ArticleTranslational lung cancer research2025
Deep learning in histopathology images for prediction of oncogenic driver molecular alterations in lung cancer: a systematic review and meta-analysis.
Article in Translational lung cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Robust artificial intelligence frameworks for lung cancer subtyping and malignancy detection on thoracic CT.Scientific reports · 2026Article
- Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.BMC cancer · 2026Article
- Charting the blueprint: a bibliometric analysis reveals future strategies in lung cancer targeted therapy (2003-2025).Journal of thoracic disease · 2026Article
- Artificial Intelligence inCancers · 2026Review
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
Background: Lung cancer (LC) is the second most diagnosed cancer and the leading cause of cancer mortality worldwide. Non-small cell lung cancer (NSCLC) accounts for 85% of cases, with oncogenic alterations like Methods: A systematic review registered in PROSPERO (CRD42024573602) was conducted in Embase, LILACS, Medline, Web of Science, and Cochrane to identify studies on DL models using H&E slides for LC gene alterations. Only English and Spanish studies were included. Key metrics were extracted for meta-analysis. Studies without LC-specific data, missing essential metrics, or with inconsistent results were excluded. Results: We found evidence that convolutional neural networks (CNNs) were the most common architectures in studies. Also, in the meta-analysis, Conclusions: Our results emphasize the potential of these models as screening tools despite H&E WSI.It is necessary to validate these predictive models among diverse populations and clinical outcomes. This approach is crucial and leaves an open door for advances in precision medicine, offering promising avenues for personalized treatment strategies.
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Registered trials
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