Trial reportJACC. Cardiovascular imaging2025
Coronary Plaque Radiomic Phenotypes Predict Fatal or Nonfatal Myocardial Infarction: Analysis of the SCOT-HEART Trial.
Trial report in JACC. Cardiovascular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01149590 (Role of Multidetector Computed Tomography in the Diagnosis and Management of Patients Attending a Rapid Access Chest Pain Clinic), which is not on this map. Cited by 13 papers.
What it found
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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.
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.
Role of Multidetector Computed Tomography in the Diagnosis and Management of Patients Attending a Rapid Access Chest Pain Clinic
Who cites it
13 citing papers in PubMed.
- Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial.Open heart · 2025Trial
- Coronary artery calcium score-emerging role for opportunistic screening on thoracic CT and novel applications.The British journal of radiology · 2026Review
- Unraveling Atherosclerosis through Multi-omics: Systematic Insights into the Unique Applications and Clinical Perspectives.Current atherosclerosis reports · 2026Review
- First scan, then treat: 10 years of the SCOT-HEART study.European heart journal supplements : journal of the European Society of Cardiology · 2026Article
- Applications of coronary computed tomography angiography: a clinical cardiologist's point of view.European heart journal supplements : journal of the European Society of Cardiology · 2026Article
- Pericoronary Radiomics Signature for Non-Culprit Lesion Progression and Revascularization Decision in NSTE-ACS.Diagnostics (Basel, Switzerland) · 2026Article
- Application of fractal dimension combined with radiomics and clinical scores in recurrence prediction of atrial fibrillation after radiofrequency ablation.Frontiers in cardiovascular medicine · 2026Article
- Cardiac CT for personalized phenotyping in stable coronary artery disease: toward precision medicine.BJR open · 2026Review
- A CCTA coronary plaque radiomic model for predicting major adverse cardiovascular events in patients with coronary artery disease.Frontiers in cardiovascular medicine · 2026Article
- Interpretable machine learning for detecting symptomatic patients with carotid atherosclerosis on computed tomography angiography: a retrospective diagnostic study.BMC medical imaging · 2025Article
- Viability Test in Prediction of Response to Cardiac Resynchronization Therapy.Journal of clinical medicine · 2025Article
- Advanced Detection and Therapeutic Monitoring of Atherosclerotic Plaque Using CD36-Targeted Lipid Core Probe.Pharmaceutics · 2025Article
- Emerging Applications of Positron Emission Tomography in Coronary Artery Disease.Journal of personalized medicine · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundCoronary computed tomography (CT) angiography-derived attenuation-based plaque burden assessments can identify patients at risk of myocardial infarction.
objectivesThis study sought to assess whether more detailed plaque morphology assessment using patient-based radiomic characterization could further enhance the identification of patients at risk of myocardial infarction during long-term follow-up.
methodsPost hoc analysis of coronary CT angiography was performed within the SCOT-HEART (Scottish Computed Tomography of the HEART) clinical trial. Coronary plaque segmentations were used to calculate plaque burdens and eigen radiomic features that described plaque morphology. Univariable and multivariable Cox proportional hazard models were used to evaluate the association between clinical and image-based features and fatal or nonfatal myocardial infarction, whereas Harrell's C-statistic and cumulative/dynamic area under the curve (AUC) values with cross-validation were used to evaluate prognostic performance.
resultsScans from 1,750 patients (aged 58 ± 9 years; 56% male) were analyzed. Over a median of 8.6 years of follow-up, 82 patients had a fatal or nonfatal myocardial infarction. Among the eigen radiomic features, 15 were associated with myocardial infarction in univariable analysis, and 8 features retained their association following adjustment for cardiovascular risk score and plaque burden metrics. Adding plaque burden metrics to a clinical model incorporating cardiovascular risk score, Agatston score and presence of obstructive coronary artery disease had similar prediction performance (C-statistic 0.70 vs 0.70), whereas further addition of eigen radiomic features improved model performance (C-statistic 0.74). In temporal analysis, the model including eigen radiomic features had higher cumulative/dynamic AUC values following the fifth year of follow-up.
conclusionsRadiomics-based precision phenotyping of coronary plaque morphology provided improvements to long-term prediction of myocardial infarction by CT angiography over and above clinical factors and plaque burden. (Scottish Computed Tomography of the HEART [SCOT-HEART]; NCT01149590).
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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.