A combined model integrating DL groups with clinical variables improved prediction of PFS compared to clinical features alone (HR = 0.50, 95% CI 0.33-0.75; p<0.001 in MSK; HR = 0.54, 95% CI 0.31-0.91; p=0.02 in CGFL).<h4>Conclusions</h4>Deep learning applied to PD-L1 IHC slides identifies reproducible histomorphological patterns associated with outcomes in anti-PD-1-treated NSCLC patients. This evidence concerns the gene CD274 and non-small cell lung carcinoma.