Noninvasive CT radiomics-clinical model accurately classifies anhydrous uric acid stones: a multicenter study.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, 223300, Jiangsu, China.
- Department of Urology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou, 225300, Jiangsu, China.
- Department of Urology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, 223300, Jiangsu, China.
- Department of Urology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Northern Jiangsu Institute of Clinical Medicine, Nanjing Medical University, Huaian, 223300, Jiangsu, China. [email protected].
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, 223300, Jiangsu, China. [email protected].
Abstract
Urolithiasis, particularly anhydrous uric acid stones (AUAs), imposes significant clinical and economic burdens. Accurate preoperative differentiation of AUAs from other stone types remains challenging, yet essential for personalized patient management. In this multicenter retrospective study, 468 patients diagnosed with urolithiasis underwent preoperative non-contrast CT imaging. Radiomics analysis was performed, extracting 1,218 features that were subsequently reduced to six key features via LASSO regression. A combined predictive model was developed by integrating radiomics-derived probabilities with independent clinical predictors. The model's performance was rigorously assessed through internal and external validation datasets using ROC analysis, calibration plots, decision curve analysis, and SHAP for model interpretability. The combined radiomics-clinical model achieved excellent diagnostic performance with area under the ROC curve (AUC) values of 0.942 (training set), 0.944 (internal test set), and 0.957 (external validation set), significantly surpassing individual clinical or radiomics models. SHAP analysis identified urine pH, serum uric acid, and radiomics-derived predicted probability as critical predictive factors for AUAs. Calibration and decision curve analyses supported the model's robust predictive reliability and potential for meaningful clinical benefit. This study demonstrates that integrating CT-based radiomics with key clinical variables significantly improves preoperative differentiation of AUAs, providing a robust, non-invasive diagnostic tool that can enhance personalized treatment strategies. Future research should aim to incorporate metabolomic profiling and advanced imaging modalities to further optimize clinical translation and implementation.