RT Journal Article T1 Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms A1 Molero, Aida A1 Hernández, Susana A1 Alonso, Marta A1 Peressini, Melina A1 Curto, Daniel A1 López-Ríos Moreno, Fernando A1 Conde, Esther AB Aims: To study programmed death ligand 1 (PD-L1) expression and tumour infiltrating lymphocytes (TILs) in patients with early-stage non-small cell lung carcinoma (NSCLC) with artificial intelligence (AI) algorithms.Methods: The study included samples from 50 early-stage NSCLCs. PD-L1 immunohistochemistry (IHC) stained slides (clone SP263) were scored manually and with two different AI tools (PathAI and Navify Digital Pathology) by three pathologists. TILs were digitally assessed on H&E and CD8 IHC stained sections with two different algorithms (PathAI and Navify Digital Pathology, respectively). The agreement between observers and methods for each biomarker was analysed. For PD-L1, the turn-around time (TAT) for manual versus AI-assisted scoring was recorded.Results: Agreement was higher in tumours with low PD-L1 expression regardless of the approach. Both AI-powered tools identified a significantly higher number of cases equal or above 1% PD-L1 tumour proportion score as compared with manual scoring (p=0.00015), a finding with potential therapeutic implications. Regarding TAT, there were significant differences between manual scoring and AI use (p value <0.0001 for all comparisons). The total TILs density with the PathAI algorithm and the total density of CD8+ cells with the Navify Digital Pathology software were significantly correlated (τ=0.49 (95% CI 0.37, 0.61), p value<0.0001).Conclusions: This preliminary study supports the use of AI algorithms for the scoring of PD-L1 and TILs in patients with NSCLC. PB BMJ Publishing Group SN 0021-9746 YR 2024 FD 2024-10-17 LK https://hdl.handle.net/20.500.14352/118190 UL https://hdl.handle.net/20.500.14352/118190 LA eng NO Molero A, Hernandez S, Alonso M, et al. J Clin Pathol Epub ahead of print: [please include Day Month Year]. doi:10.1136/ jcp-2024-209766 NO Fondos FEDER NO European Commission NO Instituto de Salud Carlos III (España) NO Comunidad Autónoma de Madrid DS Docta Complutense RD 10 abr 2025