Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature.

European radiology - 1 Jul 2018

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

Abstract excerpt

OBJECTIVES: To predict ATRX mutation status in patients with lower-grade gliomas using radiomic analysis. METHODS: Cancer Genome Atlas (TCGA) patients with lower-grade gliomas were randomly allocated into training (n = 63) and validation (n = 32) sets. An independent external-validation set (n = 91) was built based on the Chinese Genome Atlas (CGGA) database. After feature extraction, an ATRX-related signature...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Brain
Share to Brain Neoplasms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Glioma

Share to Magnetic Resonance Imaging

Share to ROC Curve

Share to Support Vector Machine

Share to X-linked Nuclear Protein

Source

Identifiers

PMID: 29404769
DOI: 10.1007/s00330-017-5267-0

Genes

ATRX

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature.PMID 29404769Read ↗

ATRX status in patients with gliomas: Radiomics analysis.Semantic proximity Read ↗

A radiomics feature-based nomogram to predict telomerase reverse transcriptase promoter mutation status and the prognosis of lower-grade gliomas.Semantic proximity Read ↗

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

Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas.Semantic proximity Read ↗

Comparison of MRI Sequences to Predict ATRX Status Using Radiomics-Based Machine LearningSemantic proximity Read ↗

A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas.Semantic proximity Read ↗

Noninvasive Prediction of TERT Promoter Mutations in High-Grade Glioma by Radiomics Analysis Based on Multiparameter MRI.Semantic proximity Read ↗

Multimodal MRI-based radiomic nomogram for predicting telomerase reverse transcriptase promoter mutation in IDH-wildtype histological lower-grade gliomas.Semantic proximity Read ↗

Adding radiomics to the 2021 WHO updates may improve prognostic prediction for current IDH-wildtype histological lower-grade gliomas with known EGFR amplification and TERT promoter mutation status.Semantic proximity Read ↗

Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features.Semantic proximity Read ↗

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.Semantic proximity Read ↗

Radiomics improves the prognosis assessment of glioma recurrences: Focus on reliability analysis of MRI featuresSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.