Risk Stratification for Melanoma: Models Derived and Validated in a Purpose-Designed Prospective Cohort

Abstract

Background: Risk stratification can improve the efficacy and cost-efficiency of screening programs for early detection of cancer. We sought to derive a risk stratification tool for melanoma that was suitable for the general population using only self-reported information.

Methods: We used melanoma risk factor information collected at baseline from QSKIN, a prospective cohort study of Queensland adults age 40 to 69 years at recruitment (n = 41 954). We examined two separate outcomes: 1) invasive melanomas and 2) all melanomas (invasive + in situ) obtained through data linkage to the cancer registry. We used stepwise Cox proportional hazards modeling to derive the risk models in a randomly selected two-thirds sample of the data set and assessed model performance in the remaining one-third validation sample.

Results: After a median follow-up of 3.4 years, 655 (1.6%) participants developed melanoma (257 invasive, 398 in situ). The prediction model for invasive melanoma included seven terms. At baseline, the strongest predictors of invasive melanoma were age, sex, tanning ability, number of moles at age 21 years, and number of skin lesions treated destructively. The model for "all melanomas" (ie, invasive and in situ) included five additional terms. Discrimination in the validation data set was high for both models (C-index = 0.69, 95% confidence interval [CI] = 0.62 to 0.76, and C-index = 0.72, 95% CI = 0.69 to 0.75, respectively), and calibration was acceptable.

Conclusions: Such a tool could be used to target surveillance activities to those at highest predicted risk of developing melanoma over a median duration of 3.4 years.