Human papillomavirus 16 infection has been proven to be associated with oropharyngeal squamous cell carcinomas (SCCs) and is probably the main reason of the reported increase in the incidence. The role of high-risk (HR) HPV for carcinogenesis of other sites in the head and neck awaits confirmation. With the aim to evaluate the prevalence of HPV infection and the reliability of different diagnostic tools in SCCs of different sites, 109 consecutive untreated head and neck SCCs were enroled, and fresh tumour samples collected. Human papillomavirus DNA was detected by Digene Hybrid Capture 2 (HC2). Human papillomavirus E6 and E7 mRNA were detected by NucliSENS EasyQ HPVv1. P16 expression was evaluated by immunohistochemistry. In all, 12.84% of cases were infected by HR genotypes and 1.84% by low-risk genotypes. Human papillomavirus 16 accounted for 87% of HR infections. The overall agreement between DNA and RNA detection is 99.1%. Although p16 expression clearly correlates with HPV infection (P=0.0051), the inter-rater agreement is poor (k=0.27). The oropharynx showed the highest HR HPV infection rate (47.6%) and was also the only site in which p16 immunohistochemistry revealed to be a fair, but not excellent, diagnostic assay (κ=0.61). The prognostic role of HR HPV infection in oropharyngeal oncology, with its potential clinical applications, underscores the need for a consensus on the most appropriate detection methods. The present results suggest that viral mRNA detection could be the standard for fresh samples, whereas DNA detection could be routinely used in formalin-fixed, paraffin-embedded samples.

High-risk human papillomavirus (HPV) infection is a defined etiopathogenetic factor in oropharyngeal carcinogenesis with a clear prognostic value. The P16 IHC (immunohistochemistry) is a widely accepted marker for HPV-driven carcinogenesis in oropharyngeal squamous cell carcinoma (OPSCC); in the present paper, we discuss its reliability as a standalone marker in different populations. The literature suggests that rates of p16 IHC false positive results are inversely correlated with the prevalence of HPV-driven carcinogenesis in a population. We propose a formula that can calculate such a false positive rate while knowing the real prevalence of HPV-driven OPSCCs in a given population. As it has been demonstrated that p16 positive/HPV negative cases (i.e., false positives at p16 IHC) have the same prognosis as p16 negative OPSCC, we conclude that despite the valuable prognostic value of p16 IHC, relying only on a p16 IHC positive result to recommend treatment de-intensification could be risky. For this aim, confirmation with an HPV nucleic acid detection system, especially in areas with a low prevalence of HPV-related OPSCCs, should be pursued.

Background: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a promising strategy to improve OARs contouring in radiotherapy departments. A comparison of deep-learning-generated auto-contours (AC) with manual contours (MC) was performed by three expert radiation oncologists from a single center. Methods: Planning computed tomography (CT) scans of patients undergoing RT treatments for H&N cancers were considered. CT scans were processed by Limbus Contour auto-segmentation software, a commercial deep-learning auto-segmentation based software to generate AC. H&N protocol was used to perform AC, with the structure set consisting of bilateral brachial plexus, brain, brainstem, bilateral cochlea, pharyngeal constrictors, eye globes, bilateral lens, mandible, optic chiasm, bilateral optic nerves, oral cavity, bilateral parotids, spinal cord, bilateral submandibular glands, lips and thyroid. Manual revision of OARs was performed according to international consensus guidelines. The AC and MC were compared using the Dice similarity coefficient (DSC) and 95% Hausdorff distance transform (DT). Results: A total of 274 contours obtained by processing CT scans were included in the analysis. The highest values of DSC were obtained for the brain (DSC 1.00), left and right eye globes and the mandible (DSC 0.98). The structures with greater MC editing were optic chiasm, optic nerves and cochleae. Conclusions: In this preliminary analysis, deep-learning auto-segmentation seems to provide acceptable H&N OAR delineations. For less accurate organs, AC could be considered a starting point for review and manual adjustment. Our results suggest that AC could become a useful time-saving tool to optimize workload and resources in RT departments.