Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability.

Abstract

To assess how radiomic features may be combined with plaque morphological and compositional features identified by multi-contrast magnetic resonance imaging (MRI) to improve upon conventional risk assessment models in determining culprit lesions. Fifty-five patients (mean age: 62.6; 35 males) with bilateral carotid stenosis who experienced transient ischaemic attack (TIA) or stroke were included from the CARE-II multi-centre carotid imaging trial (ClinicalTrials.gov Identifier: NCT02017756). They underwent MRI within 2 weeks of the event. Classification capability in distinguishing culprit lesions was assessed by machine learning. Repeatability and reproducibility of the results were investigated by assessing the robustness of the radiomic features. Radiomics combined with a relatively conventional plaque morphological and compositional metric-based model provided incremental value over a conventional model alone [area under curve (AUC), 0.819 ± 0.002 vs. 0.689 ± 0.019 respectively, p = 0.014]. The radiomic model alone also provided value over the conventional model [AUC, 0.805 ± 0.003 vs. 0.689 ± 0.019 respectively, p = 0.031]. T2-weighted imaging-based radiomic features had consistently higher robustness and classification capabilities compared with T1-weighted images. Higher-dimensional radiomic features outperformed first-order features. Grey Level Co-occurrence Matrix (GLCM), Grey Level Dependence Matrix (GLDM) and Grey Level Size Zone Matrix (GLSZM) sub-types were particularly useful in identifying textures which could detect vulnerable lesions. The combination of MRI-based radiomic features and lesion morphological and compositional parameters provided added value to the reference-standard risk assessment for carotid atherosclerosis. This may improve future risk stratification for individuals at risk of major adverse ischemic cerebrovascular events.