Department of Biomedical Sciences, Chonnam National University Medical School, Gwangju, Korea.
Department of Urology, Saint Paul Hospital, Hanoi, Vietnam.
World J Urol. 2024 Mar 13;42(1):150. doi: 10.1007/s00345-024-04818-4.
Oral chemolysis is an effective and non-invasive treatment for uric acid urinary stones. This study aimed to classify urinary stones into either pure uric acid (pUA) or other composition (Others) using non-contrast-enhanced computed tomography scans (NCCTs).
Instances managed at our institution from 2019 to 2021 were screened. They were labeled as either pUA or Others based upon composition analyses, and randomly split into training or testing data set. Several instances contained multiple NCCTs which were all collected. In each of NCCTs, individual urinary stone was treated as individual sample. From manually drawn volumes of interest, we extracted original and wavelet radiomics features for each sample. The most important features were then selected via the Least Absolute Shrinkage and Selection Operator for building the final model on a Support Vector Machine. Performance on the testing set was evaluated via accuracy, sensitivity, specificity, and area under the precision-recall curve (AUPRC).
There were 302 instances, of which 118 had pUA urinary stones, generating 576 samples in total. From 851 original and wavelet radiomics features extracted for each sample, 10 most important features were ultimately selected. On the testing data set, accuracy, sensitivity, specificity, and AUPRC were 93.9%, 97.9%, 92.2%, and 0.958, respectively, for per-sample prediction, and 90.8%, 100%, 87.5%, and 0.902, respectively, for per-instance prediction.
The machine learning algorithm trained with radiomics features from NCCTs can accurately predict pUA urinary stones. Our work suggests a potential assisting tool for stone disease treatment selection.
口服化学溶石术是一种有效且非侵入性的尿酸尿结石治疗方法。本研究旨在通过非增强 CT 扫描（NCCT）将尿结石分为纯尿酸（pUA）或其他成分（Others）。
筛选了我院 2019 年至 2021 年期间治疗的病例。根据成分分析将其标记为 pUA 或 Others，并随机分为训练或测试数据集。一些病例包含多个 NCCT，均进行了采集。在每个 NCCT 中，将单个尿结石视为单个样本。从手动绘制的感兴趣区域中，我们为每个样本提取了原始和小波放射组学特征。然后通过最小绝对收缩和选择算子（LASSO）选择最重要的特征，以便在支持向量机上构建最终模型。通过准确性、敏感性、特异性和精度召回曲线下面积（AUPRC）来评估测试集的性能。
共有 302 例病例，其中 118 例为 pUA 尿结石，共产生 576 个样本。从每个样本提取的 851 个原始和小波放射组学特征中，最终选择了 10 个最重要的特征。在测试数据集上，针对每个样本的预测，准确性、敏感性、特异性和 AUPRC 分别为 93.9%、97.9%、92.2%和 0.958，针对每个病例的预测，准确性、敏感性、特异性和 AUPRC 分别为 90.8%、100%、87.5%和 0.902。
使用 NCCT 放射组学特征训练的机器学习算法可以准确预测 pUA 尿结石。我们的工作为结石病治疗选择提供了一种潜在的辅助工具。
