Search

Background: People at high risk of developing melanoma are usually identified by pigmentary and naevus phenotypes. Objective: We examined whether associations of these phenotypes with melanoma risk differed by ambient sun exposure or participant characteristics in two population‐based, case–control studies with comparable ancestry but different ambient sun exposure. Methods: Data were analysed from 616 cases and 496 controls from the Australian Melanoma Family Study and 2012 cases and 504 controls from the Leeds (UK) case–control study. Questionnaire, interview and dermatological skin examination data were collected using the same measurement protocols. Relative risks were estimated as odds ratios using unconditional logistic regression, adjusted for potential confounders. Results: Hair and skin colour were the strongest pigmentary phenotype risk factors. All associations of pigmentary phenotype with melanoma risk were similar across countries. The median number of clinically assessed naevi was approximately three times higher in Australia than Leeds, but the relative risks for melanoma associated with each additional common or dysplastic naevus were higher for Leeds than Australia, especially for naevi on the upper and lower limbs. Higher naevus counts on the head and neck were associated with a stronger relative risk for melanoma for women than men. The two countries had similar relative risks for melanoma based on self‐reported naevus density categories, but personal perceptions of naevus number differed by country. There was no consistent evidence of interactions between phenotypes on risk. Conclusions: Classifying people at high risk of melanoma based on their number of naevi should ideally take into account their country of residence, type of counts (clinical or self‐reported), body site on which the naevus counts are measured and sex. The presence of naevi may be a stronger indicator of a genetic predisposition in the UK than in Australia based on less opportunity for sun exposure to influence naevus development. [ABSTRACT FROM AUTHOR]

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 Linked Article: Vuong et al. Br J Dermatol 2020; 182:1262–1268 [ABSTRACT FROM AUTHOR]