ArticleScientific reports2021
Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 48 citations in OpenAlex.
- 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
- Radiomics-based diagnosis of carotid artery stenosis using non-contrast CT: model development and validation.European journal of medical research · 2025Article
- Interpretable and reproducible machine learning model for coronary calcification and segment-level stenoses stratification on computed tomography angiography.BMC medicine · 2025Article
- Association of Radiomics and Pericarotid Adipose Tissue Characteristics with Systemic Inflammation in Patients Undergoing Carotid Endarterectomy.Journal of clinical medicine · 2025Article
- Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach.European journal of radiology open · 2024Article
- Enhancing Radiomics Reproducibility: Deep Learning-Based Harmonization of Abdominal Computed Tomography (CT) Images.Bioengineering (Basel, Switzerland) · 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
- Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI.Diagnostics (Basel, Switzerland) · 2024Review
- Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability.The British journal of radiology · 2024Article
- Quality assessment of radiomics models in carotid plaque: a systematic review.Quantitative imaging in medicine and surgery · 2024Review
- Machine Intelligence in Cerebrovascular and Endovascular Neurosurgery.Advances in experimental medicine and biology · 2024Review
- Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography : An Approach to Identify Symptomatic Plaques.Clinical neuroradiology · 2023Article
- Aortic Valve Calcium Score by Computed Tomography as an Adjunct to Echocardiographic Assessment-A Review of Clinical Utility and Applications.Journal of imaging · 2023Review
- Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability.Insights into imaging · 2023Article
- Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques.Frontiers in cardiovascular medicine · 2023Article
- Predicting transient ischemic attack risk in patients with mild carotid stenosis using machine learning and CT radiomics.Frontiers in neurology · 2023Article
- Identification of high-risk intracranial plaques with 3D high-resolution magnetic resonance imaging-based radiomics and machine learning.Journal of neurology · 2022Article
- Prediction of Carotid In-Stent Restenosis by Computed Tomography Angiography Carotid Plaque-Based Radiomics.Journal of clinical medicine · 2022Article
- A Radiomics Approach to Assess High Risk Carotid Plaques: A Non-invasive Imaging Biomarker, Retrospective Study.Frontiers in neurology · 2022Article
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Authors and funding
20 authors at 4 institutions in 2 countries.
Funding
Abstract
Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to these variations would be incorporated into predictive models, for good generalisability. We extracted 93 radiomic features from carotid artery computed tomography angiograms of 41 patients with cerebrovascular events. We tested feature robustness to region-of-interest perturbations, image pre-processing settings and quantisation methods using both single- and multi-slice approaches. We assessed the ability of the most robust features to identify culprit and non-culprit arteries using several machine learning algorithms and report the average area under the curve (AUC) from five-fold cross validation. Multi-slice features were superior to single for producing robust radiomic features (67 vs. 61). The optimal image quantisation method used bin widths of 25 or 30. Incorporating our top 10 non-redundant robust radiomics features into ElasticNet achieved an AUC of 0.73 and accuracy of 69% (compared to carotid calcification alone [AUC: 0.44, accuracy: 46%]). Our results provide key information for introducing carotid CT radiomics into clinical practice. If validated prospectively, our robust carotid radiomic set could improve stroke prediction and target therapies to those at highest risk.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.