Abstract
The epidemic increase in the incidence of Human Papilloma Virus (HPV) related Oropharyngeal Squamous Cell Carcinomas (OPSCCs) in several countries worldwide represents a significant public health concern. Although gender neutral HPV vaccination programmes are expected to cause a reduction in the incidence rates of OPSCCs, these effects will not be evident in the foreseeable future. Secondary prevention strategies are currently not feasible due to an incomplete understanding of the natural history of oral HPV infections in OPSCCs. The key parameters that govern natural history models remain largely ill-defined for HPV related OPSCCs and cannot be easily inferred from experimental data. Mathematical models have been used to estimate some of these ill-defined parameters in cervical cancer, another HPV related cancer leading to successful implementation of cancer prevention strategies. We outline a “double-Bayesian” mathematical modelling approach, whereby, a Bayesian machine learning model first estimates the probability of an individual having an oral HPV infection, given OPSCC and other covariate information. The model is then inverted using Bayes’ theorem to reverse the probability relationship. We use data from the Surveillance, Epidemiology, and End Results (SEER) cancer registry, SEER Head and Neck with HPV Database and the National Health and Nutrition Examination Surveys (NHANES), representing the adult population in the United States to derive our model. The model contains 8,106 OPSCC patients of which 73.0% had an oral HPV infection. When stratified by age, sex, marital status and race/ethnicity, the model estimated a higher conditional probability for developing OPSCCs given an oral HPV infection in non-Hispanic White males and females compared to other races/ethnicities. The proposed Bayesian model represents a proof-of-concept of a natural history model of HPV driven OPSCCs and outlines a strategy for estimating the conditional probability of an individual’s risk of developing OPSCC following an oral HPV infection.
Highlights
Head and Neck Squamous Cell Carcinomas (HNSCCs) collectively refers to cancers that develop from a range of anatomical sites; oral cavity, oropharynx, nasal cavity, nasopharynx, hypopharynx and larynx [1]
The model estimates a higher conditional probability for developing Oropharyngeal Squamous Cell Cancers (OPSCCs) given an oral Human Papilloma Virus (HPV) infection in non-Hispanic White males and females compared to other races/ ethnicities
We acknowledge several limitations of the proposed model including the assumption that the different data sources considered for developing the model are drawn randomly from a common population, lack of longitudinal follow-up data, and at best recognise that this is a proof-of-concept of a natural history model of HPV driven OPSCCs
Summary
Head and Neck Squamous Cell Carcinomas (HNSCCs) collectively refers to cancers that develop from a range of anatomical sites; oral cavity, oropharynx (tonsils, base of tongue), nasal cavity, nasopharynx, hypopharynx and larynx [1]. Worldwide, these cancers represent the seventh most common cancer diagnosed annually [2]. Natural history models have played a crucial role in informing primary and secondary prevention strategies in cervical cancer [11, 12] These models have leveraged data from epidemiological studies that established a temporal relationship between exposure to HPV and subsequent malignant transformation through different stages of cervical pre-cancer and cancer [13]. These models differ in form and function, the common underlying premise for data modelling is the progression and regression of cervical HPV infections through different stages of preneoplastic disease and malignancy [14]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
