Back to Search Start Over

Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19

Authors :
Axel Bartoli
Joris Fournel
Léa Ait-Yahia
Farah Cadour
Farouk Tradi
Badih Ghattas
Sébastien Cortaredona
Matthieu Million
Adèle Lasbleiz
Anne Dutour
Bénédicte Gaborit
Alexis Jacquier
Source :
Cells, Vol 11, Iss 6, p 1034 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Background: To develop a deep-learning (DL) pipeline that allowed an automated segmentation of epicardial adipose tissue (EAT) from low-dose computed tomography (LDCT) and investigate the link between EAT and COVID-19 clinical outcomes. Methods: This monocentric retrospective study included 353 patients: 95 for training, 20 for testing, and 238 for prognosis evaluation. EAT segmentation was obtained after thresholding on a manually segmented pericardial volume. The model was evaluated with Dice coefficient (DSC), inter-and intraobserver reproducibility, and clinical measures. Uni-and multi-variate analyzes were conducted to assess the prognosis value of the EAT volume, EAT extent, and lung lesion extent on clinical outcomes, including hospitalization, oxygen therapy, intensive care unit admission and death. Results: The mean DSC for EAT volumes was 0.85 ± 0.05. For EAT volume, the mean absolute error was 11.7 ± 8.1 cm3 with a non-significant bias of −4.0 ± 13.9 cm3 and a correlation of 0.963 with the manual measures (p < 0.01). The multivariate model providing the higher AUC to predict adverse outcome include both EAT extent and lung lesion extent (AUC = 0.805). Conclusions: A DL algorithm was developed and evaluated to obtain reproducible and precise EAT segmentation on LDCT. EAT extent in association with lung lesion extent was associated with adverse clinical outcomes with an AUC = 0.805.

Details

Language :
English
ISSN :
20734409
Volume :
11
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Cells
Publication Type :
Academic Journal
Accession number :
edsdoj.b71d6080180c449e9f5fdd3ca8a3366a
Document Type :
article
Full Text :
https://doi.org/10.3390/cells11061034