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

TL;DR

This study demonstrates that combining radiomics features from both lung and epicardial adipose tissue improves the detection and interpretability of COVID-19 severity, validated across multiple cohorts with high accuracy.

Contribution

It introduces a novel hybrid model incorporating EAT radiomics features for COVID-19 severity detection, enhancing interpretability and performance over lung-only models.

Findings

Hybrid model outperforms lung-only models in AUC, NRI, and IDI.

EAT segmentation achieved high Dice similarity coefficients (~0.97).

Inclusion of EAT features improves interpretability and accuracy.

Abstract

Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and 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 EAT radiomics features, which limits interpretability. This study investigates the use of radiomics features from EAT and lungs to detect the severity of COVID-19 infections. A retrospective analysis of 515 patients with COVID-19 (Cohort1: 415, Cohort2: 100) was conducted using a proposed three-stage deep learning approach for EAT extraction. Lung segmentation was achieved using a published method. A hybrid model for detecting the severity of COVID-19 was built in a derivation cohort, and its performance and uncertainty were evaluated in internal (125, Cohort1) and external (100, Cohort2) validation cohorts. 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 hybrid model with radiomics features of both lungs and EAT showed improvements in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) compared to the model with only lung radiomics features. The hybrid 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 cohort while outperforming existing detection methods. Uncertainty quantification and radiomics features analysis confirmed the interpretability of case prediction after inclusion of EAT features.