Background: High naevus counts and ultraviolet photodamage are strong risk factors for melanoma. However, whole-of-body measures fail to capture variability across body sites. Three-dimensional total body photography (3D-TBP) and artificial intelligence (AI) allow us the opportunity to automate the extraction of site-specific distributions of naevi and photodamage.
Objectives: To identify distinct phenotypic patterns associated with melanoma in a cohort of people at high risk of advanced melanoma, using combined 3D-TBP, AI and unsupervised clustering.
Methods: Participants with a history of melanoma (diagnosed aged > 50 years) underwent 3D-TBP. Site-specific photodamage and naevus counts were assessed using density-based spatial clustering of applications with noise to identify body site-dependent phenotypic patterns. Melanoma prevalence (none, single, multiple) relative to phenotypic pattern was evaluated using population prevalence ratios (PPRs).
Results: Analysis of 117 individuals found four phenotypic patterns of increasing severity: moderate V-neck photodamage with few naevi [median 38; interquartile range (IQR) 27-72] in 28 patients (24%); moderate generalized photodamage with several naevi (median 155; IQR 90-259) in 31 patients (26%); moderate V-neck photodamage with many naevi (median 204) in 20 patients (17%); and severe generalized photodamage with few naevi (median 37; IQR 21-72) naevi in 38 patients (32%). No individuals had severe photodamage and several-to-many naevi. Interpattern comparisons revealed that participants with the mildest phenotypic pattern were least likely to be affected by invasive melanomas [PPR 1.51, 95% confidence interval (CI) 1.01-2.26], whereas those with the most severe phenotypic pattern were more likely to be affected by multiple invasive melanomas (PPR 2.00, 95% CI 1.06-3.77). The prevalence of melanoma in situ was consistent across patterns. Melanoma was more likely at sites of large naevi (> 5 mm; P < 0.05) in those with moderate photodamage patterns but were independent of naevi (> 2 mm) in individuals with severe photodamage patterns. From a control cohort unaffected by melanoma (n = 114), only 18 (15.8%) matched with a high-risk phenotypic pattern.
Conclusions: Three-dimensional TBP phenotyping of an older Australian cohort at high risk of invasive melanoma revealed four distinct phenotypic patterns associated with risk of the disease. Individuals with severe photodamage and relatively few naevi had a significantly higher risk of developing multiple invasive melanomas. For individuals with moderate photodamage, the risk of invasive melanoma was positively associated with the number of naevi. Thus, comprehensive phenotypes may be more predictive for the diagnosis and site of invasive melanoma, which may help with nuanced risk stratification and customized surveillance.
Melanoma is a rare but serious type of skin cancer. Finding melanoma at an early stage means patients may have a better outcome. People at high risk of developing melanoma often have regular skin checks to look for signs of the disease. For example, moles and sun damage are associated with an increased risk of melanoma. Doctors visually assess the number of moles and sun damage to assess risk and decide how patients are monitored. In this study, we wanted to find out whether the location of moles and sun damage was associated with the risk of melanoma. Using 3D photography, we constructed digital avatars of people diagnosed with melanoma. The patients were all older than 50 years of age. We found four distinct patterns. The first pattern involved only a few moles with moderate sun damage. The second involved a few moles with severe sun damage. The third pattern was several moles with moderate generalized sun damage. Finally, the fourth pattern was lots of moles with moderate sun damage. No people had severe sun damage and lots of moles. We also found that a type of melanoma called ‘invasive melanoma’ was more common in people with a high number of moles and sun damage. However, rates of another type of melanoma called ‘non-invasive melanoma’ were not affected by the number of moles or amount of sun damage. For people with moderate sun damage, melanoma was more likely to occur in areas where there were higher numbers of large moles. But, in people with severe sun damage, the mole count at specific sites did not predict melanoma. Our findings suggest that sun damage and where moles are around the body might help doctors tailor how they assess the risk of melanoma in people. Distinctive patterns could be found automatically with 3D photography and AI, or recognized by an experienced doctor. This information could identify people and body sites at greatest risk for invasive melanoma. It would allow for screening and surveillance to be customized for an individual patient.
© The Author(s) 2025. Published by Oxford University Press on behalf of British Association of Dermatologists.