AUTHOR=He Wenzhang , Xia Chunchao , Chen Xiaoyi , Yu Jianqun , Liu Jing , Pu Huaxia , Li Xue , Liu Shengmei , Chen Xinyue , Peng Liqing 

TITLE=Computed Tomography-Based Radiomics for Differentiation of Thymic Epithelial Tumors and Lymphomas in Anterior Mediastinum

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.869982

DOI=10.3389/fonc.2022.869982

ISSN=2234-943X

ABSTRACT=Objective: To investigate the differential diagnostic performance of computed tomography (CT)-based radiomics in thymic epithelial tumors (TETs) and lymphomas in anterior mediastinum. 
Methods: One hundred and forty-nine patients with TETs and 93 patients with lymphomas were enrolled. These patients were assigned to a training set (n = 171) and an external validation set (n = 71). Dedicated radiomics prototype software was used to segment lesions on preoperative chest enhanced CT images and extract features. The multivariable logistic regression algorithm was used to construct three models according to clinico-radiologic features, radiomics features and combined features, respectively. Performance of the three models were compared by using the area under of the receiver operating characteristic curves (AUCs). Decision curve analysis was used to evaluate clinical utility of the three models.
Results: For clinico-radiologic model, radiomics signature model and combined model, the AUCs were 0.860, 0.965, 0.975 and 0.843, 0.961, 0.955 in training cohort and test cohort, respectively (all p<0.05). The accuracies of each model were 83.6, 89.5, 91.8 and 84.5, 90.1, 85.9 in the two cohorts, respectively (all p<0.05). Compared with clinico-radiologic model, better diagnostic performances were found in radiomics signature model and combined model. 
Conclusions: Radiomics signature model and combined model exhibit outstanding and comparable differential diagnostic performances between TETs and lymphomas. The CT-based radiomics analysis might serve as an effective tool for accurate differentiating TETs from lymphomas before treatment.