Comparative performance of PD-L1 scoring by pathologists and AI algorithms.

Journal: Histopathology
Published Date: Feb 17, 2025

Abstract

AIM: This study evaluates the comparative effectiveness of pathologists versus artificial intelligence (AI) algorithms in scoring PD-L1 expression in non-small cell lung carcinoma (NSCLC). Immune-checkpoint inhibitors have revolutionized NSCLC treatment, with PD-L1 expression, measured as the tumour proportion score (TPS), serving as a critical predictive biomarker for therapeutic response.

Authors

  • Markus Plass
    Medical University of Graz, Graz, Austria.
  • Gheorghe-Emilian Olteanu
    Department of Pathology, British Columbia Cancer Agency, Vancouver, BC, Canada.
  • Sanja Dacic
    FISH and Developmental Laboratory at the University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Izidor Kern
    Cytology and Pathology Laboratory, University Clinic of Respiratory and Allergic Diseases, Golnik, Slovenia.
  • Martin Zacharias
    Physik Department T38, Technische Universität München, James-Franck-Straße, Garching, Germany.
  • Helmut Popper
    Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria.
  • Junya Fukuoka
    Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Sakamoto, Nagasaki, Japan; Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan. Electronic address: fukuokaj@nagasaki-u.ac.jp.
  • Sosuke Ishijima
    Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
  • Michaela Kargl
    Medical University Graz, Graz, Austria.
  • Christoph Murauer
    Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria.
  • Lipika Kalson
    Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria.
  • Luka Brcic
    Department of Pathology, Graz, Austria.

Keywords

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