ArticleCancer medicine2019
Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning.
Article in Cancer medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 3 of them syntheses that pooled it.
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Who cites it
63 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.European radiology · 2026Pooled it
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 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
- Virtual clinical trial based on outcome modeling with iteratively redistributed extrapolation data.Radiological physics and technology · 2023Trial
- An explainable multimodal 2.5D deep learning-radiomics model for predicting extranodal extension in lung adenocarcinoma using preoperative CT: a multicenter retrospective cohort study.BMC medical imaging · 2026Article
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- Comprehensive machine learning analysis of a radiomics-based model for predicting microsatellite instability in right Colon Cancer.Frontiers in oncology · 2026Article
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- An Integrated Clinical-Radiomics-Deep Learning Model Based onCancer medicine · 2025Article
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- Integration of 2D/3D deep learning and radiomics for predicting lymphovascular invasion in T1-stage invasive lung adenocarcinoma: a multicenter study.Frontiers in oncology · 2025Article
- Machine learning-based integration of DCE-MRI radiomics for STAT3 expression prediction and survival stratification in breast cancer.Frontiers in immunology · 2025Article
- Predicting Ki-67 expression levels in non-small cell lung cancer using an explainable CT-based deep learning radiomics model.Frontiers in oncology · 2025Article
- A meta-learning-based robust federated learning for diagnosing lung adenocarcinoma and tuberculosis granulomas.Frontiers in oncology · 2025Article
- CT-based radiomics and deep learning to predict EGFR mutation status in lung adenocarcinoma.Frontiers in oncology · 2025Article
- Preoperative assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Non-invasive, fast, and high-performance EGFR gene mutation prediction method based on deep transfer learning and model stacking for patients with Non-Small Cell Lung Cancer.European journal of radiology open · 2024Article
- Review
- Prediction of epidermal growth factor receptor (Quantitative imaging in medicine and surgery · 2024Article
- Brain metastasis magnetic resonance imaging-based deep learning for predicting epidermal growth factor receptor (Quantitative imaging in medicine and surgery · 2024Article
3 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To develop a deep learning system based on 3D convolutional neural networks (CNNs), and to automatically predict EGFR-mutant pulmonary adenocarcinoma in CT images. A dataset of 579 nodules with EGFR mutation status labels of mutant (Mut) or wild-type (WT) was retrospectively analyzed. A deep learning system, namely 3D DenseNets, was developed to process 3D patches of nodules from CT data, and learn strong representations with supervised end-to-end training. The 3D DenseNets were trained with a training subset of 348 nodules and tuned with a development subset of 116 nodules. A strong data augmentation technique, mixup, was used for better generalization. We evaluated our model on a holdout subset of 115 nodules. An independent public dataset of 37 nodules from the cancer imaging archive (TCIA) was also used to test the generalization of our method. Conventional radiomics analysis was also performed for comparison. Our method achieved promising performance on predicting EGFR mutation status, with AUCs of 75.8% and 75.0% for our holdout test set and public test set, respectively. Moreover, strong relations were found between deep learning feature and conventional radiomics, while deep learning worked through an enhanced radiomics manner, that is, deep learned radiomics (DLR), in terms of robustness, compactness and expressiveness. The proposed deep learning system predicts EGFR-mutant of lung adenocarcinomas in CT images noninvasively and automatically, indicating its potential to help clinical decision-making by identifying eligible patients of pulmonary adenocarcinoma for EGFR-targeted therapy.
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