Classification of naevus distribution patterns in the Brisbane naevus morphology study

Abstract

There are multiple risk factors which contribute to cutaneous melanoma, including but not limited to, ultraviolet (UV) radiation exposure, the amount of freckling, skin and hair colour, skin phototype, and personal and familial melanoma history. One of the single strongest risk factors for cutaneous melanoma is a high naevus count. In the clinical setting, thorough total body examinations are hindered by the practical limitations of counting high numbers of naevi. To overcome this problem, multiple prediction models which estimate naevus count and cutaneous melanoma risk have been proposed. However, the progression and standardisation of these models has been hampered by the lack of independent validation, variation in methods and incorporated variables, and the absence of consensus as to what constitutes 'at risk'. Consequently, rapidly and accurately identifying these 'at risk' individuals in the clinical setting remains difficult. Individuals with multiple naevi with distinct naevus distribution patterns have been observed clinically. The concept of naevus distribution pattern has not been thoroughly described in the literature nor have these 'patterns' been formally characterised. Through this study, we aim to determine if clinically distinct naevus distribution patterns, in at risk individuals, can be recognised, characterised and classified. Furthermore, whether a stratification model can be developed for the future classification of individuals based on naevus distribution pattern. 2D and 3D whole body images were captured from 1225 high risk individuals (with personal and/or familial melanoma history) selected from the Brisbane Naevus Morphology Study (BNMS) using the FotoFinder imaging system (FotoFinder Systems GmbH, Germany) or the Vectra WB360 imaging system (Canfield Scientific Inc, USA). To ensure the accurate identification of true naevi, only naevi ≥ 5 mm were included in naevus counts. Naevi ≥ 5 mm were counted on the head and neck, back, chest and abdomen, upper limbs and lower limbs. Naevus distribution clusters were derived using mclust and k-means cluster analysis based on anatomical regional naevus counts ≥ 5 mm. Naevus counts were adjusted for both age and sex. Individual pigmentation phenotypes and genotypes were also assessed. Three distinct clusters of naevus distribution pattern were identified using mclust analysis in the BNMS population. Cluster 1 (N=427) contained participants with a high total body naevus count (mean 49 ± 34 naevi ≥ 5 mm) with naevi predominantly centralised to the trunk and either the upper or lower limbs. Cluster 2 (N=552) constituted participants with a lower total body naevus count (mean 10 ± 6 naevi ≥ 5 mm) and lower relative naevus counts at Publications included in this thesis No publications included.