Cargando…
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...
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: | |
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 |
Ejemplares similares
-
A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas
por: Liu, Xing, et al.
Publicado: (2018) -
Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction
por: Qian, Zenghui, et al.
Publicado: (2018) -
IDH mutation-specific radiomic signature in lower-grade gliomas
por: Liu, Xing, et al.
Publicado: (2019) -
ADAM9 Expression Is Associate with Glioma Tumor Grade and Histological Type, and Acts as a Prognostic Factor in Lower-Grade Gliomas
por: Fan, Xing, et al.
Publicado: (2016) -
Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas
por: Sun, Zhiyan, et al.
Publicado: (2019)