A novel deep learning algorithm for human papillomavirus infection prediction in head and neck cancers using routine histology images.

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Abstract

The etiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens such as alcohol, tobacco, and infection with human papillomavirus (HPV). Because HPV infection influences the prognosis, treatment, and survival of patients with HNSCC, it is important to determine the HPV status of these tumors. In this paper, we propose a novel deep learning pipeline for HPV infection status prediction with state-of-the-art performance in HPV detection using only whole slide images (WSIs) of routine hematoxylin and eosin (H&E) stained HNSCC sections. We show that our Digital-HPV score generated from H&E slides produces statistically significant patient stratifications in terms of overall and disease-specific survival. In addition, quantitative profiling of the spatial tumor microenvironment and analysis of the immune profiles show relatively high levels of lymphocytic infiltration in tumor and tumor-associated-stroma. High levels of B cells, T cells and low macrophage levels were also identified in HPV-positive patients compared to HPV-negative patients, confirming different immune response patterns elicited by HPV infection in HNSCC patients.Copyright © 2023. Published by Elsevier Inc.