ArticleEuropean radiology2023
Risk stratification of gallbladder masses by machine learning-based ultrasound radiomics models: a prospective and multi-institutional study.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 22 citations in OpenAlex.
- Prediction of Gallbladder Lesions Malignancy Risk Using Contrast-Enhanced Ultrasound and Arrival-Time Parametric Imaging.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- Review
- A novel machine learning-based model for precise prediction of postoperative recurrence in gallbladder cancer: development and multicenter validation across four centers.World journal of surgical oncology · 2026Article
- A SHAP-interpretable XGBoost model: MRI-based intratumoral perfusion heterogeneity predicts HER2-zero, -low, and -positive ternary expression status in breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- An integrative deep learning model based on dual-mode ultrasound for diagnosing gallbladder polyps.Insights into imaging · 2026Article
- Ultrasound-based deep learning radiomics for the differential diagnosis of benign and malignant subpleural pulmonary lesions.Frontiers in oncology · 2026Article
- Gallstone disease classification using SLOA-optimized CatBoost classifier with explainable AI.PloS one · 2026Article
- B-mode ultrasound and contrast-enhanced ultrasound-based radiomics interpretable analysis for the prediction of macrotrabecular-massive subtype of hepatocellular carcinoma.The ultrasound journal · 2025Article
- Gallstone Classification Using Random Forest Optimized by Sand Cat Swarm Optimization Algorithm with SHAP and DiCE-Based Interpretability.Sensors (Basel, Switzerland) · 2025Article
- Update on the Role of Imaging in the Diagnosis, Staging, and Prognostication of Gallbladder Cancer.The Indian journal of radiology & imaging · 2025Review
- Development and validation of an ultrasound-based interpretable machine learning model for the classification of ≤3 cm hepatocellular carcinoma: a multicentre retrospective diagnostic study.EClinicalMedicine · 2025Article
- Ultrasound-based radiogenomics: status, applications, and future direction.Ultrasonography (Seoul, Korea) · 2025Article
- Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study.Cancer medicine · 2025Article
- Multi-Modality Ultrasound Model for Renal Fibrosis Assessment in Chronic Kidney Disease: Integrating Grayscale and Color Doppler Ultrasound Radiomics with Shear Wave Elastography.International journal of general medicine · 2025Article
- Current status of artificial intelligence analysis for the diagnosis of gallbladder diseases using ultrasonography: a scoping review.Translational gastroenterology and hepatology · 2025Review
- Artificial intelligence-aided ultrasound imaging in hepatopancreatobiliary surgery: where are we now?Surgical endoscopy · 2024Review
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Authors and funding
17 authors at 5 institutions in 1 country.
Funding
Abstract
objectiveThis study aimed to evaluate the diagnostic performance of machine learning (ML)-based ultrasound (US) radiomics models for risk stratification of gallbladder (GB) masses.
methodsWe prospectively examined 640 pathologically confirmed GB masses obtained from 640 patients between August 2019 and October 2022 at four institutions. Radiomics features were extracted from grayscale US images and germane features were selected. Subsequently, 11 ML algorithms were separately used with the selected features to construct optimum US radiomics models for risk stratification of the GB masses. Furthermore, we compared the diagnostic performance of these models with the conventional US and contrast-enhanced US (CEUS) models.
resultsThe optimal XGBoost-based US radiomics model for discriminating neoplastic from non-neoplastic GB lesions showed higher diagnostic performance in terms of areas under the curves (AUCs) than the conventional US model (0.822-0.853 vs. 0.642-0.706, p < 0.05) and potentially decreased unnecessary cholecystectomy rate in a speculative comparison with performing cholecystectomy for lesions sized over 10 mm (2.7-13.8% vs. 53.6-64.9%, p < 0.05) in the validation and test sets. The AUCs of the XGBoost-based US radiomics model for discriminating carcinomas from benign GB lesions were higher than the conventional US model (0.904-0.979 vs. 0.706-0.766, p < 0.05). The XGBoost-US radiomics model performed better than the CEUS model in discriminating GB carcinomas (AUC: 0.995 vs. 0.902, p = 0.011).
conclusionsThe proposed ML-based US radiomics models possess the potential capacity for risk stratification of GB masses and may reduce the unnecessary cholecystectomy rate and use of CEUS. CLINICAL RELEVANCE STATEMENT: The machine learning-based ultrasound radiomics models have potential for risk stratification of gallbladder masses and may potentially reduce unnecessary cholecystectomies. KEY POINTS: • The XGBoost-based US radiomics models are useful for the risk stratification of GB masses. • The XGBoost-based US radiomics model is superior to the conventional US model for discriminating neoplastic from non-neoplastic GB lesions and may potentially decrease unnecessary cholecystectomy rate for lesions sized over 10 mm in comparison with the current consensus guideline. • The XGBoost-based US radiomics model could overmatch CEUS model in discriminating GB carcinomas from benign GB lesions.
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