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      Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography : Implications for Cardiovascular Risk Prediction

      research-article
      , BMedSci, MBBS a , b , , PhD a , b , c , , MD, PhD d , , BSc a , b , , BSc e , , MPhil a , b , , BSc a , b , , BSc a , b , , MD a , , PhD a , c , , MD f , , MD, PhD g , h , , MD, PhD i , , MD j , , MD, PhD j , k , , MD, MBA c , l , , MD, PhD m , , MD n , , MD, PhD b , l , , MD, PhD a , b , l , , MD, MBA o , , MD, MBA f , p , , MD, PhD d , , MD, PhD a , b , l , , ORFAN Investigators
      Jacc. Cardiovascular Imaging
      Elsevier
      adipose tissue, atherosclerosis, atrial fibrillation, computed tomography, deep-learning, visceral fat

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          Abstract

          Background

          Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented.

          Objectives

          This study sought to develop a deep-learning network for automated quantification of EAT volume from CCTA, test it in patients who are technically challenging, and validate its prognostic value in routine clinical care.

          Methods

          The deep-learning network was trained and validated to autosegment EAT volume in 3,720 CCTA scans from the ORFAN (Oxford Risk Factors and Noninvasive Imaging Study) cohort. The model was tested in patients with challenging anatomy and scan artifacts and applied to a longitudinal cohort of 253 patients post-cardiac surgery and 1,558 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) Trial, to investigate its prognostic value.

          Results

          External validation of the deep-learning network yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT volume was associated with coronary artery disease (odds ratio [OR] per SD increase in EAT volume: 1.13 [95% CI: 1.04-1.30]; P = 0.01), and atrial fibrillation (OR: 1.25 [95% CI: 1.08-1.40]; P = 0.03), after correction for risk factors (including body mass index). EAT volume predicted all-cause mortality (HR per SD: 1.28 [95% CI: 1.10-1.37]; P = 0.02), myocardial infarction (HR: 1.26 [95% CI:1.09-1.38]; P = 0.001), and stroke (HR: 1.20 [95% CI: 1.09-1.38]; P = 0.02) independently of risk factors in SCOT-HEART (5-year follow-up). It also predicted in-hospital (HR: 2.67 [95% CI: 1.26-3.73]; P ≤ 0.01) and long-term post–cardiac surgery atrial fibrillation (7-year follow-up; HR: 2.14 [95% CI: 1.19-2.97]; P ≤ 0.01).

          Conclusions

          Automated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification.

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          2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain

          This clinical practice guideline for the evaluation and diagnosis of chest pain provides recommendations and algorithms for clinicians to assess and diagnose chest pain in adult patients.
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            Author and article information

            Contributors
            @Charis_Oxford @henrywwest
            Journal
            JACC Cardiovasc Imaging
            JACC Cardiovasc Imaging
            Jacc. Cardiovascular Imaging
            Elsevier
            1936-878X
            1876-7591
            1 June 2023
            June 2023
            : 16
            : 6
            : 800-816
            Affiliations
            [a ]Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
            [b ]Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
            [c ]Caristo Diagnostics Pty Ltd, Oxford, United Kingdom
            [d ]Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland, United Kingdom
            [e ]Northwestern University, Evanston, Illinois, USA
            [f ]Royal Brompton and Harefield National Health Service (NHS) Foundation Trust, London, United Kingdom
            [g ]Translational Cardiovascular Research Group, Department of Cardiology, Milton Keynes University Hospital, Milton Keynes, United Kingdom
            [h ]Faculty of Medicine and Health Sciences, University of Buckingham, Buckingham, United Kingdom
            [i ]Department of Cardiovascular Sciences and National Institute for Health Research Leicester Biomedical Research Centre, University of Leicester, Leicester, United Kingdom
            [j ]Royal United Hospitals Bath NHS Foundation Trust, Bath, United Kingdom
            [k ]Department of Health, University of Bath, Bath, United Kingdom
            [l ]Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
            [m ]University College London, London, United Kingdom
            [n ]National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
            [o ]The Cleveland Clinic, Cleveland, Ohio, USA
            [p ]School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
            Author notes
            [] Address for correspondence: Prof Charalambos Antoniades, British Heart Foundation Chair of Cardiovascular Medicine, Acute Multidisciplinary Imaging and Interventional Centre, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom. charalambos.antoniades@ 123456cardiov.ox.ac.uk @Charis_Oxford @henrywwest
            Article
            S1936-878X(22)00722-7
            10.1016/j.jcmg.2022.11.018
            10663979
            36881425
            ea18095b-5ca6-4647-907f-f9129918a08e
            © 2023 The Authors

            This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

            History
            : 17 July 2022
            : 9 November 2022
            : 17 November 2022
            Categories
            Original Research

            adipose tissue,atherosclerosis,atrial fibrillation,computed tomography,deep-learning,visceral fat

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