Abstract
ObjectivesTo prospectively validate three quantitative single-energy CT (SE-CT) methods for classifying uric acid (UA) and non-uric acid (non-UA) stones.MethodsBetween 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.ResultsAltogether 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.ConclusionA 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.
Highlights
Urinary stone disease continues to be an increasing reason for health care admissions worldwide, with an incidence of 7% among women and 11% among men in the USA in 2010 [1]
Single-energy CT is the first-line modality for the detection of urinary stones, whereas the in vivo stone analysis is usually conducted with dual-energy CT, with limited availability in most emergency radiology settings [23]
The k-nearest neighbors (kNN)-peak point Laplacian (ppLapl)-maxHU method obtained a sensitivity for uric acid (UA) stones, 3–20 mm, of 100% (37/37), a specificity of 97% (103/106), and an accuracy of 98% (140/143)
Summary
Urinary stone disease continues to be an increasing reason for health care admissions worldwide, with an incidence of 7% among women and 11% among men in the USA in 2010 [1]. Non-enhanced single-energy CT (SE-CT) is the first-line diagnostic tool for suspected renal colic and is able to detect most urinary stones with high specificity. It is highly reproducible for measuring stone size and useful for predicting spontaneous stone passage [6, 14, 16]. The highest (peak) attenuation (maxHU) of a single voxel in the stone showed to be a powerful predictor of stone composition, but, to increase the specificity, Lidén proposed a purely quantitative SE-CT method called peak point Laplacian/maxHU (ppLapl-maxHU). The purpose of the present study was to prospectively validate two previously published (ppLapl-maxHU and maxHU), and one derived (kNN-ppLapl-maxHU) quantitative single-energy CT methods for classifying uric acid stones on a separate, previously unseen stone dataset
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