Leander van Eekelen, Joey Spronck, Monika Looijen-Salamon, Shoko Vos, Enrico Munari, Ilaria Girolami, Albino Eccher, Balazs Acs, Ceren Boyaci, Gabriel Silva de Souza, Muradije Demirel-Andishmand, Luca Dulce Meesters, Daan Zegers, Lieke van der Woude, Willemijn Theelen, Michel van den Heuvel, Katrien Grünberg, Bram van Ginneken, Jeroen van der Laak, Francesco Ciompi
Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset...
March 26, 2024: Scientific Reports