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Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection by Yao, Ni; Tian, Yanhui; Neves, Daniel Gama das; Zhao, Chen; Mesquita, Claudio Tinoco; Martins, Wolney de Andrade; Santos, Alair Augusto Sarmet Moreira Damas dos; Li, Yanting; Han, Chuang; Zhu, Fubao; Dai, Neng; Zhou, Weihua is a scholarly article available to read on EtoBox.

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

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 (NR

Author

Yao, Ni; Tian, Yanhui; Neves, Daniel Gama das; Zhao, Chen; Mesquita, Claudio Tinoco; Martins, Wolney de Andrade; Santos, Alair Augusto Sarmet Moreira Damas dos; Li, Yanting; Han, Chuang; Zhu, Fubao; Dai, Neng; Zhou, Weihua

Published

2023

Language

EN