Capabilities of 3D Mechanics and Radiomics Analysis in Predicting High-Risk Carotid Plaques

The objectives were to develop and evaluate a machine learning model based on a combination of biomechanics and image texture analysis, to improve the detection of high-risk carotid plaques. Sixty-five patients, who underwent high-resolution, multi-contrast, magnetic resonance imaging (MRI) of the carotid artery wall within two weeks of a TIA or stroke, were assessed. The MR images were provided by the CARE-II multi-centre carotid imaging trial (ClinicalTrials.gov Identifier: NCT02017756). Following 3D artery construction, parametric maps of structural stress, wall shear stress, and inward remodeling were computed using a one-way fluid structure interaction (FSI) approach. A radiomics pipeline was developed to derive image texture features from mechanics maps and the MR images. Machine learning models were then developed to distinguish non-culprit and culprit carotid plaques, where culprit plaques were deemed responsible for the symptoms associated with the TIA/stroke. The performance of a combined model, developed from the most predictive features of the mechanics map and MR image models, was compared with mechanics and MRI-based models individually. Mechanics [Accuracy = 0.68, AUC = 0.75 ± 0.03] and MRI-based models [Accuracy = 0.67, AUC = 0.71 ± 0.04] showed greater predictive capabilities for culprit lesions than the measurement of vessel stenosis alone [Accuracy = 0.62, AUC = 0.57 ± 0.04] (p < 0.001). The combined mechano-radiomics model [Accuracy = 0.76, AUC= 0.82 ± 0.04] (p=0.037) showed significant improvement in the prediction of culprit plaques compared with MRI and mechanics map features alone, as well as the clinically conventional measurement of vascular stenosis.