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

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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Bibliographic Details
Main Authors: Yiming Li, Zenghui Qian, Kaibin Xu, Kai Wang, Xing Fan, Shaowu Li, Tao Jiang, Xing Liu, Yinyan Wang
Format: Article
Language:English
Published: Elsevier 2018-01-01
Series:NeuroImage: Clinical
Online Access:http://www.sciencedirect.com/science/article/pii/S2213158217302723

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http://www.sciencedirect.com/science/article/pii/S2213158217302723

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