ArticleNPJ precision oncology2026
Validation of histopathology-based deep learning algorithms for detection of actionable non-small cell lung cancer biomarkers.
Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Comment on "Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes".Journal of cancer research and clinical oncology · 2026Article
- Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.Journal of thoracic disease · 2026Review
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
Corrections and comments
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Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
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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.