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Original Investigation |

Development and External Validation of a Melanoma Risk Prediction Model Based on Self-assessed Risk Factors

Kylie Vuong, MBBS, MIPH, FRACGP1,2; Bruce K. Armstrong, MBBS (Hons), PhD, FAFPHM1; Elisabete Weiderpass, MD, MSc, PhD3,4,5,6; Eiliv Lund, PhD3; Hans-Olov Adami, PhD4,7; Marit B. Veierod, PhD8; Jennifer H. Barrett, PhD9; John R. Davies, PhD9; D. Timothy Bishop, PhD9; David C. Whiteman, MBBS(Hons), PhD10; Catherine M. Olsen, PhD10; John L. Hopper, PhD11; Graham J. Mann, PhD12,13; Anne E. Cust, MPH(Hons), PhD1,13; Kevin McGeechan, PhD14 ; and the Australian Melanoma Family Study Investigators
[+] Author Affiliations
1Cancer Epidemiology and Prevention Research, Sydney School of Public Health, University of Sydney, Sydney, Australia
2School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
3Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, Arctic University of Norway, Tromsø, Norway
4Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
5Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway
6Genetic Epidemiology Group, Folkhälsan Research Center, Helsinki, Finland
7Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
8Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
9Leeds Institute of Cancer and Pathology, Faculty of Medicine and Health, Leeds University, Leeds, United Kingdom
10Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
11Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
12Centre for Cancer Research, Westmead Institute for Medical Research, University of Sydney, Westmead, Australia
13Melanoma Institute Australia, University of Sydney, North Sydney, Australia
14Sydney School of Public Health, University of Sydney, Sydney, Australia
JAMA Dermatol. 2016;152(8):889-896. doi:10.1001/jamadermatol.2016.0939.
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Importance  Identifying individuals at high risk of melanoma can optimize primary and secondary prevention strategies.

Objective  To develop and externally validate a risk prediction model for incident first-primary cutaneous melanoma using self-assessed risk factors.

Design, Setting, and Participants  We used unconditional logistic regression to develop a multivariable risk prediction model. Relative risk estimates from the model were combined with Australian melanoma incidence and competing mortality rates to obtain absolute risk estimates. A risk prediction model was developed using the Australian Melanoma Family Study (629 cases and 535 controls) and externally validated using 4 independent population-based studies: the Western Australia Melanoma Study (511 case-control pairs), Leeds Melanoma Case-Control Study (960 cases and 513 controls), Epigene-QSkin Study (44 544, of which 766 with melanoma), and Swedish Women’s Lifestyle and Health Cohort Study (49 259 women, of which 273 had melanoma).

Main Outcomes and Measures  We validated model performance internally and externally by assessing discrimination using the area under the receiver operating curve (AUC). Additionally, using the Swedish Women’s Lifestyle and Health Cohort Study, we assessed model calibration and clinical usefulness.

Results  The risk prediction model included hair color, nevus density, first-degree family history of melanoma, previous nonmelanoma skin cancer, and lifetime sunbed use. On internal validation, the AUC was 0.70 (95% CI, 0.67-0.73). On external validation, the AUC was 0.66 (95% CI, 0.63-0.69) in the Western Australia Melanoma Study, 0.67 (95% CI, 0.65-0.70) in the Leeds Melanoma Case-Control Study, 0.64 (95% CI, 0.62-0.66) in the Epigene-QSkin Study, and 0.63 (95% CI, 0.60-0.67) in the Swedish Women’s Lifestyle and Health Cohort Study. Model calibration showed close agreement between predicted and observed numbers of incident melanomas across all deciles of predicted risk. In the external validation setting, there was higher net benefit when using the risk prediction model to classify individuals as high risk compared with classifying all individuals as high risk.

Conclusions and Relevance  The melanoma risk prediction model performs well and may be useful in prevention interventions reliant on a risk assessment using self-assessed risk factors.

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Figure 1.
Observed and Predicted Numbers of Incident Melanomas by Deciles of Predicted Risk Over 20 Years of Follow-up Using Australian Rates From 2007 Through 2009

This graph compares the observed and predicted numbers of incident melanomas by deciles of predicted risk over 20 years of follow-up.

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Figure 2.
Decision Curves Obtained From Plotting the Net Benefit at Different 20-Year Absolute Risk Thresholds

This graph compares the decision curves from classifying individuals as high risk using the risk prediction model, classifying all individuals as high risk, and classifying all individuals as low risk (horizontal line at 0) over 20 years of follow-up. Net benefit at different 20-year absolute risk thresholds is calculated as {true-positive classifications – [% risk threshold/(100 − % risk threshold) × false-positive classifications]}/total number of participants (see the Results section).

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