Abstract: PD11-12
Sources of Funding: None

Introduction

Several options exist for estimating renal and ureteral stone burden on CT, including volume, surface area, and maximum diameter. To date, no specific measure is accepted as the gold standard for use in research or clinical care. This is because calculating all these individual parameters is difficult and time consuming. Therefore, we developed an automated tool for calculating clinically relevant urinary stone parameters on CT.

Methods

An algorithm was developed that identifies stones on CT based on an attenuation threshold within a region of interest (ROI). A threshold of 250 Hounsfield units (HU) was selected to ensure that the stone remains a single object, while eliminating adjacent soft-tissue. For each CT, the images were exported, an ROI was identified by a board-certified radiologist, and the algorithm was applied to this ROI (MATLAB 9.1; Natick, MA) (Figure 1). Stone parameters analyzed included volume, maximum diameter, largest diameter in x, y and z dimensions, cumulative diameter, and HU. Volume was measured by summing all voxels within the stone and this value was correlated (Pearson correlation) to the calculated volume using the formula for a sphere (4/3πr³, where r is the maximum radius).

Results

As a pilot validation study of the algorithm, a total of 10 consecutive patients (11 stones) with a history of nephrolithiasis who underwent a CT from 1/2016-4/2016 were included in this analysis. Table 1 outlines the calculated parameters for each stone. The correlation between measured (voxel sum) and calculated (sphere formula) stone volume was 0.577.

Conclusions

Automated calculation of clinically relevant urinary stone parameters, such as maximum diameter, measured volume, and stone density can easily be obtained and visualized at the point-of-care. Measured and calculated stone volume have a weak correlation, likely due to the variability in stone shape. Future investigations will determine how automated stone measurements can help us to identify which patients will have treatment success.

Funding

None