7T-high resolution MRI-derived radiomic analysis for the identification of symptomatic intracranial atherosclerotic plaques.

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.