Leveraging Deep Learning for Immune Cell Quantification and Prognostic Evaluation in Radiotherapy-Treated Oropharyngeal Squamous Cell Carcinomas.

The tumor microenvironment (TME) plays a critical role in cancer progression and therapeutic responsiveness, with the tumor immune microenvironment (TIME) being a key modulator. In head and neck squamous cell carcinomas (HNSCC), immune cell infiltration significantly influences the response to radiotherapy (RT). A better understanding of the TIME in HNSCC could help identify patients most likely to benefit from combining RT with immunotherapy. Standardized, cost-effective methods for studying TIME in HNSCC are currently lacking. This study aims to leverage deep learning (DL) to quantify immune cell densities using immunohistochemistry (IHC) in untreated oropharyngeal squamous cell carcinoma (OPSCC) biopsies of patients scheduled for curative RT, and to assess their prognostic value. We analyzed 84 pre-treatment formalin-fixed paraffin-embedded (FFPE) tumor biopsies from OPSCC patients. Immunohistochemistry was performed for CD3, CD8, CD20, CD163, and FOXP3, and whole slide images (WSIs) were digitized for analysis using a U-Net-based DL model. Two quantification approaches were applied: a cell-counting method and an area-based method. These methods were applied to stained regions. The DL model achieved high accuracy in detecting stained cells across all biomarkers. Strong correlations were found between our DL pipeline, the HALO® Image Analysis Platform, and the open-source QuPath software for estimating immune cell densities. Our DL pipeline provided an accurate and reproducible approach for quantifying immune cells in OPSCC. The area-based method demonstrated superior prognostic value for recurrence-free survival (RFS), when compared to the cell-counting method. Elevated densities of CD3, CD8, CD20, and FOXP3 were associated with improved RFS, while CD163 showed no significant prognostic association. These results highlight the potential of DL in digital pathology for assessing TIME and predicting patient outcomes.