Karaca Y. et al., 2026: Comparison of Ureteral Stone Volume and Maximum Stone Length in Predicting Shock Wave Lithotripsy Success.

Karaca Y, Sinanoglu O, Karaca DI, Sarica G, Sarica K.
Arch Esp Urol. 2026 Mar;79(2):241-246. doi: 10.56434/j.arch.esp.urol.20267902.29

Abstract

Background: This study aimed to compare maximum stone length and stone volume as predictors of stone-free (SF) status after extracorporeal shock wave lithotripsy (SWL) for ureteral stones.

Methods: Data from 236 patients treated with SWL for solitary radiopaque ureteral stone (5-15 mm) between January 2022 and March 2024 were retrospectively analysed. Stone length and volume were measured on noncontrast computed tomography. SF status was determined on radiography and ultrasonography 4 weeks after the last SWL session and defined as no residual stone or < 4 mm residual fragments. Binary logistic regression models were used to evaluate potential predictors of SWL outcome in univariable and multivariable analyses.

Results: The overall SF rate was 68%. The non-SF group had significantly higher body mass index (BMI), Hounsfield unit, maximum stone length, stone volume, skin-to-stone distance and proximal ureteral diameter (all p < 0.05) compared with the SF group. Multivariate analysis revealed maximum stone length (p = 0.004) and BMI (p = 0.009) as independent predictors of SWL outcome, whilst stone volume lost statistical significance (p = 0.2).

Conclusions: Simple linear stone measurement without routine reliance on computed tomography (CT)-based volumetric assessment appears sufficient for predicting SWL outcomes in ureteral stones. This approach may help avoid the time-consuming nature and potential radiation exposure associated with volumetric analysis. Further prospective, multicentre studies with standardised imaging and large patient cohorts are warranted to confirm these results.

Comment Peter Alken

Somehow it seems to me that this is a step back even though it may be statistically correct that stone length is a better predictor of ESWL success than stone volume.

See also:

Katsimperis S, Tzelves L, Juliebø-Jones P, Tsaturyan A, Ventimiglia E, Perri D, Boeykens M, Jahrreiss V, Moretto S, Sahin MF, Olivero A, Nowak Ł, Pietropaolo A, Martinez BB, Somani B, Skolarikos A. From conventional imaging software to artificial intelligence: tools for stone volume assessment in urolithiasis. Α review by the EAU and YAU sections of endourology. World J Urol. 2026 May 24;44(1):379. doi: 10.1007/s00345-026-06484-0. PMID: 42177680.

Peter Alken