Abstract
INTRODUCTION: In head and neck squamous cell carcinoma (HNSCC) high numbers of tumor infiltrating CD8 T cells in the tumor microenvironment are associated with better outcome. However, no investigators have employed automated image analysis on whole slide images to permit CD8 scores for use in clinical practice. The aim of this study was to develop and validate an image analysis algorithm to automatically quantify CD8 T cells in patients with oropharyngeal HNSCC. MATERIALS AND METHODS: Using brightfield image analysis results were cross-validated with fluorescence based quantification (AQUA™). A nuclear image algorithm designed to run on whole slide images was optimized to manual count. The algorithm was locked down and used on a cohort of whole tissue sections from HNSCC patients. Multivariate clinicopathologic parameters and outcomes were statistically correlated with image analysis results. RESULTS: Linear correlation between manual counts and the customized CD8 algorithm was 0.943. A total of 74 oropharyngeal HNSCC cases were analyzed for CD8 immune cell infiltrate using this image analysis algorithm. A CD8 immune cell density above 136 cells/mm(2) was associated with median survival of 18 years compared to 5 years. When multivariate modeling was performed, HPV infection was the only predictor of survival; however, when HPV was excluded only CD8 cell density predicts survival. CONCLUSIONS: We report the successful technical development and clinical validation of an image algorithm to automate CD8 immune cell density for oropharyngeal HNSCC. Employing brightfield image analysis on entire tumor sections instead of tumor subcompartments permits this strategy to be widely implemented.