Identifying immunodeficiency status in children with pulmonary tuberculosis: using radiomics approach based on un-enhanced chest computed tomography

作者全名："Ding, Hao; Chen, Xin; Wang, Haoru; Zhang, Li; Wang, Fang; He, Ling"

作者地址："[Ding, Hao; Chen, Xin; Wang, Haoru; Zhang, Li; He, Ling] Chongqing Med Univ, Dept Radiol, Childrens Hosp, Chongqing, Peoples R China; [Ding, Hao; Chen, Xin; Wang, Haoru; Zhang, Li; He, Ling] Minist Educ, Natl Clin Res Ctr Child Hlth & Disorders, Key Lab Child Dev & Disorders, Chongqing Key Lab Pediat, Chongqing, Peoples R China; [Wang, Fang] Shanghai United Imaging Intelligence Co Ltd, Dept Res & Dev, Shanghai, Peoples R China; [He, Ling] Chongqing Med Univ, Dept Radiol, Childrens Hosp, 136 Zhongshan Rd 2, Chongqing 400014, Peoples R China"

通信作者："He, L (通讯作者)，Chongqing Med Univ, Dept Radiol, Childrens Hosp, 136 Zhongshan Rd 2, Chongqing 400014, Peoples R China."

来源：TRANSLATIONAL PEDIATRICS

ESI学科分类：　

WOS号：WOS:001134922400015

JCR分区：Q2

影响因子：1.5

年份：2023

卷号：12

期号：12

开始页：2191

结束页：2202

文献类型：Article

关键词：Radiomics; pulmonary tuberculosis (PTB); primary immunodeficiency diseases (PIDs); children; differential diagnosis

摘要："Background: Children with primary immunodeficiency diseases (PIDs) are particularly vulnerable to infection of Mycobacterium tuberculosis (Mtb). Chest computed tomography (CT) is an important examination diagnosing pulmonary tuberculosis (PTB), and there are some differences between primary immunocompromised and immunocompetent cases with PTB. Therefore, this study aimed to use the radiomics analysis based on un-enhanced CT for identifying immunodeficiency status in children with PTB.Methods: This retrospective study enrolled a total of 173 patients with diagnosis of PTB and available immunodeficiency status. Based on their immunodeficiency status, the patients were divided into PIDs (n=72) and no-PIDs (n=101). The samplings were randomly divided into training and testing groups according to a ratio of 3:1. Regions of interest were obtained by segmenting lung lesions on un-enhanced CT images to extract radiomics features. The optimal radiomics features were identified after dimensionality reduction in the training group, and a logistic regression algorithm was used to establish radiomics model. The model was validated in the training and testing groups. Diagnostic efficiency of the model was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, calibration curve, and decision curve.Results: The radiomics model was constructed using nine optimal features. In the training set, the model achieved an AUC of 0.837, sensitivity of 0.783, specificity of 0.780, and F1 score of 0.749. The crossvalidation of the model in the training set showed an AUC of 0.774, sensitivity of 0.834, specificity of 0.720, and F1 score of 0.749. In the test set, the model achieved an AUC of 0.746, sensitivity of 0.722, specificity of 0.692, and F1 score of 0.823. Calibration curves indicated a strong predictive performance by the model, and decision curve analysis demonstrated its clinical utility.Conclusions: The CT-based radiomics model demonstrates good discriminative efficacy in identifying the presence of PIDs in children with PTB, and shows promise in accurately identifying the immunodeficiency status in this population."

基金机构："Chongqing Medical University Intelligent Medicine Program [ZHYX202217]; Basic Research and Frontier Exploration Project (Yuzhong District, Chongqing, China) [20200155]"

基金资助正文："This study was supported by Chongqing Medical University Intelligent Medicine Program (No. ZHYX202217), the Basic Research and Frontier Exploration Project (Yuzhong District, Chongqing, China; No. 20200155)."