Prediction of major adverse cardiovascular events using comprehensive AI analysis of calcifications and fat depots in CT calcium score images

Ammar Hoori; Joshua Freeze; Prerna Singh; Tao Hu; Yingnan Song; Hao Wu; Juhwan Lee; Shuo Li; Robert Gilkeson; Sadeer Al-Kindi; Sanjay Rajagopalan; David L. Wilson

doi:10.1117/12.3003977

2 April 2024 Prediction of major adverse cardiovascular events using comprehensive AI analysis of calcifications and fat depots in CT calcium score images

Ammar Hoori, Joshua Freeze, Prerna Singh, Tao Hu, Yingnan Song, Hao Wu, Juhwan Lee, Shuo Li, Robert Gilkeson, Sadeer Al-Kindi, Sanjay Rajagopalan, David L. Wilson

Author Affiliations +

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 129300C (2024) https://doi.org/10.1117/12.3003977
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Coronary calcium Agatston score and Epicardial Adipose Tissue (EAT) volume, as measured from CT Calcium Score (CTCS) images, are known risk factors for Major Adverse Cardiovascular Events (MACE). Here, we present greatly-expanded analysis using Coronary Artery Calcification (CAC) features, which more thoroughly capture pathophysiology of atherosclerosis, and EAT features, including HU thought to reflect inflammation, a harbinger of atherosclerosis. MACE-enriched dataset (2316 patients, 13.6% MACE) was divided into balanced training/testing (70/30). We employed manually segmented CACs and automatically segmented EAT using DeepFat. Calcium-omics and fat-omics features were crafted to capture pathophysiology. Elastic-net was employed for feature reduction, and Cox proportional hazards model was used to design novel calcium-fat-omics model. Baseline Agatston and EAT volume models yielded two-year-AUC training/testing results of (72.7%/68.2%) and (60.7%/55.6%), respectively. Our novel comprehensive analyses with some readily available clinical features gave improved results: calcium-omics (82.6%/72.2%), fat-omics (76.7%/71.7%), and calcium-fat-omics (83.7%/73.6%). In Kaplan-Meier survival analysis, the calcium-fat-omics model greatly improved risk stratification as compared to the standard Agatston model with five-risk intervals, suggesting improvement for personalized medicine.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ammar Hoori, Joshua Freeze, Prerna Singh, Tao Hu, Yingnan Song, Hao Wu, Juhwan Lee, Shuo Li, Robert Gilkeson, Sadeer Al-Kindi, Sanjay Rajagopalan, and David L. Wilson "Prediction of major adverse cardiovascular events using comprehensive AI analysis of calcifications and fat depots in CT calcium score images", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 129300C (2 April 2024); https://doi.org/10.1117/12.3003977

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