The usefulness of the Hounsfield unit and stone heterogeneity variation in predicting the shockwave lithotripsy outcome.

Abstract

PURPOSE This study aimed to evaluate the use of stone density variation coefficient (SDVC) as an indicator of stone heterogeneity and previously reported parameters for predicting extracorporeal shock wave lithotripsy (ESWL) outcome in urinary calculi. Moreover, a new formula that could be used to predict ESWL success was suggested. METHODS A total of 850 patients, who underwent the first session of ESWL for urinary stones between 2015 and 2020, were examined, and 220 eligible patients were included in the study. Stone density variation coefficient and other parameters associated with stone attenuation values and stone size parameters were studied as potential predictors based on noncontrast com- puted tomography (NCCT). Extracorporeal shock wave lithotripsy success was considered after 3 months by radiography or NCCT. Logistic regression analysis was performed to determine the factors contributing to treatment success. RESULTS For the 220 patients, ESWL success rate was 39.5%. The receiver operating characteristic analysis showed that SDVC (AUC=0.82; 95% confidence interval [CI]: 0.76-0.87; P < .001), mean stone density (AUC=0.81; 95% CI:0.75-0.87; P < .001), maximum stone density (AUC=0.70; 95% CI: 0.63-0.78; P < .001), stone volume (AUC=0.70; 95% CI: 0.62-0.77; P < .001), and major diam- eter (AUC=0.67; 95% CI: 0.59-0.74; P < .001) had significant prediction accuracy from high to low. Additionally, SDVC was found to be successful in predicting ESWL success, especially for patients with high mean stone density (OR = 10; 95% CI: 3.55-28.57; P < .001). The logistic regres- sion model, in which the "stone disintegration probability" (SDP) formula was found, correctly predicted ESWL success with a single session by 79.1%. CONCLUSION In conclusion, size and attenuation values were predictors of treatment success, and the best predictor was SDVC. Evaluation of SDP formula prior to ESWL could predict treatment outcomes and facilitate the decisions regarding treatment strategies.