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. 2025 Dec 11;3(4):qyaf139.
doi: 10.1093/ehjimp/qyaf139. eCollection 2025 Oct.

Coronary artery stenosis, plaque burden, and severity of myocardial ischemia

Tanja Kero  1   2   3 , Juhani Knuuti  3   4   5 , Sarah Bär  3   6 , Jeroen J Bax  7 , Antti Saraste  3   8 , Teemu Maaniitty  3   5
Affiliations

Coronary artery stenosis, plaque burden, and severity of myocardial ischemia

Tanja Kero et al. Eur Heart J Imaging Methods Pract. .

Abstract

Aims: The relationship between the extent and composition of coronary atherosclerosis and the severity of myocardial ischaemia remains incompletely understood. We assessed whether artificial intelligence-guided coronary computed tomography angiography-derived plaque burden and composition correlate with ischaemia severity.

Methods and results: We included 837 symptomatic patients undergoing coronary computed tomography angiography and subsequent 15O-water positron emission tomography myocardial perfusion imaging. Artificial intelligence-guided coronary computed tomography angiography was used to quantify plaque features-diameter stenosis, percent atheroma volume (PAV), percent non-calcified plaque volume (NCPV), and percent calcified plaque volume (CPV)-per patient and per major coronary artery (LAD, LCx, RCA). Ischaemia severity was classified into four categories based on regional hyperaemic myocardial blood flow. Increasing severity of ischaemia was associated with higher diameter stenosis and plaque burden (PAV, NCPV, CPV) on patient level and in all major coronary territories (overall P < 0.001). The LAD consistently demonstrated higher atherosclerotic burden as compared to the LCx and RCA. Ordinal logistic regression confirmed that diameter stenosis (OR 1.02-1.03, P < 0.001) and NCPV (OR 1.04-1.05, P = 0.011-0.031) were significant predictors of ischaemia severity in all coronary arteries, while CPV was predictive only in the LAD and RCA (OR 1.03-1.04, P = 0.002-0.015).

Conclusion: Artificial intelligence-guided coronary computed tomography angiography-derived measures of plaque burden and stenosis are associated with the severity of myocardial ischaemia, although overlapping distributions across ischaemia severity indicate that anatomical imaging alone may be insufficient for accurate phenotyping of flow-limiting CAD. These findings encourage for the integration of functional imaging with quantitative plaque analysis for a more comprehensive evaluation of coronary artery disease.

Keywords: artificial intelligence; coronary computed tomography angiography; coronary plaque; ischaemia; positron emission tomography.

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Conflict of interest statement

Conflict of interest: S.B. received research grants to the institution from Medis Medical Imaging Systems, Bangerter-Rhyner Stiftung (Basel, Switzerland) and Abbott, outside the submitted work. J.K. received consultancy fees from GE Healthcare and Synektik and speaker fees from Siemens, outside of the submitted work. A.S. received consultancy fees from Astra Zeneca and Pfizer, and speaker fees from Abbott, Astra Zeneca, Janssen, Novartis and Pfizer. J.J.B. received speaker fees from Abbott. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Figures

Graphical Abstract
Graphical Abstract
Relationship Between Coronary Plaque Burden and Ischemia Severity. This graphical abstract illustrates the association between quantitative coronary plaque characteristics and severity of myocardial ischemia. AI-guided coronary computed tomography angiography (AI-QCT) was used to assess plaque burden—measured as diameter stenosis, percent atheroma volume (PAV), non-calcified plaque volume (NCPV), and calcified plaque volume (CPV)—at patient level and in the three major coronary arteries: left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA). As shown, increasing stenosis (a) and PAV (b) generally correspond with increasing ischemia severity, categorized by PET-derived myocardial blood flow into normal, mild, moderate, and severe ischemia. Notably, despite overall trends, substantial overlap in plaque metrics across ischemia categories suggests that anatomical assessment alone may not fully explain functional impairment, supporting the integration of anatomical and functional imaging for comprehensive coronary artery disease evaluation. Created in BioRender. kero, t. (2025) https://BioRender.com/knx9oj2.
Figure 1
Figure 1
Patient flowchart. AI-QCT, artificial intelligence-guided quantitative computed tomography; CAD, coronary artery disease; CTA, computed tomography angiography; MPI, myocardial perfusion imaging; LAD, left anterior descending coronary artery; LCx, left circumflex coronary artery; PET, positron emission tomography; RCA, right coronary artery. This figure was published in J Nucl Cardiol. Nov;53:102470. doi: 10.1016/j.nuclcard.2025.102470. Epub 2025 Aug 9. PMID: 40789366. Kero T, Knuuti J, Bär S, Bax JJ, Saraste A, Maaniitty T: “Stenosis degree and plaque burden differ between the major epicardial coronary arteries supplying ischemic territories”. Copyright Elsevier (2025).
Figure 2
Figure 2
The distribution (%) of normal perfusion and mild, moderate, and severe ischaemia at patient level and in the main coronary artery regions.
Figure 3
Figure 3
Boxplots showing the distribution of diameter stenosis (A), PAV (B), NCPV (C), and CPV (D) at per patient level and vessel level for the main coronary arteries supplying myocardial territories with normal perfusion and mild, moderate and severe ischaemia. The boxes represent the interquartile range (IQR) and the median, the whiskers show the largest and smallest values within 1.5 × IQR, dots represent outliers, and the X in the box represents mean value.
See this image and copyright information in PMC

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