Leveraging calcium score CT radiomics for heart failure risk prediction
Open accessAbstract
Studies have used extensive clinical information to predict time-to-heart failure (HF) in patients with and without diabetes mellitus (DM). We aimed to determine a screening method using only computed tomography calcium scoring (CTCS) to assess HF risk. We analyzed CTCS scans from 1,998 patients (336 with type 2 diabetes) from a no-charge coronary artery calcium score registry (CLARIFY Study, Clinicaltrials.gov NCT04075162). We used deep learning to segment epicardial adipose tissue (EAT) and engineered radiomic features of calcifications ("calcium-omics") and EAT ("fat-omics"). We developed models incorporating radiomics to predict risk of incident HF in patients with and without type 2 diabetes. At a median follow-up of 1.7 years, 5% had incident HF. In the overall cohort, fat-omics (C-index: 77.3) outperformed models using clinical factors, EAT volume, Agatston score, calcium-omics, and calcium-and-fat-omics to predict HF. For DM patients, the calcium-omics model (C-index: 81.8) outperformed other models. In conclusion, CTCS-based models combining calcium and fat-omics can predict incident HF, outperforming prediction scores based on clinical factors.Please check article title if captured correctly.YesPlease check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.Yes.
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Radiomics-based approaches sought to refine risk stratification by extracting higher-dimensional imaging features beyond the Agatston score, including plaque texture, spatial distribution, density characteristics and lesion morphology.
“Radiomics-based approaches sought to further refine risk stratification by extracting higher-dimensional imaging features beyond the Agatston score, including plaque texture, spatial distribution, density characteristics and lesion morphology.252635–37”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [36]
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- Authors
- Singh, P., Hoori, A., Freeze, J., Hu, T., Tashtish, N., Gilkeson, R., Li, S., Rajagopalan, S., Wilson, D. L., & Al-Kindi, S.
- Affiliations
- Case Western Reserve University (US)
- University Hospitals of Cleveland (US)
- Houston Methodist (US)
- Corresponding author
- Al-Kindi, Sadeer
- ORCID
- Venue
- Scientific Reports
- Published
- 2024-11-06
- Volume
- 14
- Issue
- 1
- Pages
- 26898-26898
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11541497
- OpenAlex
- W4404119181
- Cited by
- 19
- References
- 29
- Citations by year
- 2026: 9 · 2025: 10
- FWCI
- 4.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health (T32 GM007250, R01HL167199)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Disease and Adiposity
- Keywords
- heart failure prediction, coronary artery calcium score, CT radiomics, epicardial adipose tissue, type 2 diabetes, deep learning segmentation, Agatston score, incident heart failure
- MeSH terms
- Deep Learning, Radiomics, Adipose Tissue, Aged, Calcium, Coronary Artery Disease, Diabetes Mellitus, Type 2, Female, Heart Failure, Humans, Male, Middle Aged, Risk Factors, Tomography, X-Ray Computed, Risk Assessment
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