A novel model using computed tomography parameters to predict shock wave lithotripsy success in ureteral stones at different locations

Variation coefficient of stone density and renal cortical thickness: the parameters evaluating non-contrast computed tomography imaging for predict extracorporeal shock wave lithotripsy success

The stone density (SD) is not the same in all parts of the stone due to the heterogeneous nature of the stone and the shock wave (SW) passes through tissues of many different densities until it reaches the stone. These factors affect the success of Extracorporeal Shock Wave Lithotripsy (ESWL). We aimed to evaluate the effect of the Variation Coefficient of Stone Density (VCSD) and Renal Cortical Tickness (RCT) on the success of ESWL. Between 2020 and 2023, 510 patients who underwent ESWL were divided into 2 groups treatment success (n:304) and treatment failure (n:206). Non-Contrast Computed Tomography (NCCT) imaging values of hydronephrosis degree of the kidney, stone location, stone volume (SV), stone-skin distance (SSD), SD, Standard deviation of Stone Density (SDSD), VCSD, RCT, Soft-Tissue Thickness (STT), Muscle Thickness (MT) were analyzed. VCSD value was obtained by dividing SDSD by SD. Along the SW, tissues were divided into three components: kidney (renal cortex), muscle and other soft tissues. RCT, MT and SSD were measured at three different angles (0°, 45°, and 90°) and these 3 lengths were averaged. In univariate analysis, Body Mass Index (BMI), SV, SD, VCSD, SSD, RCT and STT were demonstrated to affect ESWL success. In multivariate analysis, low BMI, SV, SD, RCT and large VCSD were significant independent predictors of ESWL success. Among these parameters, VCSD had the highest prediction accuracy, followed by SD, SV, RCT and BMI, respectively. This study demonstrated that VCSD value and RCT are predictive parameters in determining the treatment of patients with urinary calculi and selecting suitable ESWL candidates.

CT-Based Predictor for the Success of 12/14-Fr Ureteral Access Sheath Placement

PURPOSE

Ureteral access sheaths (UAS) are widely used in retrograde intrarenal surgery (RIRS), and this study aimed to develop a model for predicting the success of UAS placement based on computed tomography.

METHODS

We analyzed the clinical data of 847 patients who received ureteroscopy. Data on patient and stone characteristics and several computed tomography (CT)-based measurements were collected. A nomogram predicting the success of UAS placement was developed and validated using R software.

RESULTS

Two hundred and forty-seven patients were identified. Twenty-five patients (10.1%) failed to pass through the UAS. A model with three factors including the short diameter of ureteral calculi, the short diameter of hydronephrosis, and the diameter of the narrowest part of the renal parenchyma was to be strongly practical and had a high area under the curve on internal validation (80.3%). Using a threshold cutoff of 92%, the sensitivity and specificity for predicting UAS placement were 0.35 and 0.92, respectively.

CONCLUSION

Our study provides a nomogram for predicting the success of UAS placement, and this model could help discriminate patients who are likely to suffer from failed UAS insertion; preoperative ureteral stenting is recommended according to the prediction.