Evidence map›Paper›PMID 42433512›Full record

ArticleQuantitative imaging in medicine and surgery2026

Quantifying the impact of slice thickness on cardiovascular risk stratification in lung cancer screening: a multi-center "RESCUE" study.

Min Wang, Jingyi Zhang, Rong Zhu, Jun Gu, Li Fan, Qizhi Chen

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Min Wang *Department of Cardiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jingyi Zhang *Lingyun Community Health Center, Xuhui District, Shanghai, China.
Rong ZhuIndependent Researcher, Shanghai, China.
Jun GuDepartment of Cardiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Li FanDepartment of Cardiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qizhi ChenDepartment of Cardiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID https://orcid.org/0009-0001-3065-8917

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Patients undergoing routine non-gated chest computed tomography (CT) for health checkups or atypical chest discomfort often present with a coronary artery calcium (CAC) score of zero on standard 5.0 mm reconstructions. We hypothesized that these thick slices obscure mild calcification due to partial volume effects (PVEs), which could be recovered by retrospective analysis of native thin-slice images. This study aimed to quantify the rate of unrecognized coronary calcification on standard thick-slice CT by comparing paired thin- and thick-slice reconstructions. Methods: We analyzed data of 2,914 patients across four datasets: Stanford Coronary Calcium and chest CT's (COCA) (n=651) for reference validation; an internal cohort evaluated by invasive angiography for early-onset coronary artery disease (CAD) (n=766) and National Lung Screening Trial (NLST) (n=852) with paired thin (1.0-2.0 mm) Results: In the internal cohort, 19.0% were reclassified from CAC =0 on 5.0 mm scans to CAC >0 on 1.0 mm scans. Similarly, 10.2% of NLST participants were reclassified using 2.0 mm scans. Most reclassified patients (91-99%) fell into the mild risk category (Agatston 1-99). Crucially, 31% of symptomatic patients with CAC =0 on standard scans had obstructive CAD (>50% stenosis); many were "rescued" to a positive CAC status by thin-slice analysis. Risk categorization showed strong agreement (weighted kappa 0.705-0.816). Artificial intelligence (AI) correlated strongly with expert annotations (r=0.956). Conclusions: Standard 5 mm reconstructions cause significant false negative CAC assessments. Analyzing routinely available thin slice reconstructions improves sensitivity for early subclinical atherosclerosis without additional radiation, supporting their use in opportunistic screening.

Indexed as

artificial intelligence (AI)Coronary artery calcium (CAC)opportunistic screeningrisk stratificationslice thickness

Identifiers

PMID42433512
PMCPMC13349966

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.