Motivation: Reliable assessment of plaque vulnerability is crucial for identifying high-risk plaques and preventing stroke. Goal(s): To develop and validate a high-risk intracranial plaque classification model using a combination of morphological and signal features from Three-dimensional high-resolution magnetic resonance vessel wall imaging (3D HR-MRVWI) and machine learning. Approach: Using 3D HR-MRVWI, we extracted morphological and signal features, which were then input into support vector classifier (SVC) and logistic regression models to classify plaques as symptomatic or asymptomatic. Results: Adding signal features resulted in a notable increase in The Area Under the Curve(AUC) and accuracy compared to models using morphological features alone. Impact: This study validated the importance of signal features, such as enhancement degree, in identifying high-risk plaques, providing data support for future radiomic research. This approach aids in early screening and targeted preventive measures, potentially reducing the incidence of stroke.
Chen et al. (Tue,) studied this question.