ArticleMagnetic resonance imaging2020
Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases.
Article in Magnetic resonance imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 3 syntheses or guidelines pooled it, 56 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
- 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
- A nomogram prediction model for brain metastases in patients with lung adenocarcinoma.Oncology letters · 2026Article
- DriverNet: A Clinical and MRI-Based Framework for Noninvasive Pre-Treatment Molecular Triage in NSCLC Brain Metastases.Diagnostics (Basel, Switzerland) · 2026Article
- Novel paradigms in KRAS targeting: Unveiling strategies to combat drug resistance.Chinese medical journal · 2025Review
- Identification of the pathological subtypes of lung cancer brain metastases with multiparametric MRI radiomics: A feasibility study.Scientific reports · 2025Article
- Prediction of biomarkers for brain metastasis in nonsmall cell lung cancer based on transcriptome sequencing.Medicine · 2025Article
- Can radiomics in brain magnetic resonance imaging predict the mutational status of primary lung cancer based on brain metastasis?Polish journal of radiology · 2025Article
- Estimation of Tissue Permeability in MRI Images by Empirical Mode Decomposition Method.Journal of biomedical physics & engineering · 2024Article
- 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
- Comparison of clinical and MRI features of brain metastases between ALK+ and ALK- NSCLC.Cancer medicine · 2024Article
- Multisequence MRI-based radiomics signature as potential biomarkers for differentiating KRAS mutations in non-small cell lung cancer with brain metastases.European journal of radiology open · 2024Article
- Multisequence MRI-based radiomics nomogram for early prediction of osimertinib resistance in patients with non-small cell lung cancer brain metastases.European journal of radiology open · 2023Article
- Differential Distribution of Brain Metastases from Non-Small Cell Lung Cancer Based on Mutation Status.Brain sciences · 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
- A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based onJapanese journal of radiology · 2023Article
- Integration of artificial intelligence in lung cancer: Rise of the machine.Cell reports. Medicine · 2023Review
- Current role of machine learning and radiogenomics in precision neuro-oncology.Exploration of targeted anti-tumor therapy · 2023Review
- Prediction of adenocarcinoma and squamous carcinoma based on CT perfusion parameters of brain metastases from lung cancer: a pilot study.Frontiers in oncology · 2023Article
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
Abstract
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 metastases treated at our institution between 2009 and 2017 who underwent genotype testing of their primary lung cancer. Brain MR Images were used for segmentation of enhancing tumors and peritumoral edema, and for radiomic feature extraction. The most relevant radiomic features were identified and used with clinical data to train random forest classifiers to classify the mutation status. Of 110 patients in the study cohort (mean age 57.51 ± 12.32 years; M: F = 37:73), 75 had an EGFR mutation, 21 had an ALK translocation, and 15 had a KRAS mutation. One patient had both ALK translocation and EGFR mutation. Majority of radiomic features most relevant for mutation classification were textural. Model building using both radiomic features and clinical data yielded more accurate classifications than using either alone. For classification of EGFR, ALK, and KRAS mutation status, the model built with both radiomic features and clinical data resulted in area-under-the-curve (AUC) values based on cross-validation of 0.912, 0.915, and 0.985, respectively. Our study demonstrated that MR imaging-based radiomic analysis of brain metastases in patients with primary lung cancer may be used to classify mutation status. This approach may be useful for devising treatment strategies and informing prognosis.
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