Deep learning-based assessment of PD-L1 expression in NSCLC predicts outcome for patients treated with anti-PD-1 immunotherapy.

Fiche publication

Date publication

février 2026

Journal

Frontiers in immunology

Auteurs

Membres identifiés du Cancéropôle Est :
Pr GHIRINGHELLI François , Mme TRUNTZER Caroline , Dr DERANGERE Valentin

Tous les auteurs :
Peroz M, Roussot N, Ilie A, Rageot D, Derangere V, Truntzer C, Ghiringhelli F

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/41766864

Résumé

PD-L1 expression is widely used as a predictive biomarker for anti-PD-1 therapies in non-small cell lung cancer (NSCLC). However, its prognostic value remains controversial. Here, we investigated whether deep learning (DL) applied to PD-L1 immunohistochemistry (IHC) slides could identify histological patterns predictive of outcome in patients treated with anti-PD-1 therapy.