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

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.