ArticleScientific reports2023
Predicting EGFR mutational status from pathology images using a real-world dataset.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 32 citations in OpenAlex.
- Article
- Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2026Article
- Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.BMC cancer · 2026Article
- Inferring signaling pathway abnormalities from histopathological images via logic-constrained gene-pathway heterogeneous knowledge graph.npj biomedical innovations · 2026Article
- Validation of histopathology-based deep learning algorithms for detection of actionable non-small cell lung cancer biomarkers.NPJ precision oncology · 2026Article
- Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications.Cancers · 2026Review
- Deep Learning Predicts EGFR Mutation Status from Histology Images in Non-Small Cell Lung Cancer.Cancer research communications · 2025Article
- Multi-omics strategies for biomarker discovery and application in personalized oncology.Molecular biomedicine · 2025Review
- Deep Learning Model-Based Architectures for Lung Tumor Mutation Profiling: A Systematic Review.Cancers · 2025Review
- Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.Nature medicine · 2025Article
- EGFR Mutation Detection in Whole Slide Images of Non-Small Cell Lung Cancers Using a Two-Stage Deep Transfer Learning Approach.Cancer medicine · 2025Article
- AI-based detection of neutrophil dysplasia: an accessible and sensitive model for MDS diagnosis from peripheral blood.Annals of hematology · 2025Article
- Assessing genotype-phenotype correlations in colorectal cancer with deep learning: a multicentre cohort study.The Lancet. Digital health · 2025Article
- Deep learning in histopathology images for prediction of oncogenic driver molecular alterations in lung cancer: a systematic review and meta-analysis.Translational lung cancer research · 2025Article
- Clinicopathological Features of Non-Small Cell Lung Carcinoma with NRAS Mutation.Journal of personalized medicine · 2025Article
- Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models.Diagnostics (Basel, Switzerland) · 2025Article
- DeepSeek's impact on thoracic surgeons' work patterns-past, present and future.Journal of thoracic disease · 2025Article
- Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.The journal of pathology. Clinical research · 2024Article
- Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Article
- [A look into the neighboring discipline: eHealth in oncology].Chirurgie (Heidelberg, Germany) · 2024Review
Corrections and comments
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Authors and funding
15 authors at 1 institution.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Treatment of non-small cell lung cancer is increasingly biomarker driven with multiple genomic alterations, including those in the epidermal growth factor receptor (EGFR) gene, that benefit from targeted therapies. We developed a set of algorithms to assess EGFR status and morphology using a real-world advanced lung adenocarcinoma cohort of 2099 patients with hematoxylin and eosin (H&E) images exhibiting high morphological diversity and low tumor content relative to public datasets. The best performing EGFR algorithm was attention-based and achieved an area under the curve (AUC) of 0.870, a negative predictive value (NPV) of 0.954 and a positive predictive value (PPV) of 0.410 in a validation cohort reflecting the 15% prevalence of EGFR mutations in lung adenocarcinoma. The attention model outperformed a heuristic-based model focused exclusively on tumor regions, and we show that although the attention model also extracts signal primarily from tumor morphology, it extracts additional signal from non-tumor tissue regions. Further analysis of high-attention regions by pathologists showed associations of predicted EGFR negativity with solid growth patterns and higher peritumoral immune presence. This algorithm highlights the potential of deep learning tools to provide instantaneous rule-out screening for biomarker alterations and may help prioritize the use of scarce tissue for biomarker testing.
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Registered trials
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.