Journal
EBIOMEDICINE
Volume 85, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.ebiom.2022.104315
Keywords
COVID-19; NAFLD; Hepatic steatosis
Funding
- National Cancer Institute [1U24CA199374-01, R01 CA202752-01A1, R01 CA208236-01A1, R01 CA216579-01A1, R01 CA220581-01A1, 1U01 CA239055-01, 1U01CA248226-01, 1U54CA254566-01]
- National Heart, Lung, and Blood Institute [1R01HL15127701A1]
- National Institute of Biomedical Imaging and Bioengineering [1R43EB028736-01]
- National Center for Research Resources [C06 RR12463-01]
- VA Merit Review Award [IBX004121A]
- United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, The Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program [W81XWH-19-10668]
- Prostate Cancer Research Program [W81XWH-15-1-0558, W81XWH-20-1-0851, W81XWH18-1-0440]
- Peer Reviewed Cancer Research Program [W81XWH-18-1-0404]
- Kidney Precision Medicine Project (KPMP) Glue Grant
- Ohio Third Frontier Technology Validation Fund [UL1TR0002548]
- National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health
- NIH Roadmap for Medical Research
- Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University
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This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for segmenting and estimating liver attenuation on CT images, and investigates the association between hepatic steatosis and the severity of COVID-19 infections. The results show that DeHFt pipeline accurately assesses hepatic steatosis and reveals its association with the severity of COVID-19 infections.
Background Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death. Methods DeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N = 80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N = 805; D2, N = 1917; D3, N = 169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity. Findings The DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93-0.96] on the independent validation cohort (N = 49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR = 1.50, 95% CI [1.20-1.88], P < .001). Interpretation The DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N = 2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment. Copyright (C) 2022 The Authors. Published by Elsevier B.V.
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