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Epicardial adipose tissue (EAT) is known for its pro-inflammatory propertiesand association with Coronavirus Disease 2019 (COVID-19) severity. However,current EAT segmentation methods do not consider positional information.Additionally, the detection of COVID-19 severity lacks consideration for EATradiomics features, which limits interpretability. This study investigates theuse of radiomics features from EAT and lungs to detect the severity of COVID-19infections. A retrospective analysis of 515 patients with COVID-19 (Cohort1:415, Cohort2: 100) was conducted using a proposed three-stage deep learningapproach for EAT extraction. Lung segmentation was achieved using a publishedmethod. A hybrid model for detecting the severity of COVID-19 was built in aderivation cohort, and its performance and uncertainty were evaluated ininternal (125, Cohort1) and external (100, Cohort2) validation cohorts. For EATextraction, the Dice similarity coefficients (DSC) of the two centers were0.972 (+-0.011) and 0.968 (+-0.005), respectively. For severity detection, thehybrid model with radiomics features of both lungs and EAT showed improvementsin AUC, net reclassification improvement (NRI), and integrated discriminationimprovement (IDI) compared to the model with only lung radiomics features. Thehybrid model exhibited an increase of 0.1 (p<0.001), 19.3%, and 18.0%respectively, in the internal validation cohort and an increase of 0.09(p<0.001), 18.0%, and 18.0%, respectively, in the external validation cohortwhile outperforming existing detection methods. Uncertainty quantification andradiomics features analysis confirmed the interpretability of case predictionafter inclusion of EAT features.

Incremental Value and Interpretability of Radiomics Features of Both  Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19  Infection