Deep learning-based quantification of epicardial adipose tissue volume from non-contrast computed tomography images: a multi-centre study

作者：Shuang Leng, Nicholas Cheng, Eddy Tan, Lohendran Baskaran, Lynette Teo, Min Sen Yew, Kee Yuan Ngiam, Weimin Huang, Ping Chai, Ching Ching Ong, Ching‐Hui Sia, Malay Singh, Yan Ting Loong, N A S Raffiee, Xiaomeng Wang, John Carson Allen, Swee Yaw Tan, Mark Y. Chan, Hwee Kuan Lee, Liang Zhong · 发表于：European Heart Journal - Digital Health · 年份：2025 · DOI：10.1093/ehjdh/ztaf116 · 被引用次数：1 · 研究领域：Cardiovascular Disease and Adiposity、Adipokines, Inflammation, and Metabolic Diseases、Cardiovascular Function and Risk Factors

Abstract Aims Epicardial adipose tissue (EAT), located within the pericardial sac, has emerged as a biomarker for coronary artery disease (CAD) progression. This study aimed to develop and validate a deep learning-based system for automated EAT volume quantification using non-contrast computed tomography (NCCT) scans from a large, multi-centre, pan-Asian cohort. Methods and results A total of 1243 NCCT patient scans from three centres were used to train and internally validate a deep learning model based on 3D UNet++ architecture for pericardium segmentation, followed by intensity thresholding to derive EAT volume. Epicardial adipose tissue quantification required ∼30 s per scan. The final model was evaluated on an external testing cohort of 160 patients, including 90 non-Asian individuals. In this cohort, AI-predicted EAT volumes showed excellent agreement with expert annotations (r = 0.975; P < 0.0001). The Bland–Altman analysis demonstrated a mean bias of −5.2 cm3with 95% limits of agreement from −25.1 to 14.7 cm3. Among the non-Asian subgroup, model performance remained strong (r = 0.970; bias, −3.2 cm3; limits of agreement, −25.1–18.7 cm3). AI-derived EAT volume was independently associated with obstructive CAD (odds ratio 1.11; 95% confidence interval, 1.04–1.19; P = 0.004), after adjusting for confounders. The global χ2 statistic increased from 81.7 with coronary calcium score alone to 93.3 when EAT volume was added (P = 0.001), indicating improved risk predicti...