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<p>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%<i>and</i> ⩾ 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.</p>

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AI driven pre-regulatory validation of PD-L1 analysis in lung cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月28日

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  • Yasmine Makhlouf,
  • Perry Maxwell,
  • Paul O’Reilly,
  • Alice Geaney,
  • Jacqueline A. James,
  • Manuel Salto-Tellez

摘要

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.