Open AccessNoncontrast Computed Tomography-Based Radiomics Analysis in Discriminating Early Hematoma Expansion after Spontaneous Intracerebral Hemorrhage
Author(s) -
Zuwei Song,
Dajing Guo,
Zhuoyue Tang,
Huan Liu,
Xin Li,
Sha Luo,
Xiaomei Yao,
Wenlong Song,
Junjie Song,
Zhiming Zhou
Publication year - 2021
Publication title -
korean journal of radiology/korean journal of radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.08
H-Index - 57
eISSN - 2005-8330
pISSN - 1229-6929
DOI - 10.3348/kjr.2020.0254
Subject(s) - medicine , radiomics , logistic regression , intracerebral hemorrhage , receiver operating characteristic , radiology , hematoma , computed tomography , discriminative model , cohort , area under the curve , artificial intelligence , surgery , glasgow coma scale , computer science
To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH).