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The level of PD-L1 expression in immunohistochemistry (IHC) assays is a keybiomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patientsthat may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1expression currently includes the visual estimation of a Tumor Cell (TC) scoreby a pathologist and consists of evaluating the ratio of PD-L1 positive andPD-L1 negative tumor cells. Known challenges like differences in positivityestimation around clinically relevant cut-offs and sub-optimal quality ofsamples makes visual scoring tedious and subjective, yielding a scoringvariability between pathologists. In this work, we propose a novel deeplearning solution that enables the first automated and objective scoring ofPD-L1 expression in late stage NSCLC needle biopsies. To account for the lowamount of tissue available in biopsy images and to restrict the amount ofmanual annotations necessary for training, we explore the use ofsemi-supervised approaches against standard fully supervised methods. Weconsolidate the manual annotations used for training as well the visual TCscores used for quantitative evaluation with multiple pathologists. Concordancemeasures computed on a set of slides unseen during training provide evidencethat our automatic scoring method matches visual scoring on the considereddataset while ensuring repeatability and objectivity.

Deep Semi Supervised Generative Learning for Automated PD-L1 Tumor Cell Scoring on NSCLC Tissue Needle Biopsies