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Training on PD-L1 scoring in non-small cell lung cancer with high intra- and inter-reader agreement: results of a worldwide microscopic/digital image-based training of 751 pathologists.

Created on 24 Jun 2026

Authors

Gudrun Bänfer, Rolf Diezko, Rabea Oberthür, George L Kumar, Josef Rüschoff, Hans-Ulrich Schildhaus, Bharat Jasani

Published in

British journal of biomedical science. Volume 83. Pages 16477. Epub Jun 08, 2026.

Abstract

Semi-quantitative scoring of PD-L1 expression on tumor cells (TCs) and/or infiltrating immune cells (ICs) is complex and requires expert pathologist training to reduce inter- and intra-reader variability. We conducted a 1- or 2-day training of 751 pathologists world-wide from 63 countries over a period of 2 years (2016 and 2017). Pathologists read microscopic slides or fully digitized slides stained with PD-L1 immunohistochemistry 22C3 pharmDx assay with expression levels enriched around the clinically relevant cutoffs of 1% and 50% in non-small cell lung cancer (NSCLC). The overall inter-reader agreement (OPA) for PD-L1-stained NSCLC was 95.6% for TPS ≥1% and 87.3% for TPS ≥50% cut-off. The corresponding intra-reader agreement was 95.9% for the ≥1% and 91.4% for the ≥50% cut-off. The inter-reader negative percent agreement (NPA) for TPS ≥1% was 85.6% and 91.9% for the TPS ≥50% cut-off, and the positive percent agreement (PPA) was 97.6% (TPS ≥1%) and 81.0% (TPS ≥50%). The observed high inter- and intra-reader agreements are promising given the difficulty of reproducible scoring tumor cells with heterogeneous distribution of PD-L1 staining combined with the varied professional training, expertise, and experience of the participants. The results may reflect the utility of the expert led standardized protocol used to train pathologists for scoring PD-L1 staining in NSCLC specimens. The digital image-led training approach has the additional advantage of providing a computer-assisted scoring system and allows for remote training of pathologists worldwide.

PMID:
42338813
Bibliographic data and abstract were imported from PubMed on 24 Jun 2026.

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