Risk Prediction Models for Melanoma: A Systematic Review

Melanoma incidence is increasing rapidly worldwide among white-skinned populations. Earlier diagnosis is the principal factor that can improve prognosis. Defining high-risk populations using risk prediction models may help targeted screening and early detection approaches. In this systematic review, we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict risk of developing cutaneous melanoma. A total of 4,141 articles were identified from the literature search and six through citation searching. Twenty-five risk models were included. Between them, the models considered 144 possible risk factors, including 18 measures of number of nevi and 26 of sun/UV exposure. Those most frequently included in final risk models were number of nevi, presence of freckles, history of sunburn, hair color, and skin color. Despite the different factors included and different cutoff values for sensitivity and specificity, almost all models yielded sensitivities and specificities that fit along a summary ROC with area under the ROC (AUROC) of 0.755, suggesting that most models had similar discrimination. Only two models have been validated in separate populations and both also showed good discrimination with AUROC values of 0.79 (0.70-0.86) and 0.70 (0.64-0.77). Further research should focus on validating existing models rather than developing new ones. (C)2014 AACR.