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<p>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 (<i>N</i> = 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月9日

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

摘要

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.