Deep learning-based assessment of PD-L1 expression in NSCLC predicts outcome for patients treated with anti-PD-1 immunotherapy
Deep learning applied to PD-L1 IHC slides identifies reproducible histomorphological patterns associated with outcomes in anti-PD-1-treated NSCLC patients. This approach provides prognostic information beyond conventional PD-L1 scoring and enhances predictive accuracy when combined with clinical factors.
Authors
- Caroline Truntzer (ORCID: https://orcid.org/0000-0003-0895-4813)
- David Rageot
- Nicolas Roussot (ORCID: https://orcid.org/0000-0002-6562-6827)
- François Ghiringhelli (ORCID: https://orcid.org/0000-0002-5465-8305)
- Valentin Dérangère (ORCID: https://orcid.org/0000-0002-6311-5628)
- Morgane Peroz
- Alis Ilie (ORCID: https://orcid.org/0009-0009-5788-1586)
Institutions
- Centre Georges François Leclerc (FR)
- Research Institute for Genetic and Human Therapy (US)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2026-02-13
- DOI
- https://doi.org/10.3389/fimmu.2026.1750816
- Primary Topic
- Cancer Immunotherapy and Biomarkers
- Type
- article
- Field-Weighted Citation Impact
- 0.00