Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19 - Archive ouverte HAL Access content directly
Journal Articles Cells Year : 2022

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

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Axel Bartoli
Joris Fournel
Léa Ait-Yahia
  • Function : Author
Farah Cadour
Farouk Tradi
  • Function : Author
Badih Ghattas
  • Function : Author
Adèle Lasbleiz
  • Function : Author
Anne Dutour
  • Function : Author
Bénédicte Gaborit

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.

Dates and versions

hal-03663817 , version 1 (10-05-2022)

Identifiers

Cite

Axel Bartoli, Joris Fournel, Léa Ait-Yahia, Farah Cadour, Farouk Tradi, et al.. Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19. Cells, 2022, 11 (6), pp.1034. ⟨10.3390/cells11061034⟩. ⟨hal-03663817⟩
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