Introduction: CT is the gold standard for visualizing renal and ureteral calculi. CT three-dimensional reformatting allows for automatic, accurate, and reliable measurement of stone size, volume, density, and location. In this study, we aimed to develop and test a software platform capable of calculating a battery of clinically important urinary stone parameters at the point-of-care (POC).
Methods: The syngo Calcium Scoring (Siemens Corporation) algorithm was modified to identify calcium-based stones using an attenuation threshold (250 HU) within a region of interest. Information automatically obtained after reconstruction included voxel sum and calculated volume, maximum diameter, largest diameter in the x, y, and z planes, cumulative diameter, distribution of attenuation in HU, and position relative to the skin for calculation of the skin-to-stone distance (SSD). This algorithm was packaged into a stand-alone application (MATLAB 9.1). From April 2017 to May 2017, all patients undergoing a noncontrast CT of the abdomen or the abdomen and pelvis at the Johns Hopkins Hospital were eligible for inclusion in this validation cohort.
Results: A total of 55 index renal stones were included. The mean volume calculated by voxel sum was 216.53 mm3 (standard deviation [SD] ±616.19, range 1.50-4060.13). The mean volume calculated using the Ackermann's formula and for a sphere was 232.96 mm3 (SD ± 702.65, range 1.24-4074.04) and 1214.63 mm3 (SD ± 4233.41, range 1.77-25,246.40), respectively. The mean largest diameter in any one direction was 6.95 mm (SD ± 7.31, range 1.50-36.40). The maximum density of the stones ranged from 164 to 1725 HU. The mean SSD at the shortest possible point was 14.19 cm (SD ± 6.13, range 6.67-31.28).
Conclusions: We developed a stand-alone platform with a simple easy-to-use interface, which will allow any user the ability to calculate a battery of clinically important urinary stone parameters from CT imaging at the POC. This program is now freely available online.
Keywords: computer-assisted; image processing; kidney calculi; multidetector CT.