Classifying Ruptured Middle Cerebral Artery Aneurysms With a Machine Learning Based, Radiomics-Morphological Model: A Multicentral Study

Classifying Ruptured Middle Cerebral Artery Aneurysms With a Machine Learning Based, Radiomics-Morphological Model: A Multicentral Study

ObjectiveRadiomics and morphological features were associated with aneurysms rupture. However, the multicentral study of their predictive power for specific-located aneurysms rupture is rare. We aimed to determine robust radiomics features related to middle cerebral artery (MCA) aneurysms rupture an...

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Bibliographic Details
Main Authors: Dongqin Zhu, Yongchun Chen, Kuikui Zheng, Chao Chen, Qiong Li, Jiafeng Zhou, Xiufen Jia, Nengzhi Xia, Hao Wang, Boli Lin, Yifei Ni, Peipei Pang, Yunjun Yang
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-08-01
Series:Frontiers in Neuroscience
Subjects:
computed tomography angiography
decision support techniques
intracranial aneurysm
machine learning
middle cerebral artery
nomograms
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2021.721268/full

Internet

https://www.frontiersin.org/articles/10.3389/fnins.2021.721268/full

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