Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT

Purpose Knowledge of kidney stone composition can help in patient management; urine composition analysis and dual-energy CT are frequently used to assess stone type. We assessed if threshold-based stone segmentation and radiomics can determine the composition of kidney stones from single-energy, non-contrast abdomen-pelvis CT. Methods With IRB approval, we identified 218 consecutive patients (mean age 64 +/- 13 years; male:female 138:80) with the presence of kidney stones on non-contrast, abdomen-pelvis CT and surgical or biochemical proof of their stone composition. CT examinations were performed on one of the seven multidetector-row scanners from four vendors (GE, Philips, Siemens, Toshiba). Deidentified CT images were processed with a radiomics prototype (Frontier, Siemens Healthineers) to segment the entire kidney volumes with an AI-based organ segmentation tool. We applied a threshold of 130 HU to isolate stones in the segmented kidneys and to estimate radiomics over the segmented stone volume. A coinvestigator verified kidney stone segmentation and adjusted the volume of interest to include the entire stone volume when necessary. We applied multiple logistic regression tests with precision recall plots to obtain area under the curve (AUC) using a built-in R statistical program. Results The threshold-based stone segmentation successfully isolated kidney stones (uric acid: n = 102 patients, calcium oxalate/phosphate: n = 116 patients) in all patients. Radiomics differentiated between calcium and uric acid stones with an AUC of 0.78 (p < 0.01, 95% CI 0.73-0.83), 0.79 sensitivity, and 0.90 specificity regardless of CT vendors (GE CT: AUC = 0.82, p < 0.01, 95% CI 0.740-0896; Siemens CT: AUC = 0.77, 95% CI 0.700-0.846, p < 0.01). Conclusion Automated threshold-based stone segmentation and radiomics can differentiate between calcium oxalate/phosphate and urate stones from non-contrast, single-energy abdomen CT.