ArticleNeuroradiology2021
CT texture-based radiomics analysis of carotid arteries identifies vulnerable patients: a preliminary outcome study.
Article in Neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 32 citations in OpenAlex.
- Machine Learning Applied to CT Radiomics Identifies Symptomatic Carotid Plaques.Current medical imaging · 2026Article
- Radiomics-based diagnosis of carotid artery stenosis using non-contrast CT: model development and validation.European journal of medical research · 2025Article
- Impact of deep learning based reconstruction algorithms on CT radiomic features of carotid plaques.Journal of applied clinical medical physics · 2025Article
- Research on ischemic stroke risk assessment based on CTA radiomics and machine learning.BMC medical imaging · 2025Article
- Classification of carotid artery plaques: promising alternative methods to computed tomography angiography through radiomics approach using neck non-contrast computed tomography.Quantitative imaging in medicine and surgery · 2025Article
- An explainable CT-based machine learning model integrating carotid plaque and perivascular adipose tissue for predicting symptomatic plaques.Frontiers in neurology · 2025Article
- Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach.European journal of radiology open · 2024Article
- Article
- Quality assessment of radiomics models in carotid plaque: a systematic review.Quantitative imaging in medicine and surgery · 2024Review
- Explore the value of carotid ultrasound radiomics nomogram in predicting ischemic stroke risk in patients with type 2 diabetes mellitus.Frontiers in endocrinology · 2024Article
- Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography : An Approach to Identify Symptomatic Plaques.Clinical neuroradiology · 2023Article
- Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis.Scientific reports · 2023Article
- Predicting transient ischemic attack risk in patients with mild carotid stenosis using machine learning and CT radiomics.Frontiers in neurology · 2023Article
- Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability.Frontiers in neurology · 2023Article
- Radiomics assessment of carotid intraplaque hemorrhage: detecting the vulnerable patients.Insights into imaging · 2022Article
- Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI.Diagnostics (Basel, Switzerland) · 2022Review
- CT angiography-based radiomics as a tool for carotid plaque characterization: a pilot study.La Radiologia medica · 2022Article
- Prediction of Carotid In-Stent Restenosis by Computed Tomography Angiography Carotid Plaque-Based Radiomics.Journal of clinical medicine · 2022Article
- Computed Tomography Texture Analysis of Carotid Plaque as Predictor of Unfavorable Outcome after Carotid Artery Stenting: A Preliminary Study.Diagnostics (Basel, Switzerland) · 2021Article
- Advanced Computational Methods for Oncological Image Analysis.Journal of imaging · 2021Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo assess the potential role of computed tomography (CT) texture analysis (CTTA) in identifying vulnerable patients with carotid artery atherosclerosis.
methodsIn this case-control pilot study, 12 patients with carotid atherosclerosis and a subsequent history of transient ischemic attack or stroke were age and sex matched with 12 control cases with asymptomatic carotid atherosclerosis (follow-up time 103.58 ± 9.2 months). CTTA was performed using a commercially available research software package (TexRAD) by an operator blinded to clinical data. CTTA comprised a filtration-histogram technique to extract features at different scales corresponding to spatial scale filter (fine = 2 mm, medium = 3 mm, coarse = 4 mm), followed by quantification using histogram-based statistical parameters: mean, kurtosis, skewness, entropy, standard deviation, and mean value of positive pixels. A single axial slice was selected to best represent the largest cross-section of the carotid bifurcation or the greatest degree of stenosis, in presence of an atherosclerotic plaque, on each side.
resultsCTTA revealed a statistically significant difference in skewness between symptomatic and asymptomatic patients at the medium (0.22 ± 0.35 vs - 0.18 ± 0.39, p < 0.001) and coarse (0.23 ± 0.22 vs 0.03 ± 0.29, p = 0.003) texture scales. At the fine-texture scale, skewness (0.20 ± 0.59 vs - 0.18 ± 0.58, p = 0.009) and standard deviation (366.11 ± 117.19 vs 300.37 ± 82.51, p = 0.03) were significant before correction.
conclusionOur pilot study highlights the potential of CTTA to identify vulnerable patients in stroke and TIA. CT texture may have the potential to act as a novel risk stratification tool in patients with carotid atherosclerosis.
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