ArticleScientific reports2020
Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 4 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 4 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- Pooled it
- A Bayesian meta-analysis on MRI-based radiomics for predicting EGFR mutation in brain metastasis of lung cancer.BMC medical imaging · 2025Pooled it
- [Clinical Practice Guidelines for the Management of Brain Metastases from Non-small Cell Lung Cancer with Actionable Gene Alterations in China (2025 Edition)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Guideline
- Diagnostic accuracy of MRI-based radiomic features for EGFR mutation status in non-small cell lung cancer patients with brain metastases: a meta-analysis.Frontiers in oncology · 2024Pooled it
- Predictive modeling of peripheral blood EGFR positivity in patients with advanced primary non-small cell lung cancer.Translational lung cancer research · 2026Article
- Emerging Trends in Artificial Intelligence in Neuro-Oncology.Current oncology reports · 2025Review
- Identification of the pathological subtypes of lung cancer brain metastases with multiparametric MRI radiomics: A feasibility study.Scientific reports · 2025Article
- Article
- Can radiomics in brain magnetic resonance imaging predict the mutational status of primary lung cancer based on brain metastasis?Polish journal of radiology · 2025Article
- Predicting the T790M mutation in non-small cell lung cancer (NSCLC) using brain metastasis MR radiomics: a study with an imbalanced dataset.Discover oncology · 2024Article
- MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Exploring the Molecular Tumor Microenvironment and Translational Biomarkers in Brain Metastases of Non-Small-Cell Lung Cancer.International journal of molecular sciences · 2024Review
- Article
- Enhancing intracranial efficacy prediction of osimertinib in non-small cell lung cancer: a novel approach through brain MRI radiomics.Frontiers in neurology · 2024Article
- Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.NPJ precision oncology · 2023Review
- Follicular carcinoma of the thyroid presenting as metastasis in the wall of an arachnoid cyst.BMJ case reports · 2023Article
- Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer.Journal of neuro-oncology · 2023Article
- Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma.Frontiers in oncology · 2023Article
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Current role of machine learning and radiogenomics in precision neuro-oncology.Exploration of targeted anti-tumor therapy · 2023Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of EGFR mutations is critical to the treatment of primary lung cancer and brain metastases (BMs). Here, we explored whether radiomic features of contrast-enhanced T1-weighted images (T1WIs) of BMs predict EGFR mutation status in primary lung cancer cases. In total, 1209 features were extracted from the contrast-enhanced T1WIs of 61 patients with 210 measurable BMs. Feature selection and classification were optimized using several machine learning algorithms. Ten-fold cross-validation was applied to the T1WI BM dataset (189 BMs for training and 21 BMs for the test set). Area under receiver operating characteristic curves (AUC), accuracy, sensitivity, and specificity were calculated. Subgroup analyses were also performed according to metastasis size. For all measurable BMs, random forest (RF) classification with RF selection demonstrated the highest diagnostic performance for identifying EGFR mutation (AUC: 86.81). Support vector machine and AdaBoost were comparable to RF classification. Subgroup analyses revealed that small BMs had the highest AUC (89.09). The diagnostic performance for large BMs was lower than that for small BMs (the highest AUC: 78.22). Contrast-enhanced T1-weighted image radiomics of brain metastases predicted the EGFR mutation status of lung cancer BMs with good diagnostic performance. However, further study is necessary to apply this algorithm more widely and to larger BMs.
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