Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study

PurposeMachine learning (ML) models were constructed according to non-contrast computed tomography (NCCT) images as well as clinical and laboratory information to assess risk stratification for the occurrence of hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients.MethodsA retrosp...

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Opis bibliograficzny

Główni autorzy:	Yue Zhang, Gang Xie, Lingfeng Zhang, Junlin Li, Wuli Tang, Danni Wang, Ling Yang, Kang Li
Format:	Artigo
Język:	Inglês
Wydane:	Frontiers Media S.A. 2024-09-01
Seria:	Frontiers in Neurology
Hasła przedmiotowe:	radiomics non-contrast computed tomography machine learning acute ischemic stroke hemorrhagic transformation
Dostęp online:	https://www.frontiersin.org/articles/10.3389/fneur.2024.1413795/full
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