Applying urinary ultrasound combined with CT to predict the risk of spontaneous ureteral stone passage

We aimed to develop a comprehensive predictive model for spontaneous stone passage (SSP) by integrating parameters from urinary ultrasound, non-contrast computed tomography (NCCT), and clinical markers.

This retrospective cohort study included 303 patients with unilateral solitary ureteral stones (≤10 mm) who underwent both ultrasound and NCCT before conservative management between July 2023 and July 2025. Demographic, clinical, ultrasound, and NCCT parameters were recorded. Patients were followed for one month to assess SSP (NCCT-confirmed expulsion) versus failure. Univariate and multivariable logistic regression analyses were performed, and model performance was evaluated using receiver operating characteristic (ROC) curves, calibration analysis, and decision curve analysis (DCA).

Of the 303 patients, 191 achieved SSP and 112 failed. Independent predictors of SSP included stone location (middle/lower vs. upper ureter), smaller transverse stone diameter, thinner ureteral wall thickness (UWT), higher ureteral jet frequency (UJF), and greater stone-side ureteral jet velocity (all p < 0.05). The integrated model achieved an AUC of 0.829, outperforming NCCT (0.694) and ultrasound (0.774). Calibration and decision curve analyses (DCA) confirmed good agreement and clinical utility.

Combining ultrasound and NCCT parameters significantly improved prediction of SSP compared with single-modality approaches. This model enables individualized risk stratification, supporting clinical decision-making to reduce unnecessary interventions and optimize outcomes.