Journal article
7T-high resolution MRI-derived radiomic analysis for the identification of symptomatic intracranial atherosclerotic plaques
Interventional neuroradiology
08/30/2024
DOI: 10.1177/15910199241275722
PMCID: PMC11571523
PMID: 39210884
Abstract
Introduction High-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. Methods Patients 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. Results The 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.
Details
- Title: Subtitle
- 7T-high resolution MRI-derived radiomic analysis for the identification of symptomatic intracranial atherosclerotic plaques
- Creators
- Sebastian Sanchez - Yale UniversitySricharan Veeturi - University at Buffalo, State University of New YorkTatsat Patel - University at Buffalo, State University of New YorkDiego J Ojeda - University of IowaElena Sagues - University of IowaJacob M Miller - University of IowaVincent M Tutino - University at Buffalo, State University of New YorkEdgar A Samaniego - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Interventional neuroradiology
- DOI
- 10.1177/15910199241275722
- PMID
- 39210884
- PMCID
- PMC11571523
- NLM abbreviation
- Interv Neuroradiol
- ISSN
- 1591-0199
- eISSN
- 2385-2011
- Publisher
- SAGE PUBLICATIONS INC
- Grant note
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:This work was supported by the Common Fund, (grant number 201803866). This work was conducted on an MRI instrument funded by 1S10RR028821-01.
- Language
- English
- Electronic publication date
- 08/30/2024
- Academic Unit
- Neurology; Radiology; Iowa Neuroscience Institute; Neurosurgery
- Record Identifier
- 9984701856602771
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