Increased PD-L1 and p16 expression are common in oropharyngeal squamous cell carcinoma.

Overexpression of p16 is closely related to human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (SCC) and pertains a prognostic relevance. Programmed cell death 1-ligand 1 (PD-L1) is another important marker, as anti-PD-L1 immunotherapy is available. Retrospective analysis of 57 cases of the SCC involving oropharynx (27 cases), hypopharynx (5 cases), larynx (11 cases), and oral cavity (14 cases) was performed. Each case was scrutinized for the basaloid morphology, p16, and PD-L1 expression. Basaloid morphology was identified in 47% of total cases. The majority of basaloid SCC variants were located in the oropharynx (89%). High expression of p16 was mostly observed in the oropharynx. High PD-L1 expression was seen predominantly in oropharyngeal and hypopharyngeal locations. Further studies in a larger cohort are necessary to correlate PD-L1 and p16 expression with survival.

The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa.

Incomplete surgical resection of head and neck squamous cell carcinoma (HNSCC) is the most common cause of local HNSCC recurrence. Currently, surgeons rely on preoperative imaging, direct visualization, palpation and frozen section to determine the extent of tissue resection. It has been demonstrated that optical coherence tomography (OCT), a minimally invasive, nonionizing near infrared mesoscopic imaging modality can resolve subsurface differences between normal and abnormal head and neck mucosa. Previous work has utilized two-dimensional OCT imaging which is limited to the evaluation of small regions of interest generated frame by frame. OCT technology is capable of performing rapid volumetric imaging, but the capacity and expertise to analyze this massive amount of image data is lacking. In this study, we evaluate the ability of a retrained convolutional neural network to classify three-dimensional OCT images of head and neck mucosa to differentiate normal and abnormal tissues with sensitivity and specificity of 100% and 70%, respectively. This method has the potential to serve as a real-time analytic tool in the assessment of surgical margins.