Department of Urology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, Jiangsu Province, China.
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, Jiangsu Province, China.
World J Urol. 2024 Jul 10;42(1):397. doi: 10.1007/s00345-024-05111-0.
This study aims to investigate the predictive value of CT-based radiomics in determining the success of extracorporeal shock wave lithotripsy (SWL) treatment for ureteral stones larger than 10mm in adult patients.
A total of 301 eligible patients (165/136 successful/unsuccessful) who underwent SWL were retrospectively evaluated and divided into a training cohort (n = 241) and a test cohort (n = 60) following an 8:2 ratio. Univariate analysis was performed to assess clinical characteristics for constructing a nomogram. Radiomics and conventional radiological characteristics of stones were evaluated. Following feature selection, radiomics and radiological models were constructed using logistic regression (LR), support vector machine (SVM), random forest (RF), K nearest neighbor (KNN), and XGBoost. The models' performance was compared using metrics such as the area under the receiver operating characteristic curve (AUC), precision, recall, accuracy, and F1 score. Finally, a nomogram was created incorporating the best image model signature and clinical predictors.
The SVM-based radiomics model showed superior predictive performance in both training and test cohorts (AUC: 0.956, 0.891, respectively). The nomogram, which combined SVM-based radiomics signature with proximal ureter diameter (PUD), demonstrated further improved predictive performance in the test cohort (AUC: 0.891 vs. 0.939, P = 0.166).
Integration of CT-derived radiomics and PUD showed excellent ability to predict SWL treatment success in patients with ureteral stones larger than 10mm, providing a promising approach for clinical decision-making.
本研究旨在探讨基于 CT 的放射组学在预测成人大于 10mm 输尿管结石体外冲击波碎石（SWL）治疗成功率方面的价值。
回顾性评估了 301 名符合条件的患者（165/136 例成功/失败），他们接受了 SWL 治疗，并按照 8:2 的比例分为训练队列（n=241）和测试队列（n=60）。进行单因素分析以评估构建列线图的临床特征。评估结石的放射组学和常规放射学特征。在进行特征选择后，使用逻辑回归（LR）、支持向量机（SVM）、随机森林（RF）、K 最近邻（KNN）和 XGBoost 构建放射组学和放射学模型。使用受试者工作特征曲线（AUC）下面积、精度、召回率、准确性和 F1 评分等指标比较模型的性能。最后，创建了一个包含最佳图像模型特征和临床预测因子的列线图。
基于 SVM 的放射组学模型在训练组和测试组中均表现出优异的预测性能（AUC：0.956、0.891）。将 SVM 基放射组学特征与输尿管近端直径（PUD）相结合的列线图在测试组中表现出进一步提高的预测性能（AUC：0.891 与 0.939，P=0.166）。
CT 衍生的放射组学和 PUD 的整合显示出在预测大于 10mm 的输尿管结石 SWL 治疗成功率方面具有优异的能力，为临床决策提供了一种有前途的方法。
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