A novel risk score may be able to identify patients who are at risk for developing melanoma, according to a recent study published online ahead of print in Cancer Epidemiology, Biomarkers & Prevention.
Researchers led by John R. Davies of the University of Leeds pooled data on 16 case-controlled studies in order to predict melanoma risk using a logistic random coefficients model. The proposed risk algorithm would account for hair color, skin type, family history, freckling, nevus count, number of large nevi and history of sunburn.
Risk categories were defined based on predicted odds, and individuals were split into four risk groups based on age, sex and geographic location. The performance of the model was then assessed in an independent UK case-control dataset.
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The researchers found that cases and controls were well-discriminated within the independent dataset. They found that 29 percent of the cases were found in the highest risk group compared with 7 percent in controls, while 43 percent of controls were found in the lowest risk group compared to 13 percent of overall cases.
“This score may be a useful tool to inform members of the public about their melanoma risk,” the authors concluded.