Background: Recent studies have used basic epicardial adipose tissue (EAT) assessments (e.g., volume and mean HU) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACE). Objectives: Create novel, hand-crafted EAT features, 'fat-omics', to capture the pathophysiology of EAT and improve MACE prediction. Methods: We segmented EAT using a previously-validated deep learning method with optional manual correction. We extracted 148 radiomic features (morphological, spatial, and intensity) and used Cox elastic-net for feature reduction and prediction of MACE. Results: Traditional fat features gave marginal prediction (EAT-volume/EAT-mean-HU/ BMI gave C-index 0.53/0.55/0.57, respectively). Significant improvement was obtained with 15 fat-omics features (C-index=0.69, test set). High-risk features included volume-of-voxels-having-elevated-HU-[-50, -30-HU] and HU-negative-skewness, both of which assess high HU, which as been implicated in fat inflammation. Other high-risk features include kurtosis-of-EAT-thickness, reflecting the heterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart, emphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of Cox-identified, high- and low-risk patients were well separated with the median of the fat-omics risk, while high-risk group having HR 2.4 times that of the low-risk group (P<0.001). Conclusion: Preliminary findings indicate an opportunity to use more finely tuned, explainable assessments on EAT for improved cardiovascular risk prediction.

翻译：背景：近期研究使用基础的心外膜脂肪组织（EAT）评估指标（如体积和平均HU值）预测动脉粥样硬化相关主要不良心血管事件（MACE）的风险。目的：构建新型手工设计的EAT特征"脂肪组学"，以捕捉EAT的病理生理学特征并提升MACE预测能力。方法：采用前期验证的深度学习算法（可选人工校正）分割EAT区域。提取148项影像组学特征（形态学、空间分布及强度特征），使用Cox弹性网络进行特征筛选与MACE预测。结果：传统脂肪特征预测效果有限（EAT体积/EAT平均HU/BMI的C指数分别为0.53/0.55/0.57）。基于15项脂肪组学特征后获得显著提升（测试集C指数=0.69）。高风险特征包括高HU值体素体积（-50至-30 HU区间）和负偏态HU值——两者均评估与脂肪炎症相关的高HU值。其他高风险特征包括EAT厚度峰度（反映厚度异质性）及心脏顶部25%区域的EAT体积（强调近端冠状动脉周围脂肪）。基于Cox模型识别的中位脂肪组学风险值，能够明确区分高低风险患者的Kaplan-Meyer曲线，高风险组风险比（HR）是低风险组的2.4倍（P<0.001）。结论：初步研究表明，采用更精细化的可解释性EAT评估方法可改善心血管风险预测。