CT-based deep learning model for predicting the success of extracorporeal shock wave lithotripsy in treating ureteral stones larger than 1 cm.

Objectives: To develop a deep learning (DL) model based on computed tomography (CT) images to predict the success of extracorporeal shock wave lithotripsy (SWL) treatment for patients with ureteral stones larger than 1 cm.

Materials and methods: We enrolled 333 patients who underwent SWL treatment for ureteral stones and randomly divided them into training and test sets. A DL model was built based on CT images of ureteral stones to predict SWL outcomes. The predictive efficacy of the DL model was assessed by comparing it with traditional and radiomics models.

Results: The DL model demonstrated significantly better predictive performance in both training and test sets compared to radiomics (training set, AUC: 0.993 vs. 0.923, P < 0.001; test set AUC: 0.982 vs. 0.846, P < 0.001) and traditional models (training set AUC: 0.993 vs. 0.75, P = 0.005; test set AUC: 0.982 vs. 0.677, P < 0.001). Decision curve analysis (DCA) also proved that the DL model brought more benefit in predicting the success of SWL treatment than other methods.

Conclusion: The DL model based on CT images showed excellent ability to predict the probability of success of SWL treatment for patients with ureteral stones larger than 1 cm, providing a new auxiliary tool for clinical treatment decision-making.