Department of Nuclear Medicine, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, 130 Dongdeok-ro, Jung-gu, Daegu 41944, Korea (the Republic of).
Department of Urology, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Korea (the Republic of).
Comput Methods Programs Biomed. 2023 Oct;240:107691. doi: 10.1016/j.cmpb.2023.107691. Epub 2023 Jun 25.
Urinary stones are common urological diseases with increasing prevalence and incidence worldwide. Among the various types of stones, uric acid stones can be dissolved by oral chemolysis without any surgical procedure. Therefore, our study demonstrates that variant coefficient of stone density measured by thresholding a three-dimensional segmentation-based method from noncontrast computed tomography images can be used to identify pure uric acid stones from non-pure uric acid stones. This study provides a preoperative pure uric acid stone prediction model that could reduce invasive procedural treatments. The pure uric acid stone prediction model may offer optimized clinical decision-making for patients with urinary stones.
While most urinary stones are managed with interventional therapy, uric acid (UA) stones can be dissolved by oral chemolysis without invasive procedures. This study aimed to develop and validate a pure UA (pUA) stone prediction model using a variant coefficient of stone density (VCSD) measured by thresholding a three-dimensional (3D) segmentation-based method.
Patients with urolithiasis treated at Keimyung University Dongsan Hospital between January 2017 and December 2020 were divided into training and internal validation sets, and patients from Kyungpook National University Hospital between January 2017 and December 2018 were used as an external validation set. Each stone was segmented by a thresholding 3D segmentation-based method using an attenuation threshold of 130 Hounsfield units. VCSD was calculated as the stone heterogeneity index divided by the mean stone density.
A total of 1175 urinary stone cases in 1023 patients were enrolled in this study. Of these, 224 (19.1%) were pUA stone cases. Among the potential predictors, thresholding 3D segmentation-based VCSD, age, sex, radio-opacity, hypertension, diabetes, and urine pH were identified as independent pUA stone predictors, and VCSD was the most powerful indicator. The pUA stone prediction model showed good discrimination, yielding area under the receiver operating characteristic curve of 0.960 (95% confidence interval (CI): 0.940-0.979, P < 0.001), 0.931 (95% CI: 0.875-0.987, P < 0.001), and 0.938 (95% CI: 0.912-0.965, P < 0.001) in the training, internal validation, and external validation sets, respectively.
VCSD measured using 3D segmentation was a decisive independent predictive factor for pUA stones. Furthermore, the established prediction model with VCSD can serve as a noninvasive preoperative tool to identify pUA stones.
本研究旨在通过阈值分割三维（3D）分割法来测量结石密度变异系数（VCSD），建立并验证一个纯尿酸（pUA）结石预测模型。
2017 年 1 月至 2020 年 12 月在全州大学医院治疗的尿石症患者被分为训练集和内部验证集，2017 年 1 月至 2018 年 12 月在启明大学东山医院治疗的患者被用于外部验证集。采用 130Hu 衰减阈值的阈值分割 3D 分割法对每个结石进行分割。VCSD 计算为结石异质性指数除以平均结石密度。
本研究共纳入 1023 例患者的 1175 例尿石症病例。其中，224 例（19.1%）为 pUA 结石病例。在潜在预测因素中，阈值分割 3D 分割法 VCSD、年龄、性别、射线不透性、高血压、糖尿病和尿液 pH 值被确定为独立的 pUA 结石预测因素，其中 VCSD 是最有力的指标。pUA 结石预测模型具有良好的判别能力，在训练集、内部验证集和外部验证集中的受试者工作特征曲线下面积分别为 0.960（95%置信区间：0.940-0.979，P<0.001）、0.931（95%置信区间：0.875-0.987，P<0.001）和 0.938（95%置信区间：0.912-0.965，P<0.001）。
3D 分割法测量的 VCSD 是 pUA 结石的决定性独立预测因素。此外，基于 VCSD 建立的预测模型可作为一种非侵入性的术前工具，用于识别 pUA 结石。
