Predicting HPV association using deep learning and regular H&E stains allows granular stratification of oropharyngeal cancer patients

Human Papilloma Virus (HPV)-associated oropharyngeal squamous cell cancer (OPSCC) represents an OPSCC subgroup with an overall good prognosis with a rising incidence in Western countries. Multiple lines of evidence suggest that HPV-associated tumors are not a homogeneous tumor entity, underlining the need for accurate prognostic biomarkers. In this retrospective, multi-institutional study involving 906 patients from four centers and one database, we developed a deep learning algorithm (OPSCCnet), to analyze standard H&E stains for the calculation of a patient-level score associated with prognosis, comparing it to combined HPV-DNA and p16-status. When comparing OPSCCnet to HPV-status, the algorithm showed a good overall performance with a mean area under the receiver operator curve (AUROC)?=?0.83 (95% CI?=?0.77-0.9) for the test cohort (n?=?639), which could be increased to AUROC?=?0.88 by filtering cases using a fixed threshold on the variance of the probability of the HPV-positive class - a potential surrogate marker of HPV-heterogeneity. OPSCCnet could be used as a screening tool, outperforming gold standard HPV testing (OPSCCnet: five-year survival rate: 96% [95% CI?=?90-100%]; HPV testing: five-year survival rate: 80% [95% CI?=?71-90%]). This could be confirmed using a multivariate analysis of a three-tier threshold (OPSCCnet: high HR?=?0.15 [95% CI?=?0.05-0.44], intermediate HR?=?0.58 [95% CI?=?0.34-0.98] p?=?0.043, Cox proportional hazards model, n?=?211; HPV testing: HR?=?0.29 [95% CI?=?0.15-0.54] p?<?0.001, Cox proportional hazards model, n?=?211). Collectively, our findings indicate that by analyzing standard gigapixel hematoxylin and eosin (H&E) histological whole-slide images, OPSCCnet demonstrated superior performance over p16/HPV-DNA testing in various clinical scenarios, particularly in accurately stratifying these patients.