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

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.

European radiology - 1 Jul 2020

Choi Yoon Seong, Ahn Sung Soo, Chang Jong Hee, Kang Seok-Gu, Kim Eui Hyun, Kim Se Hoon, Jain Rajan, Lee Seung-Koo

Abstract excerpt

BACKGROUND AND PURPOSE: Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features could improve the accuracy of survival predictions for lower grade gliomas over clinical and IDH status. MATERIALS AND METHODS: Radiomic features (n = 250) were...

Topics

Join the communities discussing this publication.

ROC Curve
Brain Neoplasms
Glioma
Isocitrate Dehydrogenase
Machine Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Magnetic Resonance Imaging

Predictive Value of Tests

Survival Analysis

Source

Identifiers

PMID: 32162004
DOI: 10.1007/s00330-020-06737-5

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Community interpretation

Discuss the evidence

Ask a question, add context or challenge an interpretation. Community activity is separate from scientific evidence strength.

0

discussions

0

replies

Citations encouraged

Claims remain attributable.

No discussion yet

Be the first to add a careful, cited interpretation.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.PMID 32162004Read ↗

Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma.Semantic proximity Read ↗

Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status.Semantic proximity Read ↗

Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach.Semantic proximity Read ↗

Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI.Semantic proximity Read ↗

Machine Learning-Based Radiomics for Molecular Subtyping of 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 ↗

Prognostic Value of Preoperative MRI Metrics for Diffuse Lower-Grade Glioma Molecular Subtypes.Semantic proximity Read ↗

Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma.Semantic proximity Read ↗

Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning.Semantic proximity Read ↗

MRI Features May Predict Molecular Features of Glioblastoma in Isocitrate Dehydrogenase Wild-Type Lower-Grade Gliomas.Semantic proximity Read ↗

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review.Semantic proximity Read ↗

MRI features predict survival and molecular markers in diffuse lower-grade gliomas.Semantic proximity Read ↗

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