Risk prediction models for head and neck cancer: A rapid review

Abstract

Background: Cancer risk assessment models are used to support prevention and early detection. However, few models have been developed for head and neck cancer (HNC). Methods: A rapid review of Embase and MEDLINE identified n = 3045 articles. Following dual screening, n = 14 studies were included. Quality appraisal using the PROBAST (risk of bias) instrument was conducted, and a narrative synthesis was performed to identify the best performing models in terms of risk factors and designs. Results: Six of the 14 models were assessed as “high” quality. Of these, three had high predictive performance achieving area under curve values over 0.8 (0.87–0.89). The common features of these models were their inclusion of predictors carefully tailored to the target population/anatomical subsite and development with external validation. Conclusions: Some existing models do possess the potential to identify and stratify those at risk of HNC but there is scope for improvement.