ArticleQuantitative imaging in medicine and surgery2023
Computed tomography angiography-based radiomics model to identify high-risk carotid plaques.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in carotid research: a 25-year bibliometric analysis of global trends and future directions.Frontiers in artificial intelligence · 2026Pooled it
- Plaque-level machine-learning prediction of carotid plaque vulnerability on computed tomography angiography.Neuroradiology · 2026Article
- A radiomics model based on CTA subtraction imaging for differentiating symptomatic and asymptomatic carotid plaques: a retrospective study.BMC cardiovascular disorders · 2026Article
- Impact of Perivascular Adipose Tissue Attenuation Around the Aortic Valve on Clinical Outcomes After Transcatheter Aortic Valve Replacement.Journal of the American Heart Association · 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
- Interpretable machine learning for detecting symptomatic patients with carotid atherosclerosis on computed tomography angiography: a retrospective diagnostic study.BMC medical imaging · 2025Article
- Predicting symptomatic carotid artery plaques with radiomics-based carotid perivascular adipose tissue characteristics: a multicenter, multiclassifier study.BMC medical imaging · 2025Article
- MRI ensemble model of plaque and perivascular adipose tissue as PET-equivalent for identifying carotid atherosclerotic inflammation.EJNMMI research · 2025Article
- An explainable CT-based machine learning model integrating carotid plaque and perivascular adipose tissue for predicting symptomatic plaques.Frontiers in neurology · 2025Article
- Quantification of carotid artery plaque and peri-vascular adipose tissue attenuation on computed tomography.European heart journal. Imaging methods and practice · 2025Article
- Prediction of solid pseudopapillary tumor invasiveness of the pancreas based on multiphase contrast-enhanced CT radiomics nomogram.Frontiers in oncology · 2025Article
- 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
- Evaluating the association between vascular remodeling and plaque calcification patterns of the carotid artery and its effects on ischemic symptoms using CT angiography.Cardiovascular diagnosis and therapy · 2024Article
- Initial experience with radiomics of carotid perivascular adipose tissue in identifying symptomatic plaque.Frontiers in neurology · 2024Article
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Authors and funding
9 authors.
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Abstract
Background: Extracranial atherosclerosis is one of the major causes of stroke. Carotid computed tomography angiography (CTA) is a widely used imaging modality that allows detailed assessments of plaque characteristics. This study aimed to develop and test radiomics models of carotid plaques and perivascular adipose tissue (PVAT) to distinguish symptomatic from asymptomatic plaques and compare the diagnostic value between radiomics models and traditional CTA model. Methods: A total of 144 patients with carotid plaques were divided into symptomatic and asymptomatic groups. The traditional CTA model was built by the traditional radiological features of carotid plaques measured on CTA images which were screened by univariate analysis and multivariable logistic regression. We extracted and screened radiomics features from carotid plaques and PVAT. Then, a support vector machine was used for building plaque and PVAT radiomics models, as well as a combined model using traditional CTA features and radiomics features. The diagnostic value between radiomics models and traditional CTA model was compared in identifying symptomatic carotid plaques by Delong method. Results: The area under curve (AUC) values of traditional CTA model were 0.624 and 0.624 for the training and validation groups, respectively. The plaque radiomics model and PVAT radiomics model achieved AUC values of 0.766, 0.740 and 0.759, 0.618 in the two groups, respectively. Meanwhile, the combined model of plaque and PVAT radiomics features and traditional CTA features had AUC values of 0.883 and 0.840 for the training and validation groups, respectively, and the receiver operating characteristic curves of combined model were significantly better than those of traditional CTA model in the training group (P<0.001) and validation group (P=0.029). Conclusions: The combined model of the radiomics features of carotid plaques and PVAT and the traditional CTA features significantly contributes to identifying high-risk carotid plaques compared with traditional CTA model.
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