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Updated: Jan 17, 2026
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Coronary Artery Calcium Scoring on Dedicated Cardiac CT and Noncardiac CT Scans
Omar Dzaye1,2, Alexander C Razavi3, Yara A Jelwan1
1Johns Hopkins Ciccarone Center for Prevention of Cardiovascular Disease, Johns Hopkins Hospital, 600 N Wolfe St, Blalock 524D1, Baltimore, MD 21287.
Insights
Coronary artery calcium (CAC) can be measured on noncardiac chest CT scans, offering a cost-free method for assessing atherosclerotic cardiovascular disease (ASCVD) risk. This incidental CAC assessment identifies at-risk patients who may benefit from enhanced preventive therapies.
Area of Science:
- Cardiology
- Radiology
- Preventive Medicine
Background:
- Coronary artery calcium (CAC) is a key indicator of subclinical coronary atherosclerosis and a strong predictor of atherosclerotic cardiovascular disease (ASCVD) risk.
- While dedicated cardiac CT is the standard for CAC quantification, noncardiac chest CT scans also allow for qualitative and quantitative CAC assessment with comparable prognostic value.
Purpose of the Study:
- To review the fundamentals of CAC scoring, emphasizing the detection and quantification of incidental CAC on noncardiac chest CT scans.
- To discuss the technical aspects, challenges, and recommendations for reporting and managing incidental CAC findings.
- To highlight the potential of incidental CAC measurement as an underutilized strategy for ASCVD prevention.
Main Methods:
- Review of existing literature on CAC quantification and incidental findings on chest CT.
- Discussion of technical approaches for incidental CAC detection and measurement.
- Presentation of clinical experiences and recommendations for integrating incidental CAC assessment into routine practice.
Main Results:
- Incidental CAC measurement on noncardiac chest CT is feasible and provides similar prognostic information to dedicated CAC scans.
- Nearly 20 times more chest CT scans are performed annually than dedicated CAC scans, representing a significant opportunity for ASCVD risk identification.
- Standardized reporting and management protocols can facilitate the clinical utility of incidental CAC findings.
Conclusions:
- Incidental CAC assessment on routine chest CT scans is a valuable, low-cost tool for identifying individuals at high risk for ASCVD.
- Integrating incidental CAC measurement into clinical practice can enhance preventive strategies and improve patient outcomes.
- Future advancements, including artificial intelligence, may further automate and improve incidental CAC interpretation.
Abstract:
Coronary artery calcium (CAC) is a specific marker of subclinical coronary atherosclerosis and is strongly associated with short- and long-term atherosclerotic cardiovascular disease (ASCVD) risk. Although noncontrast electrocardiographically gated cardiac CT is the reference standard for quantification of CAC (approximately 1 mSv), studies have shown that CAC can also be qualitatively interpreted and quantified on noncardiac chest CT scans with similar prognostic value. While use of dedicated CAC scans is increasing, measurement of incidental CAC represents a major untapped opportunity for ASCVD prevention, given that nearly 20 times more chest CT examinations are performed annually in the United States than dedicated CAC scans. Incidental measurement of CAC at chest CT incurs no additional cost or radiation for patients and can identify those with significant CAC burden who may be inadequately treated with ASCVD risk reduction therapies. This review outlines the fundamentals of CAC scoring, with a focus on detection and quantification of incidental CAC. It details the technical approaches and challenges of incidental CAC assessment and provides recommendations for routine reporting, clinical advisories, and subsequent patient management. The review also presents first-hand experiences from a large academic medical center's initiative to standardize incidental CAC reporting. Future directions include the use of artificial intelligence to automate both basic and advanced CAC interpretation.
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