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<p>Non-small cell lung cancer (NSCLC) patient management relies on molecular analysis to determine eligibility for targeted therapy. Furthermore, neoadjuvant immunotherapy is primarily suitable in the absence of specific genomic alterations. However, significant challenges remain, including suboptimal molecular testing and patients being assigned to non-optimal treatment strategies. Here, we present AI classifiers for the identification of <i>EGFR</i>, <i>ALK</i>, <i>BRAF</i> and <i>MET</i> alterations directly from hematoxylin and eosin (H&amp;E)-stained tissue using CanvOI 1.1, a digital pathology foundation model. Their performance was evaluated on an independent validation dataset of 968 NSCLC samples. The classifiers achieved AUCs of 0.87 for <i>EGFR</i>, 0.96 for <i>ALK</i>, 0.88 for <i>BRAF</i> and 0.83 for <i>MET</i>. Moreover, they demonstrated high accuracy in identifying cases lacking alterations. Our results highlight the potential of deep-learning tools for the detection of NSCLC biomarkers and specifically the identification of tumors without <i>EGFR</i> or <i>ALK</i> driver alterations, supporting more informed clinical decision-making.</p>

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Validation of histopathology-based deep learning algorithms for detection of actionable non-small cell lung cancer biomarkers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月20日

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  • Christian Rolfo,
  • Efrat Ofek,
  • Yoash Barak,
  • Jonathan Weidenfeld,
  • Razan Imad Haj,
  • Yosef Molchanov,
  • Alexander Loebel,
  • David R. Braxton,
  • John S. Cupp,
  • Rinat Yacobi,
  • Chen Mayer,
  • Francesco Drago,
  • Camila Avivi,
  • Nurit Paz-Yaacov,
  • Assaf Avinoam,
  • Jonathan Zalach,
  • Addie Dvir,
  • Inbal Gazy,
  • Nir Peled,
  • Damien Urban,
  • Jair Bar,
  • Dov Hershkoviz,
  • Iris Barshack

摘要

Non-small cell lung cancer (NSCLC) patient management relies on molecular analysis to determine eligibility for targeted therapy. Furthermore, neoadjuvant immunotherapy is primarily suitable in the absence of specific genomic alterations. However, significant challenges remain, including suboptimal molecular testing and patients being assigned to non-optimal treatment strategies. Here, we present AI classifiers for the identification of EGFR, ALK, BRAF and MET alterations directly from hematoxylin and eosin (H&E)-stained tissue using CanvOI 1.1, a digital pathology foundation model. Their performance was evaluated on an independent validation dataset of 968 NSCLC samples. The classifiers achieved AUCs of 0.87 for EGFR, 0.96 for ALK, 0.88 for BRAF and 0.83 for MET. Moreover, they demonstrated high accuracy in identifying cases lacking alterations. Our results highlight the potential of deep-learning tools for the detection of NSCLC biomarkers and specifically the identification of tumors without EGFR or ALK driver alterations, supporting more informed clinical decision-making.