Lu Jianjuan, Zhu Kun, Yang Ning, Chen Qiang, Liu Lingrui, Liu Yanyan, Yang Yi, Li Jiabin
Department of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Open Forum Infect Dis. 2024 Oct 5;11(10):ofae581. doi: 10.1093/ofid/ofae581. eCollection 2024 Oct.
This study investigated the abilities of radiomics and clinical feature models to distinguish kidney stone-associated urinary tract infections (KS-UTIs) using computed tomography.
A retrospective analysis was conducted on a single-center dataset comprising computed tomography (CT) scans and corresponding clinical information from 461 patients with kidney stones. Radiomics features were extracted from CT images and underwent dimensionality reduction and selection. Multiple machine learning (Three types of shallow learning and four types of deep learning) algorithms were employed to construct radiomics and clinical models in this study. Performance evaluation and optimal model selection were done using receiver operating characteristic (ROC) curve analysis and Delong test. Univariate and multivariate logistic regression analyzed clinical and radiomics features to identify significant variables and develop a clinical model. A combined model integrating radiomics and clinical features was established. Model performance was assessed by ROC curve analysis, clinical utility was evaluated through decision curve analysis, and the accuracy of the model was analyzed via calibration curve.
Multilayer perceptron (MLP) showed higher classification accuracy than other classifiers (area under the curve (AUC) for radiomics model: train 0.96, test 0.94; AUC for clinical model: train 0.95, test 0.91. The combined radiomics-clinical model performed best (AUC for combined model: train 0.98, test 0.95). Decision curve and calibration curve analyses confirmed the model's clinical efficacy and calibration.
This study showed the effectiveness of combining radiomics and clinical features from CT scans to identify KS-UTIs. A combined model using MLP exhibited strong classification abilities.
本研究利用计算机断层扫描技术，调查了影像组学和临床特征模型区分肾结石相关性尿路感染（KS-UTIs）的能力。
对一个单中心数据集进行回顾性分析，该数据集包含461例肾结石患者的计算机断层扫描（CT）图像及相应临床信息。从CT图像中提取影像组学特征，并进行降维和特征选择。本研究采用多种机器学习算法（三种浅层学习算法和四种深度学习算法）构建影像组学和临床模型。使用受试者工作特征（ROC）曲线分析和德龙检验进行性能评估和最优模型选择。采用单因素和多因素逻辑回归分析临床和影像组学特征，以识别显著变量并建立临床模型。建立了整合影像组学和临床特征的联合模型。通过ROC曲线分析评估模型性能，通过决策曲线分析评估临床实用性，并通过校准曲线分析模型的准确性。
多层感知器（MLP）显示出比其他分类器更高的分类准确率（影像组学模型的曲线下面积（AUC）：训练集为0.96，测试集为0.94；临床模型的AUC：训练集为0.95，测试集为0.91。影像组学-临床联合模型表现最佳（联合模型的AUC：训练集为0.98，测试集为0.95）。决策曲线和校准曲线分析证实了该模型的临床疗效和校准情况。
本研究表明，结合CT扫描的影像组学和临床特征来识别KS-UTIs是有效的。使用MLP的联合模型表现出强大的分类能力。
