Trial reportCardiovascular diabetology2023
Diagnosis of coronary artery disease in patients with type 2 diabetes mellitus based on computed tomography and pericoronary adipose tissue radiomics: a retrospective cross-sectional study.
Trial report in Cardiovascular diabetology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Difference of pericoronary adipose tissue attenuation between culprit and non-culprit lesions in acute coronary syndrome: a systematic review and meta-analysis.BMC cardiovascular disorders · 2026Pooled it
- Development of a predictive model for postoperative erectile function recovery in male patients with incomplete traumatic cervical spinal cord injury.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 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
- 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
- Analysis of epicardial adipose tissue in relation to arterial hypertension using radiomics in photon-counting CT.Frontiers in cardiovascular medicine · 2026Article
- Predicting major adverse cardiovascular events in diabetic and non-diabetic patients with coronary artery disease: visual models integrating multi-parametric coronary computed tomography angiography and pericoronary adipose tissue radiomics.Frontiers in cardiovascular medicine · 2026Article
- Association between abdominal muscle mass measured by dual-source computed tomography and coronary artery calcification in patients with Type 2 diabetes mellitus.Frontiers in endocrinology · 2026Article
- Article
- Radiomics analysis of pericoronary adipose tissue for detecting ischaemia with non-obstructive coronary arteries in NAFLD patients.BMC cardiovascular disorders · 2025Article
- Machine learning-based integration of pericoronary adipose tissue and clinical risk factors for cardiovascular risk prediction in type 2 diabetes: a retrospective cohort study.European journal of medical research · 2025Article
- Predictive Value of Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve for Survival in Patients with Esophageal Cancer Following Esophagectomy.Journal of gastrointestinal cancer · 2025Article
- Subtraction fractional flow reserve with computed tomography and pericoronary fat attenuation index enhances the identification of revascularization needs in patients.BMC medical imaging · 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
- Genetic Predisposition to Low-Density Lipoprotein Cholesterol and Incident Type 2 Diabetes.JAMA cardiology · 2025Article
- Pericoronary adipose tissue attenuation predicts compositional plaque changes: a 12-month longitudinal study in individuals with type 2 diabetes without symptoms or known coronary artery disease.Cardiovascular diabetology · 2025Observational
- 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
- Article
- Linking Diabetic Retinopathy Severity to Coronary Artery Disease Risk Factors in Type 2 Diabetic Patients.Cureus · 2024Article
- Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease.BMC medical imaging · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients with type 2 diabetes mellitus (T2DM) are highly susceptible to cardiovascular disease, and coronary artery disease (CAD) is their leading cause of death. We aimed to assess whether computed tomography (CT) based imaging parameters and radiomic features of pericoronary adipose tissue (PCAT) can improve the diagnostic efficacy of whether patients with T2DM have developed CAD.
methodsWe retrospectively recruited 229 patients with T2DM but no CAD history (146 were diagnosed with CAD at this visit and 83 were not). We collected clinical information and extracted imaging manifestations from CT images and 93 radiomic features of PCAT from all patients. All patients were randomly divided into training and test groups at a ratio of 7:3. Four models were constructed, encapsulating clinical factors (Model 1), clinical factors and imaging indices (Model 2), clinical factors and Radscore (Model 3), and all together (Model 4), to identify patients with CAD. Receiver operating characteristic curves and decision curve analysis were plotted to evaluate the model performance and pairwise model comparisons were performed via the DeLong test to demonstrate the additive value of different factors.
resultsIn the test set, the areas under the curve (AUCs) of Model 2 and Model 4 were 0.930 and 0.929, respectively, with higher recognition effectiveness compared to the other two models (each p < 0.001). Of these models, Model 2 had higher diagnostic efficacy for CAD than Model 1 (p < 0.001, 95% CI [0.129-0.350]). However, Model 4 did not improve the effectiveness of the identification of CAD compared to Model 2 (p = 0.776); similarly, the AUC did not significantly differ between Model 3 (AUC = 0.693) and Model 1 (AUC = 0.691, p = 0.382). Overall, Model 2 was rated better for the diagnosis of CAD in patients with T2DM.
conclusionsA comprehensive diagnostic model combining patient clinical risk factors with CT-based imaging parameters has superior efficacy in diagnosing the occurrence of CAD in patients with T2DM.
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