Trial reportJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2022
Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.
Trial report in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Quality assessment of radiomics research in cardiac CT: a systematic review.European radiology · 2022Pooled it
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Region guided mask R-CNN with Haralick ResNet fusion for accurate coronary artery disease detection in computed tomography angiography images.Scientific reports · 2026Article
- Artificial Intelligence in Cardiovascular Imaging: From Automated Acquisition to Precision Diagnostics and Clinical Decision Support.Medical sciences (Basel, Switzerland) · 2026Review
- Clinical application of cardiac computed tomography in cardiomyopathy.The international journal of cardiovascular imaging · 2026Review
- Cardiac CT for personalized phenotyping in stable coronary artery disease: toward precision medicine.BJR open · 2026Review
- Risk stratification study of non-calcified coronary plaques based on CT pericoronary adipose tissue measurement.The international journal of cardiovascular imaging · 2025Article
- Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Intraindividual reproducibility of myocardial radiomic features between energy-integrating detector and photon-counting detector CT angiography.European radiology experimental · 2024Article
- Radiomics-based discrimination of coronary chronic total occlusion and subtotal occlusion on coronary computed tomography angiography.BMC medical imaging · 2024Article
- Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment.La Radiologia medica · 2024Review
- Article
- A novel hybrid machine learning model for auxiliary diagnosing myocardial ischemia.Frontiers in cardiovascular medicine · 2024Article
- Unveiling the hidden link: deciphering the interplay between plaque characteristics and nuclear myocardial perfusion imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Coronary computed tomography angiography imaging features combined with computed tomography-fractional flow reserve, pericoronary fat attenuation index, and radiomics for the prediction of myocardial ischemia.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Detecting obstructive coronary artery disease with machine learning: rest-only gated single photon emission computed tomography myocardial perfusion imaging combined with coronary artery calcium score and cardiovascular risk factors.Quantitative imaging in medicine and surgery · 2023Article
- Radiomics analysis of contrast-enhanced T1W MRI: predicting the recurrence of acute pancreatitis.Scientific reports · 2023Article
- Radiomics in Cardiac Computed Tomography.Diagnostics (Basel, Switzerland) · 2023Review
- Nomogram prediction model of postoperative pneumonia in patients with lung cancer: A retrospective cohort study.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoronary computed tomography angiography (CCTA) is a well-established non-invasive diagnostic test for the assessment of coronary artery diseases (CAD). CCTA not only provides information on luminal stenosis but also permits non-invasive assessment and quantitative measurement of stenosis based on radiomics. PURPOSE: This study is aimed to develop and validate a CT-based radiomics machine learning for predicting chronic myocardial ischemia (MIS).
methodsCCTA and SPECT-myocardial perfusion imaging (MPI) of 154 patients with CAD were retrospectively analyzed and 94 patients were diagnosed with MIS. The patients were randomly divided into two sets: training (n = 107) and test (n = 47). Features were extracted for each CCTA cross-sectional image to identify myocardial segments. Multivariate logistic regression was used to establish a radiomics signature after feature dimension reduction. Finally, the radiomics nomogram was built based on a predictive model of MIS which in turn was constructed by machine learning combined with the clinically related factors. We then validated the model using data from 49 CAD patients and included 18 MIS patients from another medical center. The receiver operating characteristic curve evaluated the diagnostic accuracy of the nomogram based on the training set and was validated by the test and validation set. Decision curve analysis (DCA) was used to validate the clinical practicability of the nomogram.
resultsThe accuracy of the nomogram for the prediction of MIS in the training, test and validation sets was 0.839, 0.832, and 0.816, respectively. The diagnosis accuracy of the nomogram, signature, and vascular stenosis were 0.824, 0.736 and 0.708, respectively. A significant difference in the number of patients with MIS between the high and low-risk groups was identified based on the nomogram (P < .05). The DCA curve demonstrated that the nomogram was clinically feasible.
conclusionThe radiomics nomogram constructed based on the image of CCTA act as a non-invasive tool for predicting MIS that helps to identify high-risk patients with coronary artery disease.
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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 OpenQuestion graph.