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Article

Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.

Nature medicine - 1 Sept 2025

Campanella Gabriele, Kumar Neeraj, Nanda Swaraj, Singi Siddharth, Fluder Eugene, Kwan Ricky, Muehlstedt Silke, Pfarr Nicole, Schüffler Peter J, Häggström Ida, Neittaanmäki Noora, Akyürek Levent M, Basnet Alina, Jamaspishvili Tamara, Nasr Michel R, Croken Matthew M, Hirsch Fred R, Elkrief Arielle, Yu Helena, Ardon Orly, Goldgof Gregory M, Hameed Meera, Houldsworth Jane, Arcila Maria, Fuchs Thomas J, Vanderbilt Chad

Abstract excerpt

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....

Read the complete abstract on PubMed

Topics

Humans
Biomarkers, Tumor
Lung Neoplasms
ErbB Receptors

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Mutation

Adenocarcinoma of Lung

Artificial Intelligence

Female

Male

Source

Open in PubMed Open full text

Identifiers

PMID: 40634781
DOI: 10.1038/s41591-025-03780-x
PMCID: PMC12443599

Genes

EGFR

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 24 Oct 2026.

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