ArticleScientific reports2025
AI driven pre-regulatory validation of PD-L1 analysis in lung cancer.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The assessment of PD-L1 in lung cancer using the Tumour Proportion Score (TPS) is one of the cornerstones of immune-oncology, but it is open to inter- and intra-pathologist variation, particularly around the clinical thresholds of less than 1%and ⩾ 50%, which correspond to analytical thresholds less than 5% and between 40%-60%. In this paper we describe the development of a deep learn- ing (DL) tool to assist TPS calculation. To confirm ground truth values around the clinical thresholds, we used a validated multiplex immunfluorescence panel including PD-L1, CD68 and cytokeratin. Practically, the DL tool is designed to assist in highlighting cases about these thresholds around the 1% and 50% levels for manual review, and allowing a direct interpretation of inbetween scores. Us- ing such an assisted system, we highlight the potential use of such DL tools in providing a route to future clinical quantitation of tissue-based biomarkers.
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