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

Deep learning radiomics nomograms predict Isocitrate dehydrogenase (IDH) genotypes in brain glioma: A multicenter study.

Magnetic resonance imaging - 1 Apr 2025

Li Darui, Hu Wanjun, Ma Laiyang, Yang Wenxia, Liu Yang, Zou Jie, Ge Xin, Han Yuping, Gan Tiejun, Cheng Dan, Ai Kai, Liu Guangyao, Zhang Jing

Abstract excerpt

PURPOSE: To explore the feasibility of Deep learning radiomics nomograms (DLRN) in predicting IDH genotype. METHODS: A total of 402 glioma patients from two independent centers were retrospectively included, and the data from center I was randomly divided into a training cohort (n = 239) and an internal validation cohort (n = 103) on a 7:3 basis. Center II served as an independent external validation cohort...

Read the complete abstract on PubMed

Topics

Humans
Isocitrate Dehydrogenase
Glioma
Deep Learning
Male
Female

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Nomograms

Middle Aged

Brain Neoplasms

Retrospective Studies

Adult

Genotype

Aged

Magnetic Resonance Imaging

Mutation

ROC Curve

Brain

Radiomics

Source

Identifiers

PMID: 39708927
DOI: 10.1016/j.mri.2024.110314

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 22 Oct 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Deep learning radiomics nomograms predict Isocitrate dehydrogenase (IDH) genotypes in brain glioma: A multicenter study.PMID 39708927Read ↗

Noninvasive prediction of IDH mutation status in gliomas using preoperative multiparametric MRI radiomics nomogram: A mutlicenter study.Semantic proximity Read ↗

From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas.Semantic proximity Read ↗

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

Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.Semantic proximity Read ↗

Development and validation of clinical-radiomics analysis for preoperative prediction of IDH mutation status and WHO grade in diffuse gliomas: a consecutive L-[methyl-11C] methionine cohort study with two PET scanners.Semantic proximity Read ↗

Predicting Isocitrate Dehydrogenase (IDH) Mutation Status in Gliomas Using Multiparameter MRI Radiomics Features.Semantic proximity Read ↗

Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas.Semantic proximity Read ↗

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.Semantic proximity Read ↗

Machine Learning-Based Radiomics for Molecular Subtyping of Gliomas.Semantic proximity Read ↗

Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma.Semantic proximity Read ↗

Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine LearningSemantic proximity Read ↗

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

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