Deep learning-based epicardial adipose tissue measurement, maximizing prognostic information from attenuation correction imaging

Epicardial Adipose Tissue (EAT) volume has been associated with risk of cardiovascular events, but manual annotation is time-consuming and only performed on gated Computed Tomography (CT). We developed a Deep Learning (DL) model to segment EAT from gated and ungated CT, then evaluated the association between EAT volume and death or Myocardial Infarction (MI). We included 7712 patients from three sites, two with ungated CT and one using gated CT. Of those, 500 patients from one site with ungated CT were used for model training and validation and 3,701 patients from the remaining two sites were used for external testing. Threshold for abnormal EAT volume (⪆144mL) was derived in the internal population based on Youden's index. DL EAT measurements were obtained in ⪅2 seconds compared to approximately 15 minutes for expert annotations. Excellent Spearman correlation between DL and expert reader on an external subset of N=100 gated (0.94, p⪅0.001) and N=100 ungated (0.91, p⪅0.001). During median follow-up of 3.1 years (IQR 2.1 – 4.0), 306(8.3%) patients experienced death or MI in the external testing populations. Elevated EAT volume was associated with an increased risk of death or MI for gated (hazard ratio [HR] 1.72, 95% CI 1.11-2.67) and ungated CT (HR 1.57, 95% CI 1.20 – 2.07), with no significant difference in risk (interaction p-value 0.692). EAT volume measurements provide similar risk stratification from gated and ungated CT. These measurements could be obtained on chest CT performed for a large variety of indications, potentially improving risk stratification.