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

Predicting the T790M mutation in non-small cell lung cancer (NSCLC) using brain metastasis MR radiomics: a study with an imbalanced dataset

14 Sept 2024

Abstract excerpt

BACKGROUND: Early detection of T790M mutation in exon 20 of epidermal growth factor receptor (EGFR) in non-small cell lung cancer (NSCLC) patients with brain metastasis is crucial for optimizing treatment strategies. In this study, we developed radiomics models to distinguish NSCLC patients with T790M-positive mutations from those with T790M-negative mutations using multisequence MR images of brain metastasis...

Read the complete abstract on PubMed

Topics

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

Share to Brain Metastases and Treatment
Share to Lung Cancer Diagnosis and Treatment
Share to Radiomics and Machine Learning in Medical Imaging

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 39277568
DOI: 10.1007/s12672-024-01333-1

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

Predicting the T790M mutation in non-small cell lung cancer (NSCLC) using brain metastasis MR radiomics: a study with an imbalanced datasetPMID 39277568Read ↗

Predicting EGFR T790M Mutation in Brain Metastases Using Multisequence MRI-Based Radiomics Signature.Semantic proximity Read ↗

Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer.Semantic proximity Read ↗

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

Prediction of EGFR Mutation Status in Non–Small Cell Lung Cancer Based on Ensemble LearningSemantic proximity Read ↗

Prediction of EGFR mutation status in non-small cell lung cancer based on multiparametric MRI radiomics.Semantic 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 ↗

External Validation of a CT-Based Radiogenomics Model for the Detection of EGFR Mutation in NSCLC and the Impact of Prevalence in Model Building by Using Synthetic Minority Over Sampling (SMOTE): Lessons LearnedSemantic proximity Read ↗

MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting.Semantic proximity Read ↗

Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases.Semantic proximity Read ↗

Prediction of EGFR Mutation Status in Non-small Cell Lung Cancer Based on Multiparametric MRI RadiomicsSemantic proximity Read ↗

Radiomics signature of brain metastasis: prediction of EGFR mutation status.Semantic proximity Read ↗

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