ArticleJournal of neurology2022
Identification of high-risk intracranial plaques with 3D high-resolution magnetic resonance imaging-based radiomics and machine learning.
Article in Journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- 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
- Parent Artery Disease-Related Stroke: What Is the Impact on Endovascular Treatment? A Narrative Review.Journal of clinical medicine · 2026Review
- High-resolution MRI-based Radiomics and Machine Learning for Identification of Middle Cerebral Artery Vulnerable PlaquesCurrent medical imaging · 2026Article
- Feasibility Exploration of High-Resolution MRI Plaque Features for Assessing Outcomes of Intracranial Angioplasty and Stenting in Ischemic Stroke Patients.Revista de neurologia · 2025Article
- High-Resolution Magnetic Resonance Imaging Radiomics for Identifying High-Risk Intracranial Plaques.Translational stroke research · 2025Article
- Predicting periprocedural complications risk in intracranial angioplasty and stenting from integrated high-resolution vessel wall imaging radiomics and clinical characteristics.Neuroradiology · 2025Article
- MRI-based habitat radiomics combined with vision transformer for identifying vulnerable intracranial atherosclerotic plaques and predicting stroke events: a multicenter, retrospective study.EClinicalMedicine · 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
- Distinguishing stroke from transient ischemic attack using plaque characteristics and arterial transit artifacts.Frontiers in neurology · 2025Article
- An explainable CT-based machine learning model integrating carotid plaque and perivascular adipose tissue for predicting symptomatic plaques.Frontiers in neurology · 2025Article
- Correlation Between Cognitive Impairment and Lenticulostriate Arteries: A Clinical and Radiomics Analysis.Journal of imaging informatics in medicine · 2024Article
- Review
- Risk prediction of CISS classification in endovascular treatment of basilar artery stenosis.Heliyon · 2024Article
- Initial experience with radiomics of carotid perivascular adipose tissue in identifying symptomatic plaque.Frontiers in neurology · 2024Article
- Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques.Frontiers in cardiovascular medicine · 2023Article
- Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI.Frontiers in neurology · 2023Article
- A radiomics-based approach with automated segmentation for identifying symptomatic basilar artery plaques in acute stroke.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundIdentifying high-risk intracranial plaques is significant for the treatment and prevention of stroke.
objectiveTo develop a high-risk plaque model using three-dimensional (3D) high-resolution magnetic resonance imaging (HRMRI) based radiomics features and machine learning.
methods136 patients with documented symptomatic intracranial artery stenosis and available HRMRI data were included. Among these patients, 136 and 92 plaques were identified as symptomatic and asymptomatic plaques, respectively. A conventional model was developed by recording and quantifying the radiological plaque characteristics. Radiomics features from T1-weighted images (T1WI) and contrast-enhanced T1WI (CE-T1WI) were used to construct a high-risk plaque model with linear support vector classification (linear SVC). The radiological and radiomics features were combined to build a combined model. Receiver operating characteristic (ROC) curves were used to evaluate these models.
resultsPlaque length, burden, and enhancement were independently associated with clinical symptoms and were included in the conventional model, which had an AUC of 0.853 vs. 0.837 in the training and test sets. While the radiomics and the combined model showed an improved AUC: 0.923 vs. 0.925 for the training sets and 0.906 vs. 0.903 in the test sets. Both the radiomics model (p = 0.024, p = 0.018) and combined model (p = 0.042, p = 0.049) outperformed the conventional model in the two sets, whereas the performance of the combined model was not significantly different from that of the radiomics model in the two sets (p = 0.583 and p = 0.606).
conclusionThe radiomics model based on 3D HRMRI can accurately differentiate symptomatic from asymptomatic intracranial arterial plaques and significantly outperforms the conventional model.
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Registered trials
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.