# AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score

arXiv: https://arxiv.org/abs/2401.16190 · HTML: https://mlsanity.com/paper/2401.16190/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [Cardiac CT perfusion imaging of pericoronary adipose tissue (PCAT) highlights potential confounds in coronary CTA](https://mlsanity.com/paper/2306.15593/) (2023)

## Built on by (most cited first, 3 of 3)

- [PRISM: A Framework Harnessing Unsupervised Visual Representations and Textual Prompts for Explainable MACE Survival Prediction from Cardiac Cine MRI](https://mlsanity.com/paper/2508.19325/) (2025)
- [Quantitative coronary calcification analysis for prediction of myocardial ischemia using non-contrast CT calcium scoring](https://mlsanity.com/paper/2605.21745/) (2026)
- [Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans](https://mlsanity.com/paper/2605.21762/) (2026)

## Abstract (arXiv)

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.