ArticleNature medicine2025
Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.
Article in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- Artificial intelligence in orthopaedics should be built with surgeons, not just for them!Journal of experimental orthopaedics · 2026Article
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- A Boveri perspective on cancer biomarker testing using artificial intelligence.NPJ precision oncology · 2026Review
- Guidance for laboratory implementation, governance and continuous assurance of artificial intelligence in histopathology.Virchows Archiv : an international journal of pathology · 2026Review
- End-to-end multimodal pathology foundation model with clinical dialogue.Nature medicine · 2026Article
- Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study.The journal of pathology. Clinical research · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Segment any tumour: an uncertainty-aware vision foundation model for whole-body analysis.Nature communications · 2026Article
- Genome-scale molecular profiling from routine histopathology with a multimodal pathology foundation model.Research square · 2026Article
- Regulatory science for AI-basedJournal of pathology informatics · 2026Article
- Dissecting and directing pathology foundation models.bioRxiv : the preprint server for biology · 2026Article
- Towards robust foundation models for digital pathology.Nature communications · 2026Article
- [Artificial intelligence-assisted diagnostics in the pathology of internal and oncological diseases].Innere Medizin (Heidelberg, Germany) · 2026Review
- What practicing pathologists and oncologists should know about the new computational pathology-based companion diagnostic tools.The Journal of pathology · 2026Review
- Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.Journal of thoracic disease · 2026Review
- Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective.Bioengineering (Basel, Switzerland) · 2026Review
- Cross-cancer homologous recombination deficiency prediction from whole slide images using transfer learning.Scientific reports · 2026Article
- From Passive Sampling to Precision Intervention: A Standardized Radiomics-Driven Workflow for Molecularly Adequate Lung Biopsy.Cardiovascular and interventional radiology · 2026Observational
- SpaHE-Infil: A spatial heterogeneity framework for decoding TME infiltration from H&E-stained slides.iScience · 2026Article
Corrections and comments
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Authors and funding
26 authors.
Funding
Abstract
Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic tools for patients with cancer. Despite their advantages, their clinical utility in real-world settings remains unproven. Assessing EGFR mutations in lung adenocarcinoma demands rapid, accurate and cost-effective tests that preserve tissue for genomic sequencing. PCR-based assays provide rapid results but with reduced accuracy compared with next-generation sequencing and require additional tissue. Computational biomarkers leveraging modern foundation models can address these limitations. Here we assembled a large international clinical dataset of digital lung adenocarcinoma slides (N = 8,461) to develop a computational EGFR biomarker. Our model fine-tunes an open-source foundation model, improving task-specific performance with out-of-center generalization and clinical-grade accuracy on primary and metastatic specimens (mean area under the curve: internal 0.847, external 0.870). To evaluate real-world clinical translation, we conducted a prospective silent trial of the biomarker on primary samples, achieving an area under the curve of 0.890. The artificial-intelligence-assisted workflow reduced the number of rapid molecular tests needed by up to 43% while maintaining the current clinical standard performance. Our retrospective and prospective analyses demonstrate the real-world clinical utility of a computational pathology biomarker.
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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.