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

Shuffle-ResNet: Deep learning for predicting LGG IDH1 mutation from multicenter anatomical MRI sequences.

Biomedical physics & engineering express - 11 Nov 2022

Safari Mojtaba, Beiki Manjieh, Ameri Ahmad, Toudeshki Saeed Hosseini, Fatemi Ali, Archambault Louis

Abstract excerpt

Background and Purpose.The world health organization recommended to incorporate gene information such as isocitrate dehydrogenase 1 (IDH1) mutation status to improve prognosis, diagnosis, and treatment of the central nervous system tumors. We proposed our Shuffle Residual Network (Shuffle-ResNet) to predict IDH1 gene mutation status of the low grade glioma (LGG) tumors from multicenter anatomical magnetic...

Read the complete abstract on PubMed

Topics

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

Share to Brain Neoplasms
Share to Deep Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Disease Progression

Share to Glioma

Share to Isocitrate Dehydrogenase

Share to Magnetic Resonance Imaging

Share to Mutation

Source

Identifiers

PMID: 36326618
DOI: 10.1088/2057-1976/ac9fc8

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Shuffle-ResNet: Deep learning for predicting LGG IDH1 mutation from multicenter anatomical MRI sequences.PMID 36326618Read ↗

Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging.Semantic proximity Read ↗

A novel MRI-based deep learning networks combined with attention mechanism for predicting CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma.Semantic proximity Read ↗

Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data.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 ↗

Multicenter MRI Radiomics Features to Predict IHD1 Gene Mutation Status of Low-Grade GliomaSemantic proximity Read ↗

MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas.Semantic proximity Read ↗

Predicting Prognosis and IDH Mutation Status for Patients with Lower-Grade Gliomas Using Whole Slide ImagesSemantic proximity Read ↗

Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning.Semantic proximity Read ↗

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

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

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.Semantic proximity Read ↗

Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images.Semantic proximity Read ↗

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