Deep Learning Radiomics Signature from Multicontrast MRI for Automated Identification of Symptomatic Carotid Plaques: A Multicenter Study

BACKGROUND AND PURPOSE: Ischemic stroke poses a significant global health burden. Accurately identifying symptomatic carotid atherosclerotic plaques, beyond relying solely on stenosis degree, remains a critical challenge for precise stroke risk stratification. We aimed to develop and validate a deep learning radiomics (DLR) signature based on multicontrast MRI to identify symptomatic carotid plaques accurately. MATERIALS AND METHODS: In this retrospective multicenter study, 409 carotid arteries from 355 patients with carotid atherosclerosis were enrolled (219 training, 95 internal validation, 95 external test). Deep learning (DL) and radiomics features were extracted and combined from automatically segmented plaque regions on multicontrast MRI. The optimized DLR signature derived from a 3-stage feature selection pipeline was leveraged to train diverse machine learning classifiers for robust identification of symptomatic carotid plaques. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and compared against clinical models, radiomics-only models, and DL-only models. Subgroup analysis across stenosis severities and comparison of MRI-based American Heart Association lesion types between DLR-defined risk groups were performed. RESULTS: < .001) compared with asymptomatic groups. CONCLUSIONS: The developed multicontrast MRI-based DLR signature provides a highly accurate and robust tool for the automated identification of symptomatic carotid plaques, underscoring its potential value as a noninvasive tool to guide personalized stroke prevention strategies.