ArticleInterventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences2024
7T-high resolution MRI-derived radiomic analysis for the identification of symptomatic intracranial atherosclerotic plaques.
Article in Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- 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
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Abstract
introductionHigh-resolution magnetic resonance imaging (HR-MRI) allows for detailed visualization of intracranial atherosclerotic plaques. Radiomics can be used as a tool for objective quantification of the plaque's characteristics. We analyzed the radiomics features (RFs) obtained from 7 T HR-MRI of patients with intracranial atherosclerotic disease (ICAD) to determine distinct characteristics of culprit and non-culprit plaques.
methodsPatients with stroke due to ICAD underwent HR-MRI. Culprit plaques in the vascular territory of the stroke were identified. Degree of stenosis, area degree of stenosis and plaque burden were calculated. A three-dimensional segmentation of the plaque was performed, and RFs were obtained. A machine learning model for prediction and identification of culprit plaques using significantly different RFs was evaluated.
resultsThe study included 33 patients with ICAD as stroke etiology. Univariate analysis revealed 24 RFs in pre-contrast MRI, 21 in post-contrast MRI, 13 RFs that were different between pre and post contrast MRIs. Additionally, six shape-based RFs significantly differed from culprit and non-culprit plaques. The random forest model achieved an accuracy rate of 81% (88% sensitivity and 75% specificity) in identifying culprit plaques in the independent testing dataset. This model successfully identified the culprit plaques in all patients during the testing phase. DISCUSSION: Symptomatic plaques had a distinct signature RFs compared to other plaques within the same subject. A machine learning model built with RFs successfully identified the symptomatic atherosclerotic plaques in most cases. Radiomics is a promising tool for stratification of plaques in patients with ICAD.
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