Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction
Open accessAbstract
Low-dose computed tomography attenuation correction (CTAC) scans are used in hybrid myocardial perfusion imaging (MPI) for attenuation correction and coronary calcium scoring, and contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI) approach. A multi-structure model segmented 33 structures and quantified 15 radiomics features in each organ in 10,480 patients from 4 sites. Coronary calcium and epicardial fat measures were obtained from separate AI models. The area under the receiver-operating characteristic curves (AUC) for all-cause mortality prediction of the model utilizing MPI, CT, stress test, and clinical features was 0.80 (95% confidence interval [0.74-0.87]), which was higher than for coronary calcium (0.64 [0.57-0.71]) or perfusion (0.62 [0.55-0.70]), with p < 0.001 for both. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing hybrid MPI.
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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 [26]
The Methods section reports extracting first-order statistical and 3D shape radiomic features from multiple structures, and the Results compare the resulting model with coronary calcium scoring for mortality prediction (pages not provided). The paper does not identify plaque texture, spatial distribution, density characteristics, or lesion morphology as features it extracted; its reported density measure concerns epicardial adipose tissue, not plaque. (LLM, conf=0.88)
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- Authors
- Marcinkiewicz, A. M., Zhang, W., Shanbhag, A., Miller, R. J. H., Lemley, M., Ramirez, G., Buchwald, M., Killekar, A., Kavanagh, P. B., Feher, A., Miller, E. J., Einstein, A. J., Ruddy, T. D., Liang, J. X., Builoff, V., Ouyang, D., Berman, D. S., Dey, D., & Slomka, P. J.
- Affiliations
- Cedars-Sinai Medical Center (US)
- University of Southern California (US)
- Yale University (US)
- NewYork–Presbyterian Hospital (US)
- Ottawa Hospital (CA)
- Corresponding author
- Slomka, Piotr J.
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2025-03-13
- Volume
- 8
- Issue
- 1
- Pages
- 158-158
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC11906890
- OpenAlex
- W4408391722
- Cited by
- 12
- References
- 45
- Citations by year
- 2026: 8 · 2025: 4
- FWCI
- 6.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Heart, Lung, and Blood Institute (R01HL089765)
- 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 › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- all-cause mortality prediction, radiomics, coronary artery calcium, epicardial adipose tissue, coronary artery disease
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