Measurement and Application of Incidentally Detected Coronary Calcium
Open accessAbstract
Coronary artery calcium (CAC) scoring is a powerful tool for atherosclerotic cardiovascular disease risk stratification. The nongated, noncontrast chest computed tomography scan (NCCT) has emerged as a source of CAC characterization with tremendous potential due to the high volume of NCCT scans. Application of incidental CAC characterization from NCCT has raised questions around score accuracy, standardization of methodology including the possibility of deep learning to automate the process, and the risk stratification potential of an NCCT-derived score. In this review, the authors aim to summarize the role of NCCT-derived CAC in preventive cardiovascular health today as well as explore future avenues for eventual clinical applicability in specific patient populations and broader health systems.
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CAC quantification on non-ECG-gated chest CT is not standardised.
“However, CAC quantification on non-ECG-gated chest CT is not standardised.”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [9]
Variability in CT acquisition parameters, motion artefact and lack of uniform scoring protocols introduce gaps in reproducibility and comparability with guideline-endorsed Agatston scoring from dedicated, ECG-gated studies.
“Variability in CT acquisition parameters, motion artefact and lack of uniform scoring protocols introduce gaps in reproducibility and comparability with guideline-endorsed Agatston scoring from dedicated, ECG-gated studies.9”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [9]
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1 of the 78 references couldn’t be matched to a known paper.
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2026 ACC/AHA/AACVPR/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Dyslipidemia
Blumenthal, R. S., Morris, P. B., Gaudino, M., Johnson, H. M., Anderson, T. S., Bittner, V. A., Blankstein, R., Brewer, L. C., Cho, L., de Ferranti, S. D., et al. (2026) · JACC · doi:10.1016/j.jacc.2025.11.016 · Cited by 304 · see citing works
Self-citationWriting Committee Members, Blumenthal, R. S., Morris, P. B., Gaudino, M., Johnson, H. M., Anderson, T. S., Bittner, V. A., Blankstein, R., Brewer, L. C., Cho, L., et al. (2026) · Circulation · doi:10.1161/cir.0000000000001423 · Cited by 122 · see citing works
Self-citationArtificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?
Parsa, S., Somani, S. S., Dudum, R., Jain, S. S., & Rodríguez, F. (2024) · Current Atherosclerosis Reports · doi:10.1007/s11883-024-01210-w · Cited by 22 · see citing works
Self-citationArtificial Intelligence in Ischemic Heart Disease Prevention
Parsa, S., Shah, P., Doijad, R., & Rodríguez, F. (2025) · Current Cardiology Reports · doi:10.1007/s11886-025-02203-0 · Cited by 17 · see citing works
Self-citationIncidental Finding of Coronary and Non-Coronary Artery Calcium: What Do Clinicians Need To Know?
Haudenschild, C., Parsa, S., & Rodriguez, F. (2025) · Current Atherosclerosis Reports · doi:10.1007/s11883-025-01318-7 · Cited by 5 · see citing works
Self-citationGhotbi, E., Hathaway, Q. A., Hadidchi, R., Bancks, M. P., Bluemke, D. A., Wan, M., Barr, R. G., Post, W. S., Budoff, M. J., Smith, B. M., et al. (2025) · Bone · doi:10.1016/j.bone.2025.117659 · Cited by 2 · see citing works
Atherosclerotic Cardiovascular Disease Risk Assessment in the Age of PREVENT
Brar, S., Chakrabarti, A., & Yang, E. (2025) · Current Epidemiology Reports · doi:10.1007/s40471-025-00371-9 · Cited by 2 · see citing works
A missed opportunity: Incidental coronary artery calcification prior to acute coronary syndrome
Hariharan, S., Meere, W., Zhang, K. S., Howden, N., Goonetilleke, N., Mohan, D., Parkinson, M., William, M., & Ford, T. J. (2025) · IJC Heart & Vasculature · doi:10.1016/j.ijcha.2025.101713 · Cited by 1 · see citing works
Iwanowski, M., Pappalettere, C. C., Vime‐Jubany, J., Velescu, A., Marcos, L., Chalela, R., Zuccarino, F., Maiques, J. M., Tizón‐Marcos, H., Vaquerizo, B., et al. (2026) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2025.101396 · Cited by 1 · see citing works
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
Self-citationSchwartz, A. W., Maahs, E. D., Berezowitz, A., Mojibian, H. R., & Guzman, R. J. (2025) · Journal of Vascular Surgery · doi:10.1016/j.jvs.2025.03.196
Hu, Q., zhang, H., Guo, B., jin, D., Sun, M., Chen, J., zhao, Y., Luo, S. J., & lu, G. (2025) · Research Square · doi:10.21203/rs.3.rs-6436906/v1
Fehér, A., & Sinusas, A. J. (2025) · Journal of Nuclear Cardiology · doi:10.1016/j.nuclcard.2025.102559
Impact of slice thickness on CACS calculation with virtual non-contrast in photon-counting CT
Hu, Q., Zhang, H., Guo, B., Jin, D., Sun, M., Chen, J., Luo, S. J., Zhao, Y., & Lu, G.-M. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02162-0
Application and Current Development of Non-Gated Chest CT Coronary Calcification Score
陈, 曦. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.161238
Maneechotesuwan, K., Juthong, S., Sangsayunh, P., Ratanawatkul, P., Vathesatogkit, P., Yingchoncharoen, T., Chirakarnjanakorn, S., Wong, C. K., & Sriprasart, T. (2026) · International Journal of COPD · doi:10.2147/copd.s591999
AI–driven models for early detection and prevention of cardiovascular diseases
Chugh, B., Abbot, V., Chakraborthy, G. S., Tripathi, A. K., Vishwakarma, D. K., Lamba, M., & Lamba, J. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44025-0.00017-6
Cook, D. N., Ravindran, A., O'Connor, S., Huaromo, J., Mannem, H. C., Villines, T. C., & Ayers, M. P. (2026) · Clinical Transplantation · doi:10.1111/ctr.70586
Du, L., Rodríguez, F., Ray, K. K., Yang, E., & Kim, D. S. (2026) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2026.101730
Sienkiewicz, K., Fijałkowska, J., Jelitto-Górska, M., Byczkowski, J., Koliński, M., Szurowska, E., & Rzyman, W. (2026) · Advances in respiratory medicine · doi:10.3390/arm94050075
Lee, S., Cucksey, L., Guo, T., Rezaeianzadeh, R., Samarasinghe, Y., Leung, C., Chen, C., Mehareen, J., Fordyce, C. B., & Myers, R. L. (2026) · Research Square · doi:10.21203/rs.3.rs-10975699/v1
- Authors
- Parsa, S., Saleh, A., Raygor, V., Hoeting, N., Rao, A., Navar, A. M., Rohatgi, A., Kay, F., Abbara, S., Khera, A., & Joshi, P. H.
- Affiliations
- Stanford Medicine (US)
- Texas A&M University (US)
- Sutter Health (US)
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Joshi, Parag H.
- ORCID
- Venue
- Journal of the American College of Cardiology
- Publisher
- Elsevier BV
- Published
- 2024-04
- Volume
- 83
- Issue
- 16
- Pages
- 1557-1567
- ISSN
- 0735-1097 (ISSN-L)
- OpenAlex
- W4394804230
- Cited by
- 23
- References
- 78
- Citations by year
- 2026: 13 · 2025: 9 · 2024: 1
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of the American College of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Valve Diseases and Treatments
- Keywords
- coronary artery calcium, atherosclerotic cardiovascular disease, cardiovascular risk stratification, deep learning
- MeSH terms
- Calcium, Coronary Artery Disease, Coronary Vessels, Heart, Humans, Risk Factors, Tomography, X-Ray Computed, Coronary Angiography, Atherosclerosis, Vascular Calcification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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