ArticleJACC. Cardiovascular imaging2022
Radiomics-Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography Angiography.
Article in JACC. Cardiovascular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
37 citing papers in PubMed, 3 syntheses or guidelines pooled it, 65 citations in OpenAlex.
- The Machine Learning Models in Major Cardiovascular Adverse Events Prediction Based on Coronary Computed Tomography Angiography: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Application of artificial intelligence in non-invasive cardiovascular imaging for coronary artery disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Standards for quantitative assessments by coronary computed tomography angiography (CCTA): An expert consensus document of the society of cardiovascular computed tomography (SCCT).Journal of cardiovascular computed tomographyGuideline
- Advancing Coronary Risk Assessment Through Combined Radiomic, Mechanical, and Hemodynamic Analysis.Annals of biomedical engineering · 2026Article
- Radiomic Features Extracted from Magnetic Resonance Imaging to Identify Arteriopathy in a Humanized Mouse Model of Sickle Cell Anemia and Sickle Cell Trait.Annals of biomedical engineering · 2026Article
- Unraveling Atherosclerosis through Multi-omics: Systematic Insights into the Unique Applications and Clinical Perspectives.Current atherosclerosis reports · 2026Review
- Deep learning-based automated segmentation and quantification of aortic arch calcification at chest radiograph.BMC geriatrics · 2026Article
- AI-driven radiomics in vascular disease: a narrative review of image analysis and clinical translation.Cardiovascular diagnosis and therapy · 2026Review
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Patient-level CAD-RADS scoring from coronary radiomic features.Scientific reports · 2026Article
- Uric acid-associated mechanisms of coronary artery calcification in diabetic kidney disease: evidence, hypotheses, and translational perspectives.Frontiers in cardiovascular medicine · 2026Review
- Cardiac CT for personalized phenotyping in stable coronary artery disease: toward precision medicine.BJR open · 2026Review
- Perivascular fat attenuation index at internal carotid artery stenosis: a biomarker of perivascular adipose tissue (PVAT) inflammation.Japanese journal of radiology · 2026Article
- A CCTA coronary plaque radiomic model for predicting major adverse cardiovascular events in patients with coronary artery disease.Frontiers in cardiovascular medicine · 2026Article
- Coronary computed tomography angiography plaque and flow patterns in acute coronary syndrome lesions.iScience · 2025Article
- Cardiac CT in the era of artificial intelligence: precision imaging, treatment guidance and optimised risk stratification for coronary artery disease.Open heart · 2025Review
- An artificial intelligence model integrating culprit lesion diagnosis and risk assessment for acute coronary syndrome.EClinicalMedicine · 2025Article
- Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Quantitative plaque characterization, pericoronary fat attenuation index, and fractional flow reserve: a novel method for differentiating between stable and unstable angina pectoris in a case-control study.Quantitative imaging in medicine and surgery · 2025Article
- Integrated CT-derived fractional flow reserve and perivascular fat attenuation index: a multimodal approach to predict in-stent restenosis.Frontiers in cardiovascular medicine · 2025Article
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Authors and funding
15 authors at 5 institutions in 4 countries.
Funding
Abstract
objectivesThe aim of this study was to precisely phenotype culprit and nonculprit lesions in myocardial infarction (MI) and lesions in stable coronary artery disease (CAD) using coronary computed tomography angiography (CTA)-based radiomic analysis.
backgroundIt remains debated whether any single coronary atherosclerotic plaque within the vulnerable patient exhibits unique morphology conferring an increased risk of clinical events.
methodsA total of 60 patients with acute MI prospectively underwent coronary CTA before invasive angiography and were matched to 60 patients with stable CAD. For all coronary lesions, high-risk plaque (HRP) characteristics were qualitatively assessed, followed by semiautomated plaque quantification and extraction of 1,103 radiomic features. Machine learning models were built to examine the additive value of radiomic features for discriminating culprit lesions over and above HRP and plaque volumes.
resultsCulprit lesions had higher mean volumes of noncalcified plaque (NCP) and low-density noncalcified plaque (LDNCP) compared with the highest-grade stenosis nonculprits and highest-grade stenosis stable CAD lesions (NCP: 138.1 mm
conclusionsCulprit lesions and highest-grade stenosis nonculprit lesions in MI have distinct radiomic signatures compared with lesions in stable CAD. Within the vulnerable patient may exist individual vulnerable plaques identifiable by coronary CTA-based precision phenotyping.
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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.