Background: Accurate assessment of programmed death-ligand 1 (PD-L1) immunohistochemical (IHC) expression is critical for immunotherapy in patients with non-small cell lung cancer (NSCLC). Yet, interpreting its staining is challenging, time-consuming, and causes inter-observer variability, potentially mis-stratifying patients. This necessitates the development of an artificial intelligence (AI) model to effectively quantify PD-L1 expression. Hence, we developed an AI-based deep-learning approach to automatically assess PD-L1 expression in NSCLC using IHC 22C3 assay-stained whole slide images (WSIs).
Methods: A total of 706 patients with NSCLC were included in this study and 1212 WSIs were collected from three distinct study cohorts. We accurately matched the hematoxylin and eosin-stained images of the internal dataset with the IHC WSIs. Foreground regions containing tumor tissue were extracted from WSIs, and a multi-granular multiple-instance learning approach employing instance embeddings with coarse and fine granularities was implemented to extract patch-level morphological features. A multi-grained expression interpreter-based model aggregated these features to stratify PD-L1 expression status.
Results: The model showed strong interpretive ability in all three cohorts and wide applicability to different specimen types. The macro-average area under the receiver operating characteristic curve (AUC) were 0.940/0.915/0.944 for surgical specimens, 0.955/0.844/0.865 for biopsy specimens, and 0.901/0.958/0.883 for metastases.
Conclusion: This study emphasizes the potential benefits of deep learning in automatically, rapidly, and accurately inferring PD-L1 expression from complex IHC images. It also showcases how AI frameworks can improve routine digital pathology workflows in current PD-L1 detection methods.
Keywords: Artificial Intelligence; Deep Learning; Immunohistochemistry; Non-small cell lung cancer; Programmed death-ligand 1; Whole slide imaging.
© 2025. The Author(s).