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

Mining whole-brain information with deep learning to predict EGFR mutation and subtypes in brain-metastatic NSCLC: A multicenter study.

Medical physics - 1 Mar 2026

You Shuailin, Fan Ying, Zhang Jin, Yang Chunna, Sun Yiyao, Jiang Mingchen, Chen Huanhuan, Guo Wei, Yang Hong, Jiang Wenyan

Abstract excerpt

BACKGROUND: Epidermal growth factor receptor (EGFR) and its mutation subtypes play a pivotal role in the treatment of non-small cell lung cancer (NSCLC) patients. Therefore, developing an accurate, noninvasive quantitative method to predict EGFR genotype is crucial for personalized treatment. PURPOSE: To explore a deep learning-based method with whole-brain information for predicting EGFR mutation and subtypes...

Read the complete abstract on PubMed

Topics

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

Share to Brain
Share to Brain Neoplasms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Carcinoma, Non-Small-Cell Lung

Share to Data Mining

Share to Deep Learning

Share to ErbB Receptors

Share to Image Processing, Computer-Assisted

Share to Magnetic Resonance Imaging

Source

Identifiers

PMID: 41854843
DOI: 10.1002/mp.70398

Genes

EGFR

Variants

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Mining whole-brain information with deep learning to predict EGFR mutation and subtypes in brain-metastatic NSCLC: A multicenter study.PMID 41854843Read ↗

Brain metastasis magnetic resonance imaging-based deep learning for predicting epidermal growth factor receptor (EGFR) mutation and subtypes in metastatic non-small cell lung cancerSemantic proximity Read ↗

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.Semantic proximity Read ↗

Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patientsSemantic proximity Read ↗

Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases.Semantic proximity Read ↗

Predicting EGFR Mutation Status by a Deep Learning Approach in Patients with Non-Small Cell Lung Cancer Brain MetastasesSemantic proximity Read ↗

Predicting EGFR mutation status in non-small cell lung cancer patients with brain metastases based on MRI radiomics: A systematic review and meta-analysis.Semantic proximity Read ↗

A Bayesian meta-analysis on MRI-based radiomics for predicting EGFR mutation in brain metastasis of lung cancer.Semantic proximity Read ↗

Radiomics predicts the EGFR mutation and subtypes in metastatic lung adenocarcinoma: a multi-center studySemantic proximity Read ↗

Computed tomography-based radiomics and clinical-genetic features for brain metastasis prediction in patients with stage III/IV epidermal growth factor receptor-mutant non-small-cell lung cancer.Semantic proximity Read ↗

Deep Learning Predicts EGFR Mutation Status from Histology Images in Non-Small Cell Lung Cancer.Semantic proximity Read ↗

Genomic Analysis and Clinical Correlation of Non-Small Cell Lung Cancer with Special Reference to Brain Metastasis.Semantic proximity Read ↗

Radiomic signatures for the non-invasive prediction of EGFR mutation status in brain metastases of lung adenocarcinomaSemantic proximity Read ↗

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