title: Independent validation of six melanoma risk prediction models.
abstract: Identifying people at high risk of melanoma is important for targeted prevention activities and surveillance. Several tools have been developed to classify melanoma risk, but few have been independently validated. We assessed the discriminatory performance of six melanoma prediction tools by applying them to individuals from two independent data sets, one comprising 762 melanoma cases and the second a population-based sample of 42,116 people without melanoma. We compared the model predictions with actual melanoma status to measure sensitivity and specificity. The performance of the models was variable with sensitivity ranging from 97.7 to 10.5% and specificity from 99.6 to 1.3%. The ability of all the models to discriminate between cases and controls, however, was generally high. The model developed by MacKie et al. (1989) had higher sensitivity and specificity for men (0.89 and 0.88) than women (0.79 and 0.72). The tool developed by Cho et al. (2005) was highly specific (men, 0.92; women, 0.99) but considerably less sensitive (men, 0.64; women, 0.37). Other models were either highly specific but lacked sensitivity or had low to very low specificity and higher sensitivity. Poor performance was partly attributable to the use of non-standardized assessment items and various differing interpretations of what constitutes "high risk".
authors.affiliation: Cancer Control Group, Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
authors.forename: Catherine M
authors.initials: CM
authors.lastname: Olsen
authors.affiliation: Cancer Control Group, Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
authors.forename: Rachel E
authors.initials: RE
authors.lastname: Neale
authors.affiliation: Cancer Control Group, Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; Cancer Research UK Manchester Institute and Institute of Inflammation and Repair, University of Manchester, Manchester, UK.
authors.forename: Adèle C
authors.initials: AC
authors.lastname: Green
authors.affiliation: Cancer Control Group, Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
authors.forename: Penelope M
authors.initials: PM
authors.lastname: Webb
authors.affiliation: Cancer Control Group, Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
authors.lastname: The QSkin Study
authors.affiliation: Cancer Control Group, Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
authors.lastname: The Epigene Study
authors.affiliation: Cancer Control Group, Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. Electronic address: david.whiteman@qimrberghofer.edu.au.
authors.forename: David C
authors.initials: DC
authors.lastname: Whiteman
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001315:Australia; D005260:Female; D006801:Humans; D008297:Male; D008545:Melanoma / Q000453:epidemiology*; D008875:Middle Aged; D015233:Models, Statistical; D012372:ROC Curve; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000453:epidemiology*; D055815:Young Adult