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Abstract #4594

MRI Radiomics approach for Identification of high-risk intracranial plaque basing on machine learning

Haining Wei1, Fang Wu2, Jie Lu2, and Rui Li1
1Tsinghua University, Beijing, China, 2Xuanwu Hospital, Capital Medical University, Beijing, China


Intracranial atherosclerotic disease (ICAD) is one of the main causes of ischemic stroke. Increasing evidence supports that vulnerable plaque is correlated with risk for stroke, which reveals the importance of intracranial plaque risk identification. Radiomics is an automated and repeatable approach for extracting massive features for medical imaging. However, few articles have focused on radiomic-based studies of intracranial plaque of basilar artery and middle cerebral artery. In this study, we propose to build a high-risk intracranial plaque model using radiomics features and machine learning to differentiate symptomatic plaque from asymptomatic plaque, which is helpful in guiding clinical management.

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