  The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key
biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients
that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1
expression currently includes the visual estimation of a Tumor Cell (TC) score
by a pathologist and consists of evaluating the ratio of PD-L1 positive and
PD-L1 negative tumor cells. Known challenges like differences in positivity
estimation around clinically relevant cut-offs and sub-optimal quality of
samples makes visual scoring tedious and subjective, yielding a scoring
variability between pathologists. In this work, we propose a novel deep
learning solution that enables the first automated and objective scoring of
PD-L1 expression in late stage NSCLC needle biopsies. To account for the low
amount of tissue available in biopsy images and to restrict the amount of
manual annotations necessary for training, we explore the use of
semi-supervised approaches against standard fully supervised methods. We
consolidate the manual annotations used for training as well the visual TC
scores used for quantitative evaluation with multiple pathologists. Concordance
measures computed on a set of slides unseen during training provide evidence
that our automatic scoring method matches visual scoring on the considered
dataset while ensuring repeatability and objectivity.
