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

Research output: Contribution to journalArticle

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.

Details

Authors
  • John R Davies
  • Yu-mei Chang
  • D Timothy Bishop
  • Bruce K Armstrong
  • Veronique Bataille
  • Wilma Bergman
  • Marianne Berwick
  • Paige M Bracci
  • J Mark Elwood
  • Marc S Ernstoff
  • Adele Green
  • Nelleke A Gruis
  • Elizabeth A Holly
  • Peter A Kanetsky
  • Margaret R Karagas
  • Tim K Lee
  • Loïc Le Marchand
  • Rona M Mackie
  • Anne Østerlind
  • Timothy R Rebbeck
  • Kristian Reich
  • Peter Sasieni
  • Victor Siskind
  • Anthony J Swerdlow
  • Linda Titus
  • Michael S Zens
  • Andreas Ziegler
  • Richard P Gallagher
  • Jennifer H Barrett
  • Julia Newton-Bishop
Organisations
External organisations
  • University of Leeds
  • University of Sydney
  • King's College London
  • Leiden University Medical Centre
  • University of New Mexico
  • University of California, San Francisco
  • University of Auckland
  • BC Cancer Research Centre
  • University of Hawaii Cancer Center
  • University of Glasgow
  • University of Pennsylvania
  • Dermatologikum Hamburg
  • University of Lübeck
  • Dartmouth College
  • QIMR Berghofer Medical Research Institute
  • H. Lee Moffitt Cancer Center & Research Institute
  • No affiliation available (private)
  • Queen Mary University
  • Institute of Cancer Research London
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Cancer and Oncology

Keywords

  • Algorithms, Case-Control Studies, Humans, Melanoma, Research Design, Risk Factors, Skin Neoplasms, Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Validation Studies
Original languageEnglish
Pages (from-to)817-24
Number of pages8
JournalCancer Epidemiology Biomarkers & Prevention
Volume24
Issue number5
Publication statusPublished - 2015 May
Publication categoryResearch
Peer-reviewedYes