CT Texture Analysis of Ex Vivo Renal Stones Predicts Ease of Fragmentation with Shockwave Lithotripsy.

INTRODUCTION: Understanding the factors affecting success of extracorporeal shockwave lithotripsy (SWL) would improve informed decision-making on the most appropriate treatment modality for an individual patient. Although stone size and skin-to-stone distance do correlate with fragmentation efficacy, it has been shown that stone composition and architecture, as reflected by structural heterogeneity on CT, are also important factors. This study aims to determine if CT texture analysis (CTTA), a novel, nondestructive, and objective tool that generates statistical metrics reflecting stone heterogeneity, could have utility in predicting likelihood of SWL success.

MATERIALS AND METHODS: Seven spontaneously passed, intact renal tract stones, were scanned ex vivo using standard CT KUB and micro-CT. The stones were then fragmented in vitro using a clinical lithotripter, after which, chemical composition analysis was performed. CTTA was used to generate a number of metrics that were correlated to the number of shocks needed to fragment the stone.

RESULTS: CTTA metrics reflected stone characteristics and composition, and predicted ease of SWL fragmentation. The strongest correlation with number of shocks required to fragment the stone was mean Hounsfield unit (HU) density (r = 0.806, p = 0.028) and a CTTA metric measuring the entropy of the pixel distribution of the stone image (r = 0.804, p = 0.039). Using multiple linear regression analysis, the best model showed that CTTA metrics of entropy and kurtosis could predict 92% of the outcome of number of shocks needed to fragment the stone. This was superior to using stone volume or density.

CONCLUSIONS: CTTA metrics entropy and kurtosis have been shown in this experimental ex vivo setting to strongly predict fragmentation by SWL. This warrants further investigation in a larger clinical study for the contribution of CT textural metrics as a measure of stone heterogeneity, along with other known clinical factors, to predict likelihood of SWL success.