ArticleEuropean radiology2023
Radiomics features of pericoronary adipose tissue improve CT-FFR performance in predicting hemodynamically significant coronary artery stenosis.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Effect of perivascular adipose tissue on patency and maturation rate of arteriovenous fistula in hemodialysis patients.International urology and nephrology · 2026Observational
- Mediation of stenosis-induced ischemia by proximal peri-coronary adipose tissue: A cross-sectional study.Medicine · 2026Article
- Predicting recurrent chest pain in patients with myocardial bridging using explainable machine learning: a multicenter study integrating CT-derived functional and inflammatory imaging biomarkers.BMC medical imaging · 2026Article
- Combined lesion-specific pericoronary adipose tissue attenuation and triglyceride-glucose body mass index for improved risk stratification of major adverse cardiovascular events in patients with stable angina pectoris.Quantitative imaging in medicine and surgery · 2026Article
- Radiomics of Pericoronary Adipose Tissue and CT-FFR to Predict Major Adverse Cardiovascular Events in Patients with T2DM Complicated by CAD.Journal of cardiovascular translational research · 2026Article
- A new insight on imaging characteristics of pericoronary adipose tissue for cardiovascular risk.Cardiovascular ultrasound · 2026Review
- Automated deep learning-radiomics pipeline for non-calcified coronary plaque detection using non-contrast calcium score CT.Frontiers in cardiovascular medicine · 2026Article
- Incremental diagnostic value of coronary CT angiography-derived pericoronary adipose tissue radiomics for identifying hemodynamically significant coronary stenosis.Frontiers in cardiovascular medicine · 2026Article
- Article
- Radiomic phenotype of epicardial adipose tissue derived from coronary artery calcium score predicts myocardial ischemia.La Radiologia medica · 2025Article
- Computed tomography-based assessment of pericoronary adipose tissue in cardiovascular diseases: Diagnostic and prognostic implications.World journal of radiology · 2025Review
- Prediction of plaque progression using different machine learning models of pericoronary adipose tissue radiomics based on coronary computed tomography angiography.European journal of radiology open · 2025Article
- Multimodal prediction of major adverse cardiovascular events in hypertensive patients with coronary artery disease: integrating pericoronary fat radiomics, CT-FFR, and clinicoradiological features.La Radiologia medica · 2025Article
- Effect of systolic blood pressure status on coronary inflammation and high-risk plaque characteristics.World journal of diabetes · 2025Article
- Survival outcome prediction of esophageal squamous cell carcinoma patients based on radiomics and mutation signature.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Article
- Computed Tomography-Derived Fractional Flow Reserve: Developing A Gold Standard for Coronary Artery Disease Diagnostics.Reviews in cardiovascular medicine · 2024Review
- Assessing abdominal aortic aneurysm growth using radiomic features of perivascular adipose tissue after endovascular repair.Insights into imaging · 2024Article
- A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease.Tomography (Ann Arbor, Mich.) · 2024Article
- Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease.BMC medical imaging · 2024Article
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Abstract
objectivesTo evaluate the value of radiomics-based model of pericoronary adipose tissue (PCAT) combined with CT fractional flow reserve (CT-FFR) in predicting hemodynamically significant coronary stenosis.
methodsPatients with suspected or known coronary artery disease, who had coronary computed tomography angiography (CCTA), invasive coronary angiography (ICA), and FFR within 1 month, were retrospectively included. Radiomics features of lesion-based PCAT were extracted. The lesion-specific CT-FFR values, CCTA-derived diameter stenosis, lesion length, and PCAT attenuation were also measured. FFR values were used as the reference standard to assess the diagnostic performance of radiomics model, CT-FFR, and combined model for detection of flow-limiting stenosis.
resultsA total of 146 patients with 180 lesions were included in the study. All lesions were divided into training and validation cohorts at a ratio of 2:1. CT-FFR model exhibited the highest area under the curve (AUC) (0.803 for training, 0.791 for validation) in predicting hemodynamically significant stenosis, followed by radiomics model (0.776 for training, 0.744 for validation). However, no statistically significant difference was found between the AUCs of the above two models (p > 0.05). When CT-FFR was combined with radiomics model, the AUC reached 0.900 for training cohort and 0.875 for validation cohort, which were significantly higher than that of CT-FFR and radiomics model alone (both p < 0.05).
conclusionThe diagnostic performance of PCAT radiomics model was comparable to that of CT-FFR for identification of ischemic coronary stenosis. Adding PCAT radiomics model to CT-FFR showed incremental value in discriminating flow-limiting from non-flow-limiting lesions. KEY POINTS: • Radiomics analysis of lesion-based PCAT is potentially an alternative method to identify hemodynamic significance of coronary artery stenosis. • Adding radiomics model of PCAT to CT-FFR improved diagnostic performance for the detection of flow-limiting coronary stenosis. • Radiomics features + CT-FFR is a promising noninvasive method for comprehensive evaluation of hemodynamic significance of coronary artery stenosis.
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