A clinical–radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study

OBJECTIVE: To build a clinical–radiomics model based on noncontrast computed tomography images to identify the risk of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) following intravenous thrombolysis (IVT). MATERIALS AND METHODS: A total of 517 consecutive patients wit...

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Detalles Bibliográficos
Autores principales: Ren, Huanhuan, Song, Haojie, Wang, Jingjie, Xiong, Hua, Long, Bangyuan, Gong, Meilin, Liu, Jiayang, He, Zhanping, Liu, Li, Jiang, Xili, Li, Lifeng, Li, Hanjian, Cui, Shaoguo, Li, Yongmei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050271/
https://www.ncbi.nlm.nih.gov/pubmed/36977913
http://dx.doi.org/10.1186/s13244-023-01399-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050271/
https://www.ncbi.nlm.nih.gov/pubmed/36977913
http://dx.doi.org/10.1186/s13244-023-01399-5

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