Development and External Validation of a Melanoma Risk Prediction Model Based on Self-assessed Risk Factors.
Identifying individuals at high risk of melanoma can optimize primary and secondary prevention strategies.



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



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).



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.



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.



The melanoma risk prediction model performs well and may be useful in prevention interventions reliant on a risk assessment using self-assessed risk factors.
Cancer Epidemiology and Prevention Research, Sydney School of Public Health, University of Sydney, Sydney, Australia2School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia.
Kylie
K
Vuong
Cancer Epidemiology and Prevention Research, Sydney School of Public Health, University of Sydney, Sydney, Australia.
Bruce K
BK
Armstrong
Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, Arctic University of Norway, Tromsø, Norway4Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden5Department of Research, Cancer R.
Elisabete
E
Weiderpass
Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, Arctic University of Norway, Tromsø, Norway.
Eiliv
E
Lund
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden7Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Hans-Olov
HO
Adami
Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Marit B
MB
Veierod
Leeds Institute of Cancer and Pathology, Faculty of Medicine and Health, Leeds University, Leeds, United Kingdom.
Jennifer H
JH
Barrett
Leeds Institute of Cancer and Pathology, Faculty of Medicine and Health, Leeds University, Leeds, United Kingdom.
John R
JR
Davies
Leeds Institute of Cancer and Pathology, Faculty of Medicine and Health, Leeds University, Leeds, United Kingdom.
D Timothy
DT
Bishop
Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
David C
DC
Whiteman
Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Catherine M
CM
Olsen
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
John L
JL
Hopper
Centre for Cancer Research, Westmead Institute for Medical Research, University of Sydney, Westmead, Australia13Melanoma Institute Australia, University of Sydney, North Sydney, Australia.
Graham J
GJ
Mann
Cancer Epidemiology and Prevention Research, Sydney School of Public Health, University of Sydney, Sydney, Australia13Melanoma Institute Australia, University of Sydney, North Sydney, Australia.
Anne E
AE
Cust
Sydney School of Public Health, University of Sydney, Sydney, Australia.
Kevin
K
McGeechan
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001315:Australia / Q000453:epidemiology; D002280:Basal Cell Carcinoma / Q000175:diagnosis*; D002294:Carcinoma, Squamous Cell / Q000175:diagnosis*; D016022:Case-Control Studies; D002648:Child; D059026:Diagnostic Self Evaluation; D005260:Female; D006200:Hair Color; D006801:Humans; D016015:Logistic Models; D008297:Male; D008545:Melanoma / Q000453:epidemiology / Q000235:genetics / Q000517:prevention & control*; D008875:Middle Aged; D015999:Multivariate Analysis; D009506:Nevus / Q000473:pathology*; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012878:Skin Neoplasms / Q000453:epidemiology / Q000235:genetics / Q000473:pathology* / Q000517:prevention & control*; D013548:Sweden / Q000453:epidemiology; D014466:Ultraviolet Rays / Q000009:adverse effects; D006113:United Kingdom / Q000453:epidemiology; D055815:Young Adult