ArticleEuropean radiology2023
Incremental diagnostic value of radiomics signature of pericoronary adipose tissue for detecting functional myocardial ischemia: a multicenter study.
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 13 papers.
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13 citing papers in PubMed, 20 citations in OpenAlex.
- Mediation of stenosis-induced ischemia by proximal peri-coronary adipose tissue: A cross-sectional study.Medicine · 2026Article
- Radiomic Assessment of Epicardial Adipose Tissue for the Prediction of Non-Calcified Coronary Atherosclerotic Plaques.Journal of cardiovascular development and disease · 2026Article
- Emerging quantitative CCTA imaging biomarkers for cardiovascular risk stratification: a narrative review.Frontiers in cardiovascular medicine · 2026Review
- Risk stratification study of non-calcified coronary plaques based on CT pericoronary adipose tissue measurement.The international journal of cardiovascular imaging · 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
- Differentiation of non-ST-segment elevation myocardial infarction from unstable angina using coronary computed tomography angiography: the role of imaging features and pericoronary adipose tissue radiomics.Cardiology journal · 2025Article
- Radiomics parameters of epicardial adipose tissue predict mortality in acute pulmonary embolism.Respiratory research · 2024Article
- Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease.BMC medical imaging · 2024Article
- Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning.European radiology · 2024Article
- The Role of Epicardial Adipose Tissue in Acute Coronary Syndromes, Post-Infarct Remodeling and Cardiac Regeneration.International journal of molecular sciences · 2024Review
- Triglyceride glucose index is associated with functional coronary artery stenosis in hypertensive patients.Frontiers in endocrinology · 2024Article
- Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study.Bioengineering (Basel, Switzerland) · 2023Article
- Papillary-Muscle-Derived Radiomic Features for Hypertrophic Cardiomyopathy versus Hypertensive Heart Disease Classification.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
23 authors at 12 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo determine the incremental diagnostic value of radiomics signature of pericoronary adipose tissue (PCAT) in addition to the coronary artery stenosis and plaque characters for detecting hemodynamic significant coronary artery disease (CAD) based on coronary computed tomography angiography (CCTA).
methodsIn a multicenter trial of 262 patients, CCTA and invasive coronary angiography were performed, with fractional flow reserve (FFR) in 306 vessels. A total of 13 conventional quantitative characteristics including plaque characteristics (N = 10) and epicardial adipose tissue characteristics (N = 3) were obtained. A total of 106 radiomics features depicting the phenotype of the PCAT surrounding the lesion were calculated. All data were randomly split into a training dataset (75%) and a testing dataset (25%). Then three models (including the conventional model, the PCAT radiomics model, and the combined model) were established in the training dataset using multivariate logistic regression algorithm based on the conventional quantitative features and the PCAT radiomics features after dimension reduction.
resultsA total of 124/306 vessels showed functional ischemia (FFR ≤ 0.80). The radiomics model performed better in discriminating ischemia from non-ischemia than the conventional model in both training (area under the receiver operating characteristic (ROC) curve (AUC): 0.770 vs 0.732, p < 0.05) and testing datasets (AUC: 0.740 vs 0.696, p < 0.05). The combined model showed significantly better discrimination than the conventional model in both training (AUC: 0.810 vs 0.732, p < 0.05) and testing datasets (AUC: 0.809 vs 0.696, p < 0.05).
conclusionsThe PCAT radiomics model showed good performance in predicting myocardial ischemia. Addition of PCAT radiomics to lesion quantitative characteristics improves the predictive power of functionally relevant CAD. KEY POINTS: • Based on the plaque characteristics and EAT characteristics, the conventional model showed poor performance in predicting myocardial ischemia. • The PCAT radiomics model showed good prospect in predicting myocardial ischemia. • When combining the radiomics signature with the conventional quantitative features (including plaque features and EAT features), it showed significantly better performance in predicting myocardial ischemia.
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