Development of a new method to calculate individuals' melanoma risk. (1st May 2020)

• Record Type: Journal Article
• Authors: Vuong, K.; Armstrong, B.K.; Drummond, M.; Hopper, J.L.; Barrett, J.H.; Davies, J.R.; Bishop, D.T.; Newton‐Bishop, J.; Aitken, J.F.; Giles, G.G.; Schmid, H.; Jenkins, M.A.; Mann, G.J.; McGeechan, K.; Cust, A.E.
• Is Part Of: British journal of dermatology. Volume 182:Number 5(2020)
• Page Start: e166
• Page End: e166
• Publication Date: 2020-05-01
• Subjects: Dermatology -- Periodicals; Skin -- Diseases -- Periodicals; 616.5
• DOI: 10.1111/bjd.18992
• Languages: English
• ISSNs: 0007-0963

Abstract

Summary: Melanoma is a cancer that develops in the melanocytes, the pigment cells of the skin. Melanoma incidence rates, which show how common it is, have been increasing in people with fair skin, with the highest rates in Australia, New Zealand, North America and Europe. In Australia, one in 14 men and one in 24 women will be diagnosed with melanoma during their lifetime. Risk factors for melanoma include sun exposure, sunbed use, the number of moles on the skin, the skin's sensitivity to the sun, the number of freckles, skin colour, eye colour, hair colour, family history and a number of susceptibility genes (genes that place you at higher risk). Melanoma risk prediction models, which combine many individual melanoma risk factors into an overall risk, may be useful in the prevention of melanoma by matching prevention strategies (interventions) to the patient's melanoma risk levels. Many melanoma risk prediction models use self‐assessed risk factors because it is quicker and less costly compared to the clinical (medical) assessment of risk factors, but many people underestimate the number of moles they have. This study aims to derive a melanoma risk prediction model, which includes the number of moles on the body and solar lentigines (darker skin patches caused by the sun) as clinically‐assessed risk factors, and pigmentation (skin colour) characteristics, sun exposure, family history and history of skin cancer as self‐assessed risk factors. The number of moles is the strongest risk factor in the model. The model performs well on discrimination, the model's ability to distinguish between individuals with and without melanoma. In a clinical setting, this model may help clinicians identify patients for targeted prevention strategies based on the patient's melanoma risk. This is a summary of the study: Development and external validation study of a melanoma risk prediction model incorporating clinically assessed naevi and solar lentigines