Deep learning predicts HPV-association in oropharyngeal squamous cell carcinomas and identifies patients with a favorable prognosis using regular H&E stains.

Purpose: Human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC) is tumorigenic and has been associated with a favorable prognosis compared to OPSCC caused by tobacco, alcohol, and other carcinogens. Meanwhile, machine learning has evolved as a powerful tool to predict molecular- and cellular alterations of medical images of various sources. Experimental Design: We generated a deep learning-based HPV prediction score (HPV-ps) on regular HE Giessen, n=163; Cologne, n=110). Then, the prognostic relevance in a total of 594 patients (Giessen, Cologne, HNSCC TCGA) was evaluated. In addition, we investigated whether four board-certified pathologists could identify HPV-association (n=152) and compared the results to the classifier. Results: Although pathologists were able to diagnose HPV-association from HE p=0.129), as compared to AUC=0.8 using the HPV-ps within two independent cohorts (n=273). The HPV-ps identified individuals with a favorable prognosis in a total of 594 patients from three cohorts (Giessen, OPSCC, HR=0.55, p