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Article

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

NeuroImage. Clinical - 1 Jan 2018

Li Yiming, Qian Zenghui, Xu Kaibin, Wang Kai, Fan Xing, Li Shaowu, Jiang Tao, Liu Xing, Wang Yinyan

Abstract excerpt

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 obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Identifiers

PMID: 29527478
DOI: 10.1016/j.nicl.2017.10.030
PMCID: PMC5842645

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

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