Poster, Podium & Video Sessions
Presentation Authors: Justin Ziemba*, George Fung, Rishab Gurnani, Elliot Fishman, Brian Matlaga, Satomi Kawamoto, Baltimore, MD
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.
Source Of Funding: None
Brady Urological Institute, Johns Hopkins School of Medicine
Justin B. Ziemba, MD is currently a clinical and research fellow in endourology at the James Buchanan Brady Urological Institute of the Johns Hopkins Hospital and Instructor of Urology at the Johns Hopkins School of Medicine. Clinically, he focuses on the surgical treatment and medical management of urinary stone disease. This clinical focus is supported by his research interests in the emergent care of patients with kidney stones. He is also interested in quality improvement and patient safety with a specific focus on how to educate others in the delivery of high-value care. He has served on several hospital committees aimed at improving the care provided to patients at both the Johns Hopkins Hospital and the Hospital of the University of Pennsylvania. In 2015, he was awarded the Quality and Patient Safety Innovator Award from the University of Pennsylvania Health System.
Friday, May 12
2:50 PM – 3:00 PM