Melanoma: Little Difference Found In Performance of Risk Prediction Models

Dr. Juliet A. Usher-Smith Clinical Lecturer in Primary Care The Primary Care Unit, University of Cambridge Strangeways Research Laboratory Cambridge, United Kingdom MedicalResearch.com Interview with:
Dr. Juliet A. Usher-Smith
Clinical Lecturer in Primary Care
The Primary Care Unit, University of Cambridge
Strangeways Research Laboratory
Cambridge, United Kingdom

MedicalResearch: What are the main findings of the study?

Dr Usher-Smith: Our systematic review identified 25 risk models that have the potential to identify individuals at higher risk of developing melanoma. Comparison between the different models was difficult due to the lack of validation studies and heterogeneity in choice and definition of variables. We were, however, able to show that most include well established risk factors and that, despite including a range of different variables, there is very little heterogeneity in the discriminatory ability of the models. There was also little difference in model performance between those scores suitable for self-assessment and those requiring a health care professional, suggesting potential for use at a population level to identify people at higher risk of melanoma.

MedicalResearch: Were any of the findings unexpected?

Dr Usher-Smith: We were surprised at the number of different risk factors included in the different models. Between them, the models considered 144 different possible risk factors. These included 18 different measures of number of naevi, 26 of sun / UV exposure and 14 of history of sunburn.

MedicalResearch: What should clinicians and patients take away from your report?

Dr Usher-Smith: Clinicians will be interested in the range of variables considered and relative performance of the different models and the finding that, despite the wide range of risk factors included, there is very little difference in their performance. Those involved in policy decisions will also be interested to see the potential for using risk scores among asymptomatic people to identify a subset of the population for whom targeted screening, surveillance or educational programmes could be offered.

Dr Usher-Smith: Patients will be interested to see that models have been developed that do not require clinician input and so could potentially be completed at a population level to identify those at higher risk and who would benefit from targeted screening, surveillance or educational programmes.

MedicalResearch: What recommendations do you have for future research as a result of this study?

Dr Usher-Smith: The finding that many of the models have similar performance characteristics despite the wide range of different variables included suggests that developing further models based on current known risk factors is unlikely to benefit the field. As advances are made into genes that play a role in the susceptibility of melanoma, development of new risk models incorporating genetic information may improve the discriminatory ability. Until then, further research should focus on validating existing models in different populations and assessing the costs, feasibility, acceptability and adverse consequences of applying these models.
Citation:

Risk prediction models for melanoma: A systematic review
Juliet A. Usher-Smith, Jon Emery, Angelos P. Kassianos, and Fiona M. Walter

Cancer Epidemiol Biomarkers Prev cebp.0295.2014; Published OnlineFirst June 3, 2014; doi:10.1158/1055-9965.EPI-14-0295

 

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