Comparative Efficacy of Non-contrast vs. Contrast-enhanced CT Radiomics in Predicting Coronary Artery Plaques Among Patients with Low Agatston Scores
Open accessAbstract
RATIONALE AND OBJECTIVES: Patients with a low Agatston score often present with clinical signs and symptoms suggestive of coronary artery disease, despite having minimal calcium deposits. This study aimed to compare the efficacy of low-dose non-contrast cardiac CT with coronary computed tomography angiography (CCTA) in pericoronary adipose tissue (PCAT) radiomics for predicting coronary artery plaques, using CCTA as the reference standard. MATERIALS AND METHODS: This retrospective study analyzed 459 patients with suspected coronary artery disease and a coronary artery calcium score < 100 Agatston units, who were treated between June 2021 and December 2023 at a tertiary hospital. Three predictive models for coronary artery plaques were developed: (1) a clinical factor model, (2) a hybrid model integrating clinical factors and CT PCAT radiomics, and (3) a hybrid model integrating clinical factors and CCTA PCAT radiomics. Multivariable logistic regression and receiver operating characteristic curve evaluations were performed to develop and validate predictive models. RESULTS: Both hybrid models showed significant correlations in the training set (r = 0.890, P < 0.001) and the validation set (r = 0.920, P < 0.001). The mean agreement in the training set is 0, with 3.42% (11/322) of the data points outside the 95% CI (-0.18-0.18, P < 0.001). The mean agreement in the validation set is -0.244, with 6.57% (9/137) of the data points outside the 95% CI (-0.443-0.045, P < 0.001). CONCLUSIONS: Non-contract CT PCAT radiomics showed comparable efficacy to CCTA PCAT radiomics in predicting coronary artery plaques among patients with low Agatston scores.
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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 [37]
The Extraction and Selection of the Radiomic Features section reports texture-related, first-order and shape features, and the Discussion says radiomics depicts the texture and spatial relationships of pericoronary adipose tissue (PCAT). The study uses those PCAT features to predict plaques in patients with low calcium scores; it does not describe extracting plaque texture or lesion morphology as radiomic features. Plaque density is used separately to classify plaques. (LLM, conf=0.86)
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Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review
McDonald, V. J. F., Parsa, S., Mastrodicasa, D., Rodriguez, F., Langlotz, C., Maron, D. J., Sandhu, A. T., Dudum, R., & Jain, S. S. (2026) · BMJ Digital Health & AI · doi:10.1136/bmjdh-2026-000053
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- Authors
- Liang, J., Lin, C., Qi, H., Lin, Y., Deng, L., Wu, J., Yang, C., He, Z., Li, J., Li, H., Hu, D., Chen, H., & Li, Y.
- Affiliations
- Southern Medical University (CN)
- Nanfang Hospital (CN)
- Corresponding author
- Li, Yuanzhang
- Venue
- Academic Radiology
- Publisher
- Elsevier BV
- Published
- 2025-03
- Volume
- 32
- Issue
- 3
- Pages
- 1344-1352
- ISSN
- 1076-6332 (ISSN-L)
- OpenAlex
- W4405561270
- Cited by
- 2
- References
- 35
- Citations by year
- 2026: 2
- FWCI
- 0.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Academic Radiology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- 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
- coronary plaque, pericoronary adipose tissue, radiomics, non-contrast cardiac CT, coronary CT angiography, coronary artery disease
- MeSH terms
- Computed Tomography Angiography, Radiomics, Adipose Tissue, Aged, Contrast Media, Coronary Artery Disease, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Retrospective Studies, Tomography, X-Ray Computed, Coronary Angiography, Plaque, Atherosclerotic
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