ArticleJournal of computer assisted tomography
Semiautomated Characterization of Carotid Artery Plaque Features From Computed Tomography Angiography to Predict Atherosclerotic Cardiovascular Disease Risk Score.
Article in Journal of computer assisted tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Quantification of carotid artery plaque and peri-vascular adipose tissue attenuation on computed tomography.European heart journal. Imaging methods and practice · 2025Article
- Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography.Circulation. Cardiovascular imaging · 2024Article
- Contrast-Enhanced Ultrasound Feasibility in Assessing Carotid Plaque Vulnerability-Narrative Review.Journal of clinical medicine · 2023Review
- Quantitative imaging biomarkers of coronary plaque morphology: insights from EVAPORATE.Frontiers in cardiovascular medicine · 2023Article
- Radiomics assessment of carotid intraplaque hemorrhage: detecting the vulnerable patients.Insights into imaging · 2022Article
- Article
- Identification Markers of Carotid Vulnerable Plaques: An Update.Biomolecules · 2022Review
- Correlation between computed tomography angiography and histology of carotid artery atherosclerosis: Can semi-automated imaging software predict a plaque's composition?Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2022Article
- Semiautomated carotid artery plaque composition: are intraplaque CT imaging features associated with cardiovascular risk factors?Neuroradiology · 2021Article
- Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application.Annals of translational medicine · 2021Review
- Article
- Virtual Transcriptomics: Noninvasive Phenotyping of Atherosclerosis by Decoding Plaque Biology From Computed Tomography Angiography Imaging.Arteriosclerosis, thrombosis, and vascular biology · 2021Article
- CT imaging features of carotid artery plaque vulnerability.Annals of translational medicine · 2020Review
- Carotid plaque imaging and the risk of atherosclerotic cardiovascular disease.Cardiovascular diagnosis and therapy · 2020Review
- Plaque imaging volume analysis: technique and application.Cardiovascular diagnosis and therapy · 2020Review
- Carotid Intraplaque-Hemorrhage Volume and Its Association with Cerebrovascular Events.AJNR. American journal of neuroradiology · 2019Article
- Carotid Plaque CTA Analysis in Symptomatic Subjects with Bilateral Intraparenchymal Hemorrhage: A Preliminary Analysis.AJNR. American journal of neuroradiology · 2019Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
purposeTo investigate whether selected carotid computed tomography angiography (CTA) quantitative features can predict 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores.
methodsOne hundred seventeen patients with calculated ASCVD risk scores were considered. A semiautomated imaging analysis software was used to segment and quantify plaque features. Eighty patients were randomly selected to build models using 14 imaging variables and the calculated ASCVD risk score as the end point (continuous and binarized). The remaining 37 patients were used as the test set to generate predicted ASCVD scores. The predicted and observed ASCVD risk scores were compared to assess properties of the predictive model.
resultsNine of 14 CTA imaging variables were included in a model that considered the plaque features in a continuous fashion (model 1) and 6 in a model that considered the plaque features dichotomized (model 2). The predicted ASCVD risk scores were 18.87% ± 13.26% and 18.39% ± 11.6%, respectively. There were strong correlations between the observed ASCVD and the predicted ASCVDs, with r = 0.736 for model 1 and r = 0.657 for model 2. The mean biases between observed ASCVD and predicted ASCVDs were -1.954% ± 10.88% and -1.466% ± 12.04%, respectively.
conclusionsSelected quantitative imaging carotid features extracted from the semiautomated carotid artery analysis can predict the ASCVD risk scores.
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Registered trials
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