Development and validation of an individualized risk prediction model for oropharynx cancer in the US population

BACKGROUND The incidence of oropharynx cancers has increased substantially in the United States. However, risk stratification tools for the identification of high-risk individuals do not exist. In this study, an individualized risk prediction model was developed and validated for oropharynx cancers in the US population. METHODS A synthetic, US population-based case-control study was conducted. Oropharynx cancer cases diagnosed at Ohio State University (n = 241) were propensity-weighted to represent oropharynx cancers occurring annually in the United States during 2009-2014 (n = 12,656). Controls (n = 9327) included participants in the National Health and Nutrition Examination Survey (2009-2014) and represented the annual US population aged 30 to 69 years (n = 154,532,508). The individualized 1-year absolute risk of oropharynx cancer was estimated with weighted logistic regression. RESULTS The risk prediction model included age, sex, race, smoking, alcohol use, lifetime sexual partners, and oral oncogenic human papillomavirus (HPV) status. The model had good discrimination and calibration in split-sample validation (area under the curve [AUC], 0.94; 95% confidence interval [CI], 0.92-0.97; observed/expected [O/E], 1.01; 95% CI, 0.70-1.32) and external validation (AUC, 0.87; 95% CI, 0.84-0.90; O/E, 1.08; 95% CI, 0.77-1.39). In the US population, 1-year predicted risks of oropharynx cancer were highest for older individuals (21.1/100,000 for 65- to 69-year-olds), men (13.9/100,000), whites (10.4/100,000), smokers (18.0/100,000 for >20 pack-years), heavy alcohol users (18.4/100,000), and those with prevalent oral oncogenic HPV (140.4/100,000). The risk prediction model provided substantial risk stratification, with approximately 77% of all oropharynx cancers and approximately 99% of HPV-positive oropharynx cancers occurring in the 10% of the US population with the highest model-predicted risk. CONCLUSIONS This risk prediction model will enable the efficient design of studies to address the outstanding questions pertaining to the natural history, screening, and secondary prevention of oropharynx cancers.