biostudies-literatureMathian EINCaWorldwide Cancer ResearchNETRF103591https://www.ebi.ac.uk/biostudies/studies/S-EPMC11233924biostudies-literatureUnknown9(6)<h4>Background</h4>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.<h4>Patients and methods</h4>Patients were diagnosed based on the 2021 WHO criteria, with atypical carcinoids (ACs) defined by the presence of focal necrosis and/or 2-10 mitoses per 2 mm². We investigated two markers of tumour proliferation: the Ki-67 index and phospho-histone H3 (PHH3) protein expression, quantified by pathologists and automatically via deep learning. Additionally, an unsupervised deep learning aESMO openAssessment of the current and emerging criteria for the histopathological classification of lung neuroendocrine tumours in the lungNENomics project.PMC1123392421-0005Moonen LCentonze GPelosi GVignaud JMTabone-Eglinger SAlcala NBrustugun OTBerthet JPde Montpreville VTdi Micco CMLantuejoul SGraziano PFoll MSabella GDingemans AMMansuet-Lupo AFournier CBDrouet YAltun CLund-Iversen MVoegele CPopper HRoz LFadel ELacomme SSpeel EJGirard NChen LSexton-Oates AGadot NChalabreysse LRousseau NPerrin CHofman PBrcic LMangiante LDamiola FWalter TMathian EMilione MHofman VVolante MFernandez-Cuesta LDerks JPapotti MGPlanchard GMuscarella LAfalseAssessment of the current and emerging criteria for the histopathological classification of lung neuroendocrine tumours in the lungNENomics project.<h4>Background</h4>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.<h4>Patients and methods</h4>Patients were diagnosed based on the 2021 WHO criteria, with atypical carcinoids (ACs) defined by the presence of focal necrosis and/or 2-10 mitoses per 2 mm². We investigated two markers of tumour proliferation: the Ki-67 index and phospho-histone H3 (PHH3) protein expression, quantified by pathologists and automatically via deep learning. Additionally, an unsupervised deep learning a2024-01-01T00:00:00Z2024 Jun2025-04-05T15:47:53.947Z2025-04-05T15:47:53.947ZS-EPMC112339243887832410.1016/j.esmoop.2024.103591