Radiomics derived from T2-FLAIR: the value of 2- and 3-classification tasks for different lesions in multiple sclerosis

作者全名："Shi, Zhuowei; Ma, Yuqi; Ding, Shuang; Yan, Zichun; Zhu, Qiyuan; Xiong, Hailing; Li, Chuan; Xu, Yuhui; Tan, Zeyun; Yin, Feiyue; Chen, Shanxiong; Li, Yongmei"

作者地址："[Shi, Zhuowei; Yan, Zichun; Zhu, Qiyuan; Xu, Yuhui; Tan, Zeyun; Yin, Feiyue; Li, Yongmei] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Youyi Rd, Chongqing 40016, Peoples R China; [Ma, Yuqi; Chen, Shanxiong] Southwest Univ, Coll Comp & Informat Sci, 2 Tiansheng Rd, Chongqing 400715, Peoples R China; [Ding, Shuang] Chongqing Med Univ, Childrens Hosp, Natl Clin Res Ctr Child Hlth & Disorders, Chongqing Key Lab Pediat,Minist Educ,Key Lab Child, Chongqing, Peoples R China; [Xiong, Hailing] Southwest Univ, Coll Elect & Informat Engn, Chongqing, Peoples R China; [Li, Chuan] Southwest Univ, Coll Comp & Informat Sci, Chongqing, Peoples R China; [Li, Chuan] Chongqing Coll Int Business & Econ, Big Data & Intelligence Engn Sch, Chongqing, Peoples R China"

通信作者："Li, YM (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Youyi Rd, Chongqing 40016, Peoples R China.; Chen, SX (通讯作者)，Southwest Univ, Coll Comp & Informat Sci, 2 Tiansheng Rd, Chongqing 400715, Peoples R China."

来源：QUANTITATIVE IMAGING IN MEDICINE AND SURGERY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001194966800027

JCR分区：Q2

影响因子：2.9

年份：2024

卷号：14

期号：2

开始页：　

结束页：　

文献类型：Article

关键词：Magnetic resonance imaging (MRI); multiple sclerosis (MS); radiomics

摘要："Background: White matter (WM) lesions can be classified into contrast enhancement lesions (CELs), iron rim lesions (IRLs), and non -iron rim lesions (NIRLs) based on different pathological mechanism in relapsing -remitting multiple sclerosis (RRMS). The application of radiomics established by T2 -FLAIR to classify WM lesions in RRMS is limited, especially for 3 -class classification among CELs, IRLs, and NIRLs. Methods: A total of 875 WM lesions (92 CELs, 367 IRLs, 416 NIRLs) were included in this study. The 2 -class classification was only performed between IRLs and NIRLs. For the 2- and 3 -class classification tasks, all the lesions were randomly divided into training and testing sets with a ratio of 8:2. We used least absolute shrinkage and selection operator (LASSO), reliefF algorithm, and mutual information (MI) for feature selection, then eXtreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) were used to establish discrimination models. Finally, the area under the curve (AUC), accuracy, sensitivity, specificity, and precision were used to evaluate the performance of the models. Results: For the 2 -class classification model, LASSO classifier with RF model showed the best discrimination performance with the AUC of 0.893 (95% CI: 0.838-0.942), accuracy of 0.813, sensitivity of 0.833, specificity of 0.781, and precision of 0.851. However, the 3 -class classification model of LASSO with XGBoost displayed the highest performance with the AUC of 0.920 (95% CI: 0.887-0.950), accuracy of 0.796, sensitivity of 0.839, specificity of 0.881, and precision of 0.846. Conclusions: Radiomics models based on T2 -FLAIR images have the potential for discriminating among CELs, IRLs, and NIRLs in RRMS."

基金机构：Key Project of Technological Innovation and Application Development of Chongqing Science and Technology Bureau [CSTC2021 jscx-gksb-N0008]; Chongqing Medical Scientific Research Project (Joint project of Chongqing Health Commission and Science and Technology Bureau) [2023ZDXM006]; Fundamental Research Funds for the Central Universities of China [SWU2009107]

基金资助正文："This study was supported by the Key Project of Technological Innovation and Application Development of Chongqing Science and Technology Bureau (CSTC2021 jscx-gksb-N0008) , Chongqing Medical Scientific Research Project (Joint project of Chongqing Health Commission and Science and Technology Bureau) (2023ZDXM006) , and Fundamental Research Funds for the Central Universities of China (SWU2009107) ."