Wang Zijie, Yang Guangjie, Wang Xinning, Cao Yuanchao, Jiao Wei, Niu Haitao
Department of Urology, The Affiliated Hospital of Qingdao University, 16th Jiangsu Road, Qingdao, 266012, China.
PET-CT Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Urolithiasis. 2023 Feb 6;51(1):37. doi: 10.1007/s00240-023-01405-x.
The aim of this study was to develop a CT-based radiomics and clinical variable diagnostic model for the preoperative prediction of uric acid calculi. In this retrospective study, 370 patients with urolithiasis who underwent preoperative urinary CT scans were enrolled. The CT images of each patient were manually segmented, and radiomics features were extracted. Sixteen radiomics features were selected by one-way analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO). Logistic regression (LR), random forest (RF) and support vector machine (SVM) were used to model the selected features, and the model with the best performance was selected. Multivariate logistic regression was used to screen out significant clinical variables, and the radiomics features and clinical variables were combined to construct a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), etc., were used to evaluate the diagnostic performance of the model. Among the three machine learning models, the LR model had the best performance and good robustness of the dataset. Therefore, the LR model was used to construct the nomogram. The AUCs of the nomogram model in the training set and validation set were 0.878 and 0.867, respectively, which were significantly higher than those of the radiomics model and the clinical feature model. The CT-based radiomics model based has good performance in distinguishing uric acid stones from nonuric acid stones, and the nomogram model has the best diagnostic performance among the three models. This model can provide an effective reference for clinical decision-making.
本研究的目的是开发一种基于CT的放射组学和临床变量诊断模型，用于术前预测尿酸结石。在这项回顾性研究中，纳入了370例接受术前泌尿系统CT扫描的尿石症患者。对每位患者的CT图像进行手动分割，并提取放射组学特征。通过单因素方差分析（ANOVA）和最小绝对收缩和选择算子（LASSO）选择了16个放射组学特征。使用逻辑回归（LR）、随机森林（RF）和支持向量机（SVM）对所选特征进行建模，并选择性能最佳的模型。使用多因素逻辑回归筛选出显著的临床变量，并将放射组学特征与临床变量相结合构建列线图模型。采用受试者操作特征（ROC）曲线下面积（AUC）等评估模型的诊断性能。在三种机器学习模型中，LR模型性能最佳，且数据集具有良好的稳健性。因此，使用LR模型构建列线图。列线图模型在训练集和验证集的AUC分别为0.878和0.867，显著高于放射组学模型和临床特征模型。基于CT的放射组学模型在区分尿酸结石和非尿酸结石方面具有良好性能，列线图模型在三种模型中诊断性能最佳。该模型可为临床决策提供有效参考。
