ArticleBMC medical imaging2025
Predicting ESWL success for ureteral stones: a radiomics-based machine learning approach.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Predicting intervention or predicting practice? Reappraising treatment pathways in renal colic.Urolithiasis · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
objectivesThis study aimed to develop and validate a machine learning (ML) model that integrates radiomics and conventional radiological features to predict the success of single-session extracorporeal shock wave lithotripsy (ESWL) for ureteral stones.
methodsThis retrospective study included 329 patients with ureteral stones who underwent ESWL between October 2022 and June 2024. Patients were randomly divided into a training set (n = 230) and a test set (n = 99) in a 7:3 ratio. Preoperative clinical data and noncontrast CT images were collected, and radiomic features were extracted by outlining the stone's region of interest (ROI). Univariate analysis was used to identify clinical and conventional radiological features related to the success of single-session ESWL. Radiomic features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm to calculate a radiomic score (Rad-score). Five machine learning models (RF, KNN, LR, SVM, AdaBoost) were developed using 10-fold cross-validation. Model performance was assessed using AUC, accuracy, sensitivity, specificity, and F1 score. Calibration and decision curve analyses were used to evaluate model calibration and clinical value. SHAP analysis was conducted to interpret feature importance, and a nomogram was built to improve model interpretability.
resultsUreteral diameter proximal to the stone (UDPS), stone-to-skin distance (SSD), and renal pelvic width (RPW) were identified as significant predictors. Six radiomic features were selected from 1,595 to calculate the Rad-score. The LR model showed the best performance on the test set, with an accuracy of 83.8%, sensitivity of 84.9%, specificity of 82.6%, F1 score of 84.9%, and AUC of 0.888 (95% CI: 0.822-0.949). SHAP analysis indicated that the Rad-score and UDPS were the most influential features. Calibration and decision curve analyses confirmed the model's good calibration and clinical utility.
conclusionThe LR model, integrating radiomics and conventional radiological features, demonstrated strong performance in predicting the success of single-session ESWL for ureteral stones. This approach may assist clinicians in making more accurate treatment decisions.
trial registrationRetrospectively. CLINICAL TRIAL NUMBER: Not applicable.
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