ArticleBMC cardiovascular disorders2024
Radiomics of pericoronary adipose tissue on computed tomography angiography predicts coronary heart disease in patients with type 2 diabetes mellitus.
Article in BMC cardiovascular disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
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9 citing papers in PubMed.
- Radiomic Features Extracted from Magnetic Resonance Imaging to Identify Arteriopathy in a Humanized Mouse Model of Sickle Cell Anemia and Sickle Cell Trait.Annals of biomedical engineering · 2026Article
- Role of artificial intelligence in developing predictive models for major adverse cardiovascular outcomes using CCTA adipose tissue characteristics: a systematic review and meta-analysis.European heart journal. Digital health · 2026Review
- Prediction of stroke events in patients with type 2 diabetes mellitus by interpretable machine learning based on perivascular adipose tissue features: a multicenter cohort study.Quantitative imaging in medicine and surgery · 2026Article
- Effects of dapagliflozin on myocardial motion parameters and cardiac electrophysiology in patients with type 2 diabetes Mellitus and coronary heart disease.Frontiers in cardiovascular medicine · 2026Article
- The application of coronary CT angiography combined with dynamic electrocardiogram in the diagnosis of myocardial ischemia in coronary heart disease.Frontiers in cardiovascular medicine · 2026Article
- CT-based radiomics nomogram of vertebral bodies and paravertebral muscles for differential diagnosis of type 2 diabetes mellitus.Frontiers in endocrinology · 2026Article
- Application of snake model imaging selection and analysis to the ultrasound diagnosis of white matter damage in preterm infants.BMC medical imaging · 2025Article
- Computed tomography-based assessment of pericoronary adipose tissue in cardiovascular diseases: Diagnostic and prognostic implications.World journal of radiology · 2025Review
- Correction: Radiomics of pericoronary adipose tissue on computed tomography angiography predicts coronary heart disease in patients with type 2 diabetes mellitus.BMC cardiovascular disorders · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
Abstract
backgroundDiabetes is a common chronic metabolic disease. The progression of the disease promotes vascular inflammation and the formation of atherosclerosis, leading to cardiovascular disease. The coronary artery perivascular adipose tissue attenuation index based on CCTA is a new noninvasive imaging biomarker that reflects the spatial changes in perivascular adipose tissue attenuation in CCTA images and the inflammation around the coronary arteries. In this study, a radiomics approach is proposed to extract a large number of image features from CCTA in a high-throughput manner and combined with clinical diagnostic data to explore the predictive ability of vascular perivascular adipose imaging data based on CCTA for coronary heart disease in diabetic patients.
methodsR language was used for statistical analysis to screen the variables with significant differences. A presegmentation model was used for CCTA vessel segmentation, and the pericoronary adipose region was screened out. PyRadiomics was used to calculate the radiomics features of pericoronary adipose tissue, and SVM, DT and RF were used to model and analyze the clinical data and radiomics data. Model performance was evaluated using indicators such as PPV, FPR, AAC, and ROC.
resultsThe results indicate that there are significant differences in age, blood pressure, and some biochemical indicators between diabetes patients with and without coronary heart disease. Among 1037 calculated radiomic parameters, 18.3% showed significant differences in imaging omics features. Three modeling methods were used to analyze different combinations of clinical information, internal vascular radiomics information and pericoronary vascular fat radiomics information. The results showed that the dataset of full data had the highest ACC values under different machine learning models. The support vector machine method showed the best specificity, sensitivity, and accuracy for this dataset.
conclusionsIn this study, the clinical data and pericoronary radiomics data of CCTA were fused to predict the occurrence of coronary heart disease in diabetic patients. This provides information for the early detection of coronary heart disease in patients with diabetes and allows for timely intervention and treatment.
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