Assessment of the current and emerging criteria for the histopathological classification of lung neuroendocrine tumours in the lungNENomics project.
Fiche publication
Date publication
juin 2024
Journal
ESMO open
Auteurs
Membres identifiés du Cancéropôle Est :
Pr VIGNAUD Jean-Michel
Tous les auteurs :
Mathian É, Drouet Y, Sexton-Oates A, Papotti MG, Pelosi G, Vignaud JM, Brcic L, Mansuet-Lupo A, Damiola F, Altun C, Berthet JP, Fournier CB, Brustugun OT, Centonze G, Chalabreysse L, de Montpréville VT, di Micco CM, Fadel E, Gadot N, Graziano P, Hofman P, Hofman V, Lacomme S, Lund-Iversen M, Mangiante L, Milione M, Muscarella LA, Perrin C, Planchard G, Popper H, Rousseau N, Roz L, Sabella G, Tabone-Eglinger S, Voegele C, Volante M, Walter T, Dingemans AM, Moonen L, Speel EJ, Derks J, Girard N, Chen L, Alcala N, Fernandez-Cuesta L, Lantuejoul S, Foll M
Lien Pubmed
Résumé
Six thoracic pathologists reviewed 259 lung neuroendocrine tumours (LNETs) from the lungNENomics project, with 171 of them having associated survival data. This cohort presents a unique opportunity to assess the strengths and limitations of current World Health Organization (WHO) classification criteria and to evaluate the utility of emerging markers.
Mots clés
Ki-67, PHH3, deep learning, histological classification, lung neuroendocrine tumours
Référence
ESMO Open. 2024 06 14;9(6):103591