Department of Radiology, Faculty of Medicine and Health, Örebro University, 70185, Örebro, Sweden.
Department of Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
Eur Radiol. 2021 Aug;31(8):5980-5989. doi: 10.1007/s00330-021-07713-3. Epub 2021 Feb 26.
To prospectively validate three quantitative single-energy CT (SE-CT) methods for classifying uric acid (UA) and non-uric acid (non-UA) stones.
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.
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.
A quantitative SE-CT method (kNN-ppLapl-maxHU) can classify UA stones with accuracy comparable to DE-CT.
• 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.
前瞻性验证三种定量单能量 CT（SE-CT）方法用于分类尿酸（UA）和非尿酸（非-UA）结石。
2018 年 9 月至 2019 年 9 月，如果初始尿路 SE-CT 扫描中至少有一个 3-20mm 的尿石，将 116 名研究参与者前瞻性纳入研究。对感兴趣的结石进行额外的双能 CT（DE-CT）扫描。此外，为了包括足够数量的 UA 结石，回顾性纳入了 8 名在 DE-CT 上确诊为 UA 结石的参与者。预测模型中使用的 SE-CT 结石特征为（1）最大衰减（maxHU）和（2）在结石中 maxHU 位置计算的峰值点拉普拉斯（ppLapl）。两种预测模型是以前发表的方法（ppLapl-maxHU 和 maxHU），第三种方法是基于 k-最近邻（kNN）算法的以前结果得出的（kNN-ppLapl-maxHU）。这三种方法在这个新的独立结石数据集上进行了评估。参考标准是 CT 供应商的肾结石 DE-CT 应用。
共评估了 124 名参与者（59±14 岁，91 名男性），其中 106 名非-UA 和 37 名 UA 结石。对于 UA 和非-UA 结石的分类，kNN-ppLapl-maxHU 的灵敏度、特异性和准确率分别为 100%（37/37）、97%（103/106）和 98%（140/143）；ppLapl-maxHU 为 95%（35/37）、98%（104/106）和 97%（139/143）；maxHU 为 92%（34/37）、94%（100/106）和 94%（134/143）。
定量 SE-CT 方法（kNN-ppLapl-maxHU）可准确分类 UA 结石，与 DE-CT 相当。
单能量 CT 是疑似肾绞痛的一线诊断工具。
一种基于内部尿石衰减分布的单能量 CT 方法，可以高精度地将尿石分类为尿酸和非尿酸结石。
这极大地增加了体内结石分析的可用性。
