MRI features predict p53 status in lower-grade gliomas via a machine-learning approach

BACKGROUND: P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images. METHODS: Preoperative MR images were retrospectively obt...

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Detalles Bibliográficos
Autores principales: Li, Yiming, Qian, Zenghui, Xu, Kaibin, Wang, Kai, Fan, Xing, Li, Shaowu, Jiang, Tao, Liu, Xing, Wang, Yinyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Regular Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5842645/
https://www.ncbi.nlm.nih.gov/pubmed/29527478
http://dx.doi.org/10.1016/j.nicl.2017.10.030

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5842645/
https://www.ncbi.nlm.nih.gov/pubmed/29527478
http://dx.doi.org/10.1016/j.nicl.2017.10.030

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