Kylie Vuong1, Bruce K Armstrong2, Elisabete Weiderpass3, Eiliv Lund4, Hans-Olov Adami5, Marit B Veierod6, Jennifer H Barrett7, John R Davies7, D Timothy Bishop7, David C Whiteman8, Catherine M Olsen8, John L Hopper9, Graham J Mann10, Anne E Cust11, Kevin McGeechan12. 1. 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. 2. Cancer Epidemiology and Prevention Research, Sydney School of Public Health, University of Sydney, Sydney, Australia. 3. 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. 4. Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, Arctic University of Norway, Tromsø, Norway. 5. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden7Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. 6. Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway. 7. Leeds Institute of Cancer and Pathology, Faculty of Medicine and Health, Leeds University, Leeds, United Kingdom. 8. Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia. 9. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia. 10. Centre for Cancer Research, Westmead Institute for Medical Research, University of Sydney, Westmead, Australia13Melanoma Institute Australia, University of Sydney, North Sydney, Australia. 11. Cancer Epidemiology and Prevention Research, Sydney School of Public Health, University of Sydney, Sydney, Australia13Melanoma Institute Australia, University of Sydney, North Sydney, Australia. 12. Sydney School of Public Health, University of Sydney, Sydney, Australia.
Abstract
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.
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.
Authors: June K Robinson; Namita Jain; Ashfaq A Marghoob; William McGaghie; Michael MacLean; Pedram Gerami; Brittney Hultgren; Rob Turrisi; Kimberly Mallett; Gary J Martin Journal: J Gen Intern Med Date: 2018-02-05 Impact factor: 5.128
Authors: Haoming Xu; Michael A Marchetti; Stephen W Dusza; Esther Chung; Maira Fonseca; Alon Scope; Alan C Geller; Marilyn Bishop; Ashfaq A Marghoob; Allan C Halpern Journal: JAMA Dermatol Date: 2017-10-01 Impact factor: 10.282
Authors: K Vuong; B K Armstrong; M Drummond; J L Hopper; J H Barrett; J R Davies; D T Bishop; J Newton-Bishop; J F Aitken; G G Giles; H Schmid; M A Jenkins; G J Mann; K McGeechan; A E Cust Journal: Br J Dermatol Date: 2019-09-22 Impact factor: 9.302
Authors: Mariah M Johnson; Sancy A Leachman; Lisa G Aspinwall; Lee D Cranmer; Clara Curiel-Lewandrowski; Vernon K Sondak; Clara E Stemwedel; Susan M Swetter; John Vetto; Tawnya Bowles; Robert P Dellavalle; Larisa J Geskin; Douglas Grossman; Kenneth F Grossmann; Jason E Hawkes; Joanne M Jeter; Caroline C Kim; John M Kirkwood; Aaron R Mangold; Frank Meyskens; Michael E Ming; Kelly C Nelson; Michael Piepkorn; Brian P Pollack; June K Robinson; Arthur J Sober; Shannon Trotter; Suraj S Venna; Sanjiv Agarwala; Rhoda Alani; Bruce Averbook; Anna Bar; Mirna Becevic; Neil Box; William E Carson; Pamela B Cassidy; Suephy C Chen; Emily Y Chu; Darrel L Ellis; Laura K Ferris; David E Fisher; Kari Kendra; David H Lawson; Philip D Leming; Kim A Margolin; Svetomir Markovic; Mary C Martini; Debbie Miller; Debjani Sahni; William H Sharfman; Jennifer Stein; Alexander J Stratigos; Ahmad Tarhini; Matthew H Taylor; Oliver J Wisco; Michael K Wong Journal: Melanoma Manag Date: 2017-03-01
Authors: Leonardo Vinícius Monteiro de Assis; Gabriela Sarti Kinker; Maria Nathália Moraes; Regina P Markus; Pedro Augusto Fernandes; Ana Maria de Lauro Castrucci Journal: Front Oncol Date: 2018-06-12 Impact factor: 6.244