Abstract
Objectives: To investigate the value of radiomics features of epicardial adipose tissue (EAT) combined with lung for detecting the severity of Coronavirus Disease 2019 (COVID-19) infection. Methods: The retrospective study included data from 515 COVID-19 patients (Cohort1: 415, cohort2: 100) from the two centers between January 2020 and July 2020. A deep learning method was developed to extract the myocardium and visceral pericardium from chest CTs, and then a threshold was applied for automatic EAT extraction. Lung segmentation was achieved according to a published method. Radiomics features of both EAT and lung were extracted for the severity prediction. In a derivation cohort (290, cohort1), univariate analysis and Pearson correlation analysis were used to identify predictors of the severity of COVID-19. A generalized linear regression model for detecting the severity of COVID-19 was built in a derivation cohort and evaluated in internal (125, cohort1) and external (100, cohort2) validation cohorts. Results: For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (0.011) and 0.968 (0.005), respectively. For severity detection, the AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) of the model with radiomics features of both lung and EAT increased by 0.09 (p<0.001), 22.4%, and 17.0%, respectively, compared with the model with lung radiomics features, in the internal validation cohort. The AUC, NRI, and IDI increased by 0.04 (p<0.001), 11.1%, and 8.0%, respectively, in the external validation cohort. Conclusion: Radiomics features of EAT combined with lung have incremental value in detecting the severity of COVID-19.
Abstract (translated)
目标:研究肺外膜脂肪组织(EAT)与肺部的射频图像特征值在检测新冠病毒2019感染严重程度方面的价值。方法:回顾性研究包括2020年1月至7月的两个中心515名COVID-19患者的数据。采用深度学习方法从胸片中提取心室内膜和肺内联膜,然后应用阈值自动提取EAT。肺部分割按公开方法实现。将EAT和肺部的射频图像特征提取出来用于严重程度预测。在推导群体(290人,群体1)中采用单因素分析和Pearson相关分析确定COVID-19严重程度预测的影响因素。在推导群体中建立了检测COVID-19严重程度的通用线性回归模型,并在内部验证群体(125人,群体1)和外部验证群体(100人,群体2)中评估。结果:对于EAT提取,两个中心的平均Dice相似性系数(DSC)分别为0.972(0.011)和0.968(0.005),分别对应于严重程度预测的AUC为0.988(0.002), NRI为22.4%和IDI为17.0%。与具有肺部射频图像特征模型相比,在内部验证群体中的AUC、 NRI和IDI分别增加0.09(p<0.001)、11.1%和8.0%,而在外部验证群体中的AUC、 NRI和IDI分别增加0.04(p<0.001)、11.1%和8.0%。结论:肺外膜脂肪组织与肺部的射频图像特征值在检测COVID-19严重程度方面具有增量价值。
URL
https://arxiv.org/abs/2301.12340
