ArticleEuropean radiology2018
Identification of high-risk plaque features in intracranial atherosclerosis: initial experience using a radiomic approach.
Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 5 of them syntheses that pooled it.
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Who cites it
57 citing papers in PubMed, 5 syntheses or guidelines pooled it, 77 citations in OpenAlex.
- Artificial intelligence diagnostic performance in image-based vulnerable carotid plaque detection: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- High-risk plaque characteristics associated with recurrent stroke in patients with intracranial stenosis: a systematic review and meta-analysis.Journal of neurology · 2025Pooled it
- Radiomics and ischemic stroke research: bibliometric insights and visual trends (2004-2024).Frontiers in neurology · 2025Pooled it
- Imaging endpoints of intracranial atherosclerosis using vessel wall MR imaging: a systematic review.Neuroradiology · 2021Pooled it
- Culprit intracranial plaque without substantial stenosis in acute ischemic stroke on vessel wall MRI: A systematic review.Atherosclerosis · 2019Pooled it
- Identification of high-risk carotid plaque with MRI-based radiomics and machine learning.European radiology · 2021Trial
- Expert consensus on intracranial vessel wall MRI in cerebrovascular disease: Society for Magnetic Resonance Angiography recommendations.European radiology · 2026Review
- Plaque features for the prediction of in-stent restenosis in symptomatic intracranial atherosclerotic stenosis from a retrospective single-center study.European radiology · 2026Article
- A hybrid model based on vessel wall magnetic resonance imaging predicts recurrence in posterior circulation ischemic stroke: a multi-institutional study.Journal of neurointerventional surgery · 2026Article
- Integration of Conventional and Radiomic Features From Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging for Multimodal Prediction of Symptomatic Carotid Atherosclerotic Plaques.Journal of the American Heart Association · 2026Article
- Multimodal Radiomics Model Combining HR-VWI and Clinical Features for Identifying Symptomatic Basilar Atherosclerotic Plaques.BioMed research international · 2026Article
- High-Resolution Magnetic Resonance Imaging Radiomics for Identifying High-Risk Intracranial Plaques.Translational stroke research · 2025Article
- MRI ensemble model of plaque and perivascular adipose tissue as PET-equivalent for identifying carotid atherosclerotic inflammation.EJNMMI research · 2025Article
- Atherosclerotic plaque evolution predicts cerebral ischemic events in patients with intracranial atherosclerosis: a multicentre longitudinal study using high-resolution MRI.European radiology · 2025Article
- A clinical-radiomics nomogram to predict early neurological deterioration in patients with stroke undergoing intravenous thrombolysis.Journal of the Chinese Medical Association : JCMA · 2025Article
- Carotid plaque segmentation and classification using MRI-based plaque texture analysis and convolutional neural network.Frontiers in medicine · 2025Article
- Intracranial stenting with the Neuroform Atlas Stent for symptomatic intracranial atherosclerotic stenosis: a bi-center retrospective analysis including stroke recurrence nomogram.Frontiers in neurology · 2025Article
- A radiomic model based on 7T intracranial vessel wall imaging for identification of culprit middle cerebral artery plaque associated with subcortical infarctions.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- A clinical-radiomics combined model based on carotid atherosclerotic plaque for prediction of ischemic stroke.Frontiers in neurology · 2024Article
- Artificial intelligence in ischemic stroke images: current applications and future directions.Frontiers in neurology · 2024Review
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 2 countries.
Funding
Abstract
objectivesTo evaluate a quantitative radiomic approach based on high-resolution magnetic resonance imaging (HR-MRI) to differentiate acute/sub-acute symptomatic basilar artery plaque from asymptomatic plaque.
methodsNinety-six patients with basilar artery stenosis underwent HR-MRI between January 2014 and December 2016. Patients were scanned with T1- and T2-weighted imaging, as well as T1 imaging following gadolinium-contrast injection (CE-T1). The stenosis value, plaque area/burden, lumen area, minimal luminal area (MLA), intraplaque haemorrhage (IPH), contrast enhancement ratio and 94 quantitative radiomic features were extracted and compared between acute/sub-acute and asymptomatic patients. Multi-variate logistic analysis and a random forest model were used to evaluate the diagnostic performance.
resultsIPH, MLA and enhancement ratio were independently associated with acute/subacute symptoms. Radiomic features in T1 and CE-T1 images were associated with acute/subacute symptoms, but the features from T2 images were not. The combined IPH, MLA and enhancement ratio had an area under the curve (AUC) of 0.833 for identifying acute/sub-acute symptomatic plaques, and the combined T1 and CE-T1 radiomic approach had a significantly higher AUC of 0.936 (p = 0.01). Combining all features achieved an AUC of 0.974 and accuracy of 90.5%.
conclusionsRadiomic analysis of plaque texture on HR-MRI accurately distinguished between acutely symptomatic and asymptomatic basilar plaques. KEY POINTS: • High-resolution magnetic resonance imaging can assess basilar artery atherosclerotic plaque. • Radiomic features in T1 and CE-T1 images are associated with acute symptoms. • Radiomic analysis can accurately distinguish between acute symptomatic and asymptomatic plaque. • The highest accuracy may be achieved by combining radiomic and conventional features.
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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.