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Predicting Major Adverse Cardiovascular Events (MACE) accurately is crucial for implementing personalized medicine interventions effectively. Recent research has highlighted the significance of thoracic fat deposits, specifically Epicardial Adipose Tissue (EAT) and Paracardial Adipose Tissue (PAT), in predicting MACE. Their proximity to the coronary arteries and potential role in stimulating inflammation and atherosclerosis development contribute to their predictive utility. In this study, we developed a MACE prediction model based on Cox proportional hazards model with elastic net regularization, incorporating hand-crafted image features derived from EAT and PAT in non-contrast, CT Calcium Score (CTCS) exams. We constructed and collected morphological, intensity, and spatial features from manually corrected, deep learning-based adipose segmentation. To highlight the influence of imaging features, our preliminary study utilized a MACE-enriched cohort of 400 individuals (56% MACE) from a CLARIFY study of the University Hospitals of Cleveland. We divided the cohort into training (80%) and held-out testing (20%). We obtained c-index (training/testing) results for EAT-omics (0.66/0.69), and PAT-omics (0.64/0.67) models, respectively, both much better than the traditional EAT volume model gave (0.53/0.53). Notably, we identified high-risk features, including negative HU skewness in EAT, likely an indicator of fatty inflammation. Similar measurements in PAT did not. The improved discrimination with EAT and its connection to inflammation markers is consistent with its direct vascular communication with the myocardium and coronary vasculature. As PAT is outside the pericardial sac, it does not have direct vascular communication. These promising preliminary findings suggest that an AI adipose analysis can be a useful add-on to improve MACE prediction from CTCS exams.
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