AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score
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| Natura: | Preprint |
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2024
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| _version_ | 1866911767330816000 |
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| author | Hu, Tao Freeze, Joshua Singh, Prerna Kim, Justin Song, Yingnan Wu, Hao Lee, Juhwan Al-Kindi, Sadeer Rajagopalan, Sanjay Wilson, David L. Hoori, Ammar |
| author_facet | Hu, Tao Freeze, Joshua Singh, Prerna Kim, Justin Song, Yingnan Wu, Hao Lee, Juhwan Al-Kindi, Sadeer Rajagopalan, Sanjay Wilson, David L. Hoori, Ammar |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_16190 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score Hu, Tao Freeze, Joshua Singh, Prerna Kim, Justin Song, Yingnan Wu, Hao Lee, Juhwan Al-Kindi, Sadeer Rajagopalan, Sanjay Wilson, David L. Hoori, Ammar Quantitative Methods Artificial Intelligence 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. |
| title | AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2401.16190 |