ArticleFrontiers in oncology2022
Machine Learning-Based Radiological Features and Diagnostic Predictive Model of Xanthogranulomatous Cholecystitis.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 14 citations in OpenAlex.
- Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle-aged and older US people with prediabetes or diabetes.Clinical cardiology · 2023Trial
- Advances in Imaging and Diagnosis of Emphysematous Cholecystitis.Healthcare (Basel, Switzerland) · 2026Review
- GBCapsNet: A calibrated capsule network for automated gallbladder disease diagnosis via ultrasound imaging.PloS one · 2026Article
- The Challenging Management of Gallbladder Masses: A Case of Xanthogranulomatous Cholecystitis and Diffuse Biliary Hamartomatosis Mimicking Gallbladder Metastatic Cancer.Case reports in surgery · 2026Article
- Machine learning model for differentiating xanthogranulomatous cholecystitis and gallbladder cancer in multicenter largescale study.NPJ digital medicine · 2025Article
- Towards precision medicine strategies using plasma proteomic profiling for suspected gallbladder cancer: A pilot study.JHEP reports : innovation in hepatology · 2025Article
- 18F-FDG PET/CT in xanthogranulomatous cholecystitis with CA199 elevation: diagnostic dilemmas and differentiation strategies.Frontiers in medicine · 2025Article
- Deep-learning models for differentiation of xanthogranulomatous cholecystitis and gallbladder cancer on ultrasound.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Article
- Gallbladder cancer masquerading as xanthogranulomatous cholecystitis: a case report and literature review.Frontiers in oncology · 2024Article
- Machine learning-based diagnostic model for preoperative differentiation between xanthogranulomatous cholecystitis and gallbladder carcinoma: a multicenter retrospective cohort study.Frontiers in oncology · 2024Article
- Differential diagnoses of gallbladder tumors using CT-based deep learning.Annals of gastroenterological surgery · 2022Article
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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