Digital pathology for the identification of prognostic biomarkers in head and neck cancer

Abstract

Head and neck cancers comprise a group of diseases with the most common being oral cavity and oropharyngeal cancers. The incidence of these cancers is on the rise but vary globally due to differences in risk factors such as alcohol, tobacco and betel quid consumption in addition to human papilloma virus infection. Despite advances in treatment, including cancer immunotherapy, the mortality rate remains high, which is mainly attributed to late diagnosis. Early detection of malignancies and prediction of malignant transformation in potentially malignant lesions are therefore vital to improve patient outcome. Digital pathology, which uses pre-defined algorithms to generate consistent and faster histopathological analysis, has made great strides in the quantification and identification of different markers capable of predicting disease progression, patient prognosis and response to therapy in head and neck cancer. The combination of digital pathology with different novel technologies including omics platforms, artificial intelligence and machine learning holds great translational potential for identifying prognostic biomarkers for head and neck cancer and beyond.