Single-energy CT predicts uric acid stones with accuracy comparable to dual-energy CT—prospective validation of a quantitative method

Abstract

Objectives To prospectively validate three quantitative single-energy CT (SE-CT) methods for classifying uric acid (UA) and non-uric acid (non-UA) stones. Methods Between September 2018 and September 2019, 116 study participants were prospectively included in the study if they had at least one 3–20-mm urinary stone on an initial urinary tract SE-CT scan. An additional dual-energy CT (DE-CT) scan was performed, limited to the stone of interest. Additionally, to include a sufficient number of UA stones, eight participants with confirmed UA stone on DE-CT were retrospectively included. The SE-CT stone features used in the prediction models were (1) maximum attenuation (maxHU) and (2) the peak point Laplacian (ppLapl) calculated at the position in the stone with maxHU. Two prediction models were previously published methods (ppLapl-maxHU and maxHU) and the third was derived from the previous results based on the k-nearest neighbors (kNN) algorithm (kNN-ppLapl-maxHU). The three methods were evaluated on this new independent stone dataset. The reference standard was the CT vendor’s DE-CT application for kidney stones. Results Altogether 124 participants (59 ± 14 years, 91 men) with 106 non-UA and 37 UA stones were evaluated. For classification of UA and non-UA stones, the sensitivity, specificity, and accuracy were 100% (37/37), 97% (103/106), and 98% (140/143), respectively, for kNN-ppLapl-maxHU; 95% (35/37), 98% (104/106), and 97% (139/143) for ppLapl-maxHU; and 92% (34/37), 94% (100/106), and 94% (134/143) for maxHU. Conclusion A quantitative SE-CT method (kNN-ppLapl-maxHU) can classify UA stones with accuracy comparable to DE-CT. Key Points • Single-energy CT is the first-line diagnostic tool for suspected renal colic. • A single-energy CT method based on the internal urinary stone attenuation distribution can classify urinary stones into uric acid and non-uric acid stones with high accuracy. • This immensely increases the availability of in vivo stone analysis.

Advancements in Uric Acid Stone Detection: Integrating Deep Learning with CT Imaging and Clinical Assessments in the Upper Urinary Tract

“…The recognition model in this study does not require a large amount of computing power, and the deployed hardware cost is not high; the data used for identification can also be obtained without cost in most hospitals with well-established information systems. Moreover, our model exhibited superior accuracy in predicting uric acid stones compared to various CT scans [19, 20], providing a fast, reliable, and low-cost alternative method for identifying pure uric acid kidney stones.…”

Dual-energy CT revisited: a focused review of clinical use cases

“…Another, and one of the earliest use cases of DECT in renal imaging is the characterization of renal calculi. Significant evidence is available that indicates that identification of uric acid concrements is possible by means of material-specific maps for uric acid [71][72][73][74]. Furthermore, several groups investigated whether stone composition analysis is possible beyond this.…”

Imaging in stone diagnosis and surgical planning

“…Dual-energy CT is superior to conventional CT in [124,126] differentiating stone composition, though its utility may be limited by very small stones or large patients [120]. However, quantitative models incorporating max HUD and peak point Laplacian-maximum attenuation algorithm may be applied to single energy CT to be comparable in accuracy to dual-energy CT [127].…”