Evidence map›Paper›PMID 42033770›Full record

ReviewThe British journal of radiology2026

Coronary artery calcium score-emerging role for opportunistic screening on thoracic CT and novel applications.

Ryan Patel, Michelle C Williams

Abstract readReview
In one paragraph

Review in The British journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ryan PatelBritish Heart Foundation Centre of Research Excellence, University of Edinburgh, Edinburgh EH16SUF, United Kingdom.
Michelle C WilliamsBritish Heart Foundation Centre of Research Excellence, University of Edinburgh, Edinburgh EH16SUF, United Kingdom.ORCID 0000-0003-3556-2428

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronary artery calcium (CAC) can be identified on thoracic CT performed for a variety of clinical indications, including routine thoracic CT, lung cancer screening CT, and attenuation correction CT. Identification of CAC on thoracic CT as part of opportunistic screening can improve the assessment of cardiovascular risk and can identify patients who may benefit from preventative medical therapies. This approach enhances early detection of coronary artery disease within existing healthcare workflows. However, at present, there are no randomized trials that assess the clinical impact of this approach. Recently, the development of artificial intelligence/machine learning (AI/ML) models has facilitated the automated identification and quantification of CAC, offering rapid and reproducible assessments. Artificial intelligence/machine learning models could reduce radiologist workload and provide end-to-end integration into routine clinical practice. Novel technologies such as photon-counting CT offer further potential for CAC detection and risk stratification through the use of high-resolution imaging and spectral imaging with virtual non-contrast images. This review discusses the evidence for opportunistic screening for CAC on thoracic CT and highlights novel applications including AI/ML assessments, CAC as a multi-system biomarker, and the potential for photon counting CT.

Indexed as

Coronary Artery DiseaseRadiography, ThoracicTomography, X-Ray ComputedVascular CalcificationArtificial IntelligenceCoronary VesselsHumansMachine Learningartificial intelligencecomputed tomographycoronary artery calciummachine learningphoton counting computed tomography

Identifiers

PMID42033770
PMCPMC13623238

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.