Risk Prediction Models for Oral Cancer: A Systematic Review

Oral cancer is among the twenty most common cancers worldwide. Finding and treating this cancer early improves survival rates. Screening the whole population to check for oral cancer is unlikely to be an efficient use of resources; however, screening only individuals at higher risk has been shown to reduce oral cancer deaths and be cost-effective for healthcare services. Mathematical models have previously been developed to identify these high-risk groups; however, it is not known whether any of these would be suitable for use in clinical practice. In this study, we identified and compared previously published models. We found several that had potential, but only two had been tested outside the original study population. We suggest that future research should focus on (a) testing how well the models identify those at high risk within potential screening populations and (b) assessing how the models might be included within the healthcare systems. In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction models could be used to target screening to those at highest risk or to select individuals for preventative interventions. This review (a) systematically identified published models that predict the development of oral cancer and are suitable for use in the general population and (b) described and compared the identified models, focusing on their development, including risk factors, performance and applicability to risk-stratified screening. A search was carried out in November 2022 in the Medline, Embase and Cochrane Library databases to identify primary research papers that report the development or validation of models predicting the risk of developing oral cancer (cancers of the oral cavity or oropharynx). The PROBAST tool was used to evaluate the risk of bias in the identified studies and the applicability of the models they describe. The search identified 11,222 articles, of which 14 studies (describing 23 models), satisfied the eligibility criteria of this review. The most commonly included risk factors were age (n = 20), alcohol consumption (n = 18) and smoking (n = 17). Six of the included models incorporated genetic information and three used biomarkers as predictors. Including information on human papillomavirus status was shown to improve model performance; however, this was only included in a small number of models. Most of the identified models (n = 13) showed good or excellent discrimination (AUROC > 0.7). Only fourteen models had been validated and only two of these validations were carried out in populations distinct from the model development population (external validation). Conclusions: Several risk prediction models have been identified that could be used to identify individuals at the highest risk of oral cancer within the context of screening programmes. However, external validation of these models in the target population is required, and, subsequently, an assessment of the feasibility of implementation with a risk-stratified screening programme for oral cancer.