Development and validation of a melanoma risk score based on pooled data from 16 case-control studies

Abstract

Background: We report the development of a cutaneous melanoma risk algorithm based upon seven factors; hair color, skin type, family history, freckling, nevus count, number of large nevi, and history of sunburn, intended to form the basis of a self-assessment Web tool for the general public.

Methods: Predicted odds of melanoma were estimated by analyzing a pooled dataset from 16 case-control studies using logistic random coefficients models. Risk categories were defined based on the distribution of the predicted odds in the controls from these studies. Imputation was used to estimate missing data in the pooled datasets. The 30th, 60th, and 90th centiles were used to distribute individuals into four risk groups for their age, sex, and geographic location. Cross-validation was used to test the robustness of the thresholds for each group by leaving out each study one by one. Performance of the model was assessed in an independent UK case-control study dataset.

Results: Cross-validation confirmed the robustness of the threshold estimates. Cases and controls were well discriminated in the independent dataset [area under the curve, 0.75; 95% confidence interval (CI), 0.73-0.78]. Twenty-nine percent of cases were in the highest risk group compared with 7% of controls, and 43% of controls were in the lowest risk group compared with 13% of cases.

Conclusion: We have identified a composite score representing an estimate of relative risk and successfully validated this score in an independent dataset.

Impact: This score may be a useful tool to inform members of the public about their melanoma risk.

Figure 1

ROC curve showing discrimination between cases and controls in the Leeds melanoma case-control study using the risk algorithm: (AUC=0.75, 95% CI 0.73-0.78).