WhARIO: whole-slide-image-based survival analysis for patients treated with immunotherapy.
Fiche publication
Date publication
mai 2024
Journal
Journal of medical imaging (Bellingham, Wash.)
Auteurs
Membres identifiés du Cancéropôle Est :
Pr BIBEAU Frédéric
Tous les auteurs :
Tourniaire P, Ilie M, Mazières J, Vigier A, Ghiringhelli F, Piton N, Sabourin JC, Bibeau F, Hofman P, Ayache N, Delingette H
Lien Pubmed
Résumé
Immune checkpoint inhibitors (ICIs) are now one of the standards of care for patients with lung cancer and have greatly improved both progression-free and overall survival, although of the patients respond to the treatment, and some face acute adverse events. Although a few predictive biomarkers have integrated the clinical workflow, they require additional modalities on top of whole-slide images and lack efficiency or robustness. In this work, we propose a biomarker of immunotherapy outcome derived solely from the analysis of histology slides.
Mots clés
deep learning, digital pathology, immunotherapy, lung cancer, nonparametric clustering, survival analysis
Référence
J Med Imaging (Bellingham). 2024 05;11(3):037502