Trial reportEuropean radiology2018
Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study.
Trial report in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
99 citing papers in PubMed, 3 syntheses or guidelines pooled it, 166 citations in OpenAlex.
- Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques.BMC cardiovascular disorders · 2023Pooled it
- Functional Coronary Artery Assessment: a Systematic Literature Review.Wiener klinische Wochenschrift · 2022Pooled it
- Development and application of artificial intelligence in cardiac imaging.The British journal of radiology · 2020Pooled it
- Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow.Circulation. Cardiovascular imaging · 2022Trial
- Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Trial
- Cardiac Phase Space Tomography: A novel method of assessing coronary artery disease utilizing machine learning.PloS one · 2018Trial
- Effects of Pulsatile Flow on Fractional Flow Reserve Assessed Using a Reduced-Order Model.Annals of biomedical engineering · 2026Article
- Artificial Intelligence-Driven Fractional Flow Reserve Assessment: Technical Foundations, Clinical Insights, and Future Directions.Medicina (Kaunas, Lithuania) · 2026Review
- Artificial intelligence-enabled plaque characterization from coronary computed tomography establishes basis of angina in women with nonobstructive atherosclerosis.Communications medicine · 2026Article
- A leap into the future: excluding the ischaemic origin of chest pain through artificial intelligence.European heart journal supplements : journal of the European Society of Cardiology · 2026Article
- Mechanisms, precision therapies, and technological frontiers in coronary atherosclerosis: a comprehensive review.Acta pharmacologica Sinica · 2026Review
- Design and rationale of the WARRIOR ancillary study for coronary CT angiographic analysis.American heart journal · 2026Article
- Quantitative assessment of coronary peri-vascular fat attenuation index using dual-layer spectral CT for differentiating high-risk plaques in coronary artery disease.BMC medical imaging · 2026Article
- Automatic segmentation of coronary plaques in coronary CT angiography using neural networks.PloS one · 2026Article
- Cardiac CT for personalized phenotyping in stable coronary artery disease: toward precision medicine.BJR open · 2026Review
- Cardiac CT in Non-Obstructive Coronary Artery Disease (NOCAD): A Literature Review.Journal of clinical medicine · 2025Review
- The plaque factor: Can coronary CT angiography plaque analysis link stenosis and ischemia?European radiology · 2025Article
- Performance assessment of computed tomographic angiography fractional flow reserve using deep learning: SMART trial summary.Journal of geriatric cardiology : JGC · 2025Article
- Clinical Applications of Cardiac Computed Tomography: A Focused Review for the Clinical Cardiologists.Journal of cardiovascular development and disease · 2025Review
- Illicit Anabolic Steroid Use and Cardiovascular Status in Men and Women.JAMA network open · 2025Article
39 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 3 institutions in 3 countries.
Funding
Abstract
objectivesWe aimed to investigate if lesion-specific ischaemia by invasive fractional flow reserve (FFR) can be predicted by an integrated machine learning (ML) ischaemia risk score from quantitative plaque measures from coronary computed tomography angiography (CTA).
methodsIn a multicentre trial of 254 patients, CTA and invasive coronary angiography were performed, with FFR in 484 vessels. CTA data sets were analysed by semi-automated software to quantify stenosis and non-calcified (NCP), low-density NCP (LD-NCP, < 30 HU), calcified and total plaque volumes, contrast density difference (CDD, maximum difference in luminal attenuation per unit area) and plaque length. ML integration included automated feature selection and model building from quantitative CTA with a boosted ensemble algorithm, and tenfold stratified cross-validation.
resultsEighty patients had ischaemia by FFR (FFR ≤ 0.80) in 100 vessels. Information gain for predicting ischaemia was highest for CDD (0.172), followed by LD-NCP (0.125), NCP (0.097), and total plaque volumes (0.092). ML exhibited higher area-under-the-curve (0.84) than individual CTA measures, including stenosis (0.76), LD-NCP volume (0.77), total plaque volume (0.74) and pre-test likelihood of coronary artery disease (CAD) (0.63); p < 0.006.
conclusionsIntegrated ML ischaemia risk score improved the prediction of lesion-specific ischaemia by invasive FFR, over stenosis, plaque measures and pre-test likelihood of CAD. KEY POINTS: • Integrated ischaemia risk score improved prediction of ischaemia over quantitative plaque measures • Integrated ischaemia risk score showed higher prediction of ischaemia than standard approach • Contrast density difference had the highest information gain to identify lesion-specific ischaemia.
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Registered trials
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.