Utility of stone volume estimated by software algorithm in predicting success of medical expulsive therapy

Abstract

Introduction: We sought to assess the accuracy of using stone volume (SV) estimated with a software algorithm as a predictor for stone passage in a trial of medical expulsive therapy (MET).
 Methods: We identified patients with ureteral stones discharged from the ER on MET. Patients with infection, non-ureteral stones, or needing immediate surgical intervention were excluded. For each stone, longest dimension (LD) was recorded and SV was estimated by a computed tomography (CT)-based region growing (RG) algorithm and standard ellipsoid formula (EF). Stone passage within 30 days was assessed via electronic chart and followup phone call.
 Results: Fifty-one patients were included for analysis (53±16.7 years, 24% female). The mean LD was 4.85±2.02 mm. The mean SV was similar by EF and RG (0.051±0.057cm3 vs. 0.049± 0.052 cm3; p=0.28). Thirty-three (65%) patients passed their stone, while 18 (35%) did not. The mean LD for passed stones vs. failed passage was 4.1±1.7 mm vs. 6.2±1.8 mm (p=0.0002); the mean EF volume was 0.028±0.035 cm3 vs. 0.093±0.066 cm3 (p=0.00007); and the mean volume by RG was 0.028±0.027cm3 vs. 0.088±0.063 cm3 (p=0.00005).
 Conclusions: The clinical utility of using SV estimated by software algorithm as a predictor for success of MET has not previously been examined. We demonstrate that spontaneously passed stones had a significantly smaller volume than those requiring intervention. Further prospective studies are needed to validate these findings and establish volume thresholds for probability of stone passage.

Imaging in stone diagnosis and surgical planning

“…Stone volume has been used to predict stone behavior with some success according to recent investigations. Three studies in the past 2 years have evaluated machine learning models to predict spontaneous ureteral stone passage based on volume, with areas under the curve of 0.83–0.85 [91,93,94 ▪ ]. This may also be applied to predict surgical efficacy.…”

“…There is thus far no optimal manual calculation for stone volume [79][80][81][82][83]. Automated assessment of stone volume is more accurate and reproducible than manual measurements [79,81,[83][84][85][86][87][88][89][90][91][92].…”

Alpha-Blocker Prescribing Trends for Ureteral Stones: A Single-Centre Study

“…Stone disease is responsible for a significant impact on quality of life and presents a significant economic burden to health-care systems. 1,2 Although several patient and stone factors influence the decision between management strategies, [3][4][5] the choice and success of management plans is influenced by the use of adjunctive therapies such as alpha-blockers. 6 The efficacy for alpha-blockers in ureteral stones has been extensively reported on and debated in recent years.…”

Does volume matter? Incorporating estimated stone volume in a nomogram to predict ureteral stone passage

“…8 Effectively, using 3D reconstruction software, stone volume could be estimated from CT scans. 9 Nevertheless, these software are not readily available in all medical centers. 10,11 Alternatively, the European Association of Urology suggests that a stone's length, width (axial diameter), and depth could be plugged into an ellipsoid formula to estimate the stone's volume.…”