automatically locate and label the gallbladder in T2-weighted axial MR images to detect gallstones and highlight the visual characteristics of the target region, thereby supporting radiologists. All automation was designed to operate on a single optimal slice instead of the entire volume. While this approach limits generalisability, it offers a practical starting point for method development. This setup reflects a feasibility-oriented design, rather than a comprehensive diagnostic capability. The dataset included 788 axial MR images from different patients. Each image was labeled and segmented by an experienced radiologist to train and test the models at the image level. Results: The proposed model with squeeze and excitation (SE) modification improved classification accuracy, and at the image level, stone detection improved in terms of accuracy, precision, and specificity, although recall and F1 scores slightly decreased. Conclusions: The results show that the modified Mask R-CNN model can detect gallstones with up to 0.89 accuracy, supporting the clinical applicability of the proposed method.