Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment

Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment

BackgroundThis study aimed to develop a clinical-radiomics model using hyperattenuated imaging markers (HIM), characterized by hyperattenuation on head non-contrast computed tomography immediately after thrombectomy, to predict the risk of hemorrhagic transformation (HT) in patients undergoing endov...

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Bibliographic Details
Main Authors:	Sheng Hu, Junyu Liu, Jiayi Hong, Yuting Chen, Ziwen Wang, Jibo Hu, Shiying Gai, Xiaochao Yu, Jingjing Fu
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2025-02-01
Series:	Frontiers in Neurology
Subjects:	hemorrhagic transformation machine learning thrombectomy acute ischemic stroke multi-detector CT
Online Access:	https://www.frontiersin.org/articles/10.3389/fneur.2025.1471274/full
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