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

Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases.

Magnetic resonance imaging - 1 Jun 2020

Chen Bihong T, Jin Taihao, Ye Ningrong, Mambetsariev Isa, Daniel Ebenezer, Wang Tao, Wong Chi Wah, Rockne Russell C, Colen Rivka, Holodny Andrei I, Sampath Sagus, Salgia Ravi

Abstract excerpt

Lung cancer metastases comprise most of all brain metastases in adults and most brain metastases are diagnosed by magnetic resonance (MR) scans. The purpose of this study was to conduct an MR imaging-based radiomic analysis of brain metastatic lesions from patients with primary lung cancer to classify mutational status of the metastatic disease. We retrospectively identified lung cancer patients with brain...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Anaplastic Lymphoma Kinase

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Area Under Curve

Share to Brain Neoplasms

Share to ErbB Receptors

Share to Magnetic Resonance Imaging

Share to Neoplasm Metastasis

Share to Proto-Oncogene Proteins p21(ras)

Source

Open in PubMed Open full text

Identifiers

PMID: 32179095
DOI: 10.1016/j.mri.2020.03.002
PMCID: PMC7237274

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases.PMID 32179095Read ↗

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

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

Differentiating EGFR from ALK mutation status using radiomics signature based on MR sequences of brain metastasis.Semantic proximity Read ↗

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

Distinguishing EGFR mutation molecular subtypes based on MRI radiomics features of lung adenocarcinoma brain metastases.Semantic proximity Read ↗

Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary 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 ↗

Multiparametric MRI-Based Radiomics Approaches for Preoperative Prediction of EGFR Mutation Status in Spinal Bone Metastases in Patients with Lung Adenocarcinoma.Semantic proximity Read ↗

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

MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients.Semantic proximity Read ↗

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

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