A combined non-enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub-centimeter pulmonary solid nodules

作者全名："Lin, Rui-Yu; Zheng, Yi-Neng; Lv, Fa-Jin; Fu, Bin-Jie; Li, Wang-Jia; Liang, Zhang-Rui; Chu, Zhi-Gang"

作者地址："[Lin, Rui-Yu; Zheng, Yi-Neng; Lv, Fa-Jin; Fu, Bin-Jie; Li, Wang-Jia; Liang, Zhang-Rui; Chu, Zhi-Gang] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Youyi Rd, Chongqing, Peoples R China"

通信作者："Chu, ZG (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Youyi Rd, Chongqing, Peoples R China."

来源：MEDICAL PHYSICS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000942338300001

JCR分区：Q1

影响因子：3.2

年份：2023

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：computed tomography; radiomics; solid nodules; sub-centimeter

摘要："BackgroundRadiomics has been used to predict pulmonary nodule (PN) malignancy. However, most of the studies focused on pulmonary ground-glass nodules. The use of computed tomography (CT) radiomics in pulmonary solid nodules, particularly sub-centimeter solid nodules, is rare. PurposeThis study aims to develop a radiomics model based on non-enhanced CT images that can distinguish between benign and malignant sub-centimeter pulmonary solid nodules (SPSNs, <1 cm). MethodsThe clinical and CT data of 180 SPSNs confirmed by pathology were analyzed retrospectively. All SPSNs were divided into two groups: training set (n = 144) and testing set (n = 36). From non-enhanced chest CT images, over 1000 radiomics features were extracted. Radiomics feature selection was performed using the analysis of variance and principal component analysis. The selected radiomics features were fed into a support vector machine (SVM) to develop a radiomics model. The clinical and CT characteristics were used to develop a clinical model. Associating non-enhanced CT radiomics features with clinical factors were used to develop a combined model using SVM. The performance was evaluated using the area under the receiver-operating characteristic curve (AUC). ResultsThe radiomics model performed well in distinguishing between benign and malignant SPSNs, with an AUC of 0.913 (95% confidence interval [CI], 0.862-0.954) in the training set and an AUC of 0.877 (95% CI, 0.817-0.924) in the testing set. The combined model outperformed the clinical and radiomics models with an AUC of 0.940 (95% CI, 0.906-0.969) in the training set and an AUC of 0.903 (95% CI, 0.857-0.944) in the testing set. ConclusionsRadiomics features based on non-enhanced CT images can be used to differentiate SPSNs. The combined model, which included radiomics and clinical factors, had the best discrimination power between benign and malignant SPSNs."

基金机构：Joint Project of Chongqing Science and Technology Commission; Chongqing Public Health Commission [2022MSXM050]; Joint Medical Key Project of Chongqing Science and Health [2022ZDXM006]; Key project of Technological Innovation and Application Development of Chongqing Science and Technology Bureau; National Natural Science Foundation of China [CSTC2021jscx-gksb-N0030]; [81601545]

基金资助正文："Joint Project of Chongqing Science and Technology Commission and Chongqing Public Health Commission, Grant/Award Number: 2022MSXM050; Joint Medical Key Project of Chongqing Science and Health, Grant/Award Number: 2022ZDXM006; Key project of Technological Innovation and Application Development of Chongqing Science and Technology Bureau, Grant/Award Number: CSTC2021jscx-gksb-N0030; National Natural Science Foundation of China, Grant/Award Number: 81601545"