ArticleClinical neuroradiology2023
Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography : An Approach to Identify Symptomatic Plaques.
Article in Clinical neuroradiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Plaque-Level Machine Learning Prediction of Intraplaque Hemorrhage in Carotid Arteries Using Computed Tomography Angiography.Clinical neuroradiology · 2026Article
- A radiomics model based on CTA subtraction imaging for differentiating symptomatic and asymptomatic carotid plaques: a retrospective study.BMC cardiovascular disorders · 2026Article
- Geometric and density relationships of calcification clusters in carotid atherosclerosis.The neuroradiology journal · 2026Article
- A fusion model of deep learning and conventional features based on computed tomography angiography of carotid plaque for predicting the risk of acute ischemic stroke.Quantitative imaging in medicine and surgery · 2026Article
- Machine Learning Applied to CT Radiomics Identifies Symptomatic Carotid Plaques.Current medical imaging · 2026Article
- Artificial intelligence in carotid computed tomography angiography plaque detection: Decade of progress and future perspectives.World journal of radiology · 2025Review
- Research on ischemic stroke risk assessment based on CTA radiomics and machine learning.BMC medical imaging · 2025Article
- Radiomics based on dual-energy CT virtual monoenergetic images to identify symptomatic carotid plaques: a multicenter study.Scientific reports · 2025Article
- Deep learning-based defacing tool for CT angiography: CTA-DEFACE.European radiology experimental · 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
- Identification of symptomatic carotid plaque by CTA-based radiomics: a multicenter study.Frontiers in neurologyArticle
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Authors and funding
14 authors at 4 institutions in 1 country.
Funding
Abstract
purposeTo develop and validate a combined model incorporating conventional clinical and imaging characteristics and radiomics signatures based on head and neck computed tomography angiography (CTA) to assess plaque vulnerability.
methodsWe retrospectively analyzed 167 patients with carotid atherosclerosis who underwent head and neck CTA and brain magnetic resonance imaging (MRI) within 1 month. Clinical risk factors and conventional plaque characteristics were evaluated, and radiomic features were extracted from the carotid plaques. The conventional, radiomics and combined models were developed using fivefold cross-validation. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses.
resultsPatients were divided into symptomatic (n = 70) and asymptomatic (n = 97) groups based on MRI results. Homocysteine (odds ratio, OR 1.057; 95% confidence interval, CI 1.001-1.116), plaque ulceration (OR 6.106; 95% CI 1.933-19.287), and carotid rim sign (OR 3.285; 95% CI 1.203-8.969) were independently associated with symptomatic status and were used to construct the conventional model and s radiomic features were retained to establish the radiomics model. Radiomics scores incorporated with conventional characteristics were used to establish the combined model. The area under the ROC curve (AUC) of the combined model was 0.832, which outperformed the conventional (AUC = 0.767) and radiomics (AUC = 0.797) models. Calibration and decision curves analysis showed that the combined model was clinically useful.
conclusionRadiomics signatures of carotid plaque on CTA can well predict plaque vulnerability, which may provide additional value to identify high-risk patients and improve outcomes.
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