Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.

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 inc...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Prerna Tewari, Eugene Kashdan, Cathal Walsh, Cara M Martin, Andrew C Parnell, John J O'Leary
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/a354830f3e9c4c39ae136edeed152edc
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a354830f3e9c4c39ae136edeed152edc
record_format	dspace
spelling	oai:doaj.org-article:a354830f3e9c4c39ae136edeed152edc2021-12-02T19:58:02ZEstimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.1553-734X1553-735810.1371/journal.pcbi.1009289https://doaj.org/article/a354830f3e9c4c39ae136edeed152edc2021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009289https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The 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.Prerna TewariEugene KashdanCathal WalshCara M MartinAndrew C ParnellJohn J O'LearyPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009289 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Prerna Tewari Eugene Kashdan Cathal Walsh Cara M Martin Andrew C Parnell John J O'Leary Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.
description	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.
format	article
author	Prerna Tewari Eugene Kashdan Cathal Walsh Cara M Martin Andrew C Parnell John J O'Leary
author_facet	Prerna Tewari Eugene Kashdan Cathal Walsh Cara M Martin Andrew C Parnell John J O'Leary
author_sort	Prerna Tewari
title	Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.
title_short	Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.
title_full	Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.
title_fullStr	Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.
title_full_unstemmed	Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.
title_sort	estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse bayesian modelling.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a354830f3e9c4c39ae136edeed152edc
work_keys_str_mv	AT prernatewari estimatingtheconditionalprobabilityofdevelopinghumanpapillomavirusrelatedoropharyngealcancerbycombiningmachinelearningandinversebayesianmodelling AT eugenekashdan estimatingtheconditionalprobabilityofdevelopinghumanpapillomavirusrelatedoropharyngealcancerbycombiningmachinelearningandinversebayesianmodelling AT cathalwalsh estimatingtheconditionalprobabilityofdevelopinghumanpapillomavirusrelatedoropharyngealcancerbycombiningmachinelearningandinversebayesianmodelling AT carammartin estimatingtheconditionalprobabilityofdevelopinghumanpapillomavirusrelatedoropharyngealcancerbycombiningmachinelearningandinversebayesianmodelling AT andrewcparnell estimatingtheconditionalprobabilityofdevelopinghumanpapillomavirusrelatedoropharyngealcancerbycombiningmachinelearningandinversebayesianmodelling AT johnjoleary estimatingtheconditionalprobabilityofdevelopinghumanpapillomavirusrelatedoropharyngealcancerbycombiningmachinelearningandinversebayesianmodelling
_version_	1718375793389731840

Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.

Ejemplares similares