ArticleJournal of neuro-oncology2022
Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases.
Article in Journal of neuro-oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it, 37 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
- AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 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
- DriverNet: A Clinical and MRI-Based Framework for Noninvasive Pre-Treatment Molecular Triage in NSCLC Brain Metastases.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Review
- Can radiomics in brain magnetic resonance imaging predict the mutational status of primary lung cancer based on brain metastasis?Polish journal of radiology · 2025Article
- Brain metastasis magnetic resonance imaging-based deep learning for predicting epidermal growth factor receptor (Quantitative imaging in medicine and surgery · 2024Article
- Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition.Cancers · 2024Review
- A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.European radiology experimental · 2024Article
- A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis.Journal of translational medicine · 2023Article
- Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges.Archives of pharmacal research · 2023Review
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Radiomic Signatures for PredictingFrontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeNon-small cell lung cancer (NSCLC) tends to metastasize to the brain. Between 10 and 60% of NSCLCs harbor an activating mutation in the epidermal growth-factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, due to a high discordance rate between the molecular profile of the primary tumor and the brain metastases (BMs), identifying an individual patient's EGFR status of the BMs necessitates tissue diagnosis via an invasive surgical procedure. We employed a deep learning (DL) method with the aim of noninvasive detection of the EGFR mutation status in NSCLC BM.
methodsWe retrospectively collected clinical, radiological, and pathological-molecular data of all the NSCLC patients who had been diagnosed with BMs and underwent resection of their BM during 2009-2019. The study population was then divided into two groups based upon EGFR mutational status. We further employed a DL technique to classify the two groups according to their preoperative magnetic resonance imaging features. Augmentation techniques, transfer learning approach, and post-processing of the predicted results were applied to overcome the relatively small cohort. Finally, we established the accuracy of our model in predicting EGFR mutation status of BM of NSCLC.
resultsFifty-nine patients were included in the study, 16 patients harbored EGFR mutations. Our model predicted mutational status with mean accuracy of 89.8%, sensitivity of 68.7%, specificity of 97.7%, and a receiver operating characteristic curve value of 0.91 across the 5 validation datasets.
conclusionDL-based noninvasive molecular characterization is feasible, has high accuracy and should be further validated in large prospective cohorts.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.