Faculty Bibliography

The incidence of cutaneous melanoma is rising faster than that of almost any other cancer in the United States. Acral lentiginous melanoma is a subtype of melanoma that involves the palms, soles, and nail beds. Although it is one of the rarer types of melanoma, it has a poorer prognosis than other more common subtypes. We describe a case of plantar acral melanoma in a 66-year-old woman that was initially misdiagnosed as a traumatic foot ulcer. We highlight this case to emphasize the importance of close observation and biopsy of ulcerative lesions of the foot that have atypical features or are refractory to standard treatment.

Pigmented vulvar lesions are present in approximately 1 in 10 women and include melanocytic and nonmelanocytic proliferations. Vulvar nevi, melanosis, and melanoma are particularly challenging because of the similarity of their clinical and/or histopathological presentation. As a result, they are often difficult to diagnose, may result in patient and physician anxiety, and can lead to unneeded, potentially disfiguring surgical procedures. Because it is often detected late, vulvar melanoma carries a poor prognosis with associated significant morbidity and mortality, underscoring the importance of prompt recognition and treatment. In this review, we analyze the distinct epidemiologic, clinical, and histopathologic characteristics of vulvar nevi, melanosis, and melanoma, discuss treatment options, and propose a practical, systematic approach to facilitate formulation of a differential diagnosis and initiation of appropriate management.

Although personal melanoma risk factors are well established, the contribution of socioeconomic factors, including clothing styles, social norms, medical paradigms, perceptions of tanned skin, economic trends, and travel patterns, to melanoma incidence has not been fully explored. We analyzed artwork, advertisements, fashion trends, and data regarding leisure-time activities to estimate historical changes in UV skin exposure. We used data from national cancer registries to compare melanoma incidence rates with estimated skin exposure and found that they rose in parallel. Although firm conclusions about melanoma causation cannot be made in an analysis such as this, we provide a cross-disciplinary, historical framework in which to consider public health and educational measures that may ultimately help reverse melanoma incidence trends. (Am J Public Health. Published online ahead of print September 11, 2014: e1-e8. doi:10.2105/AJPH.2014.302185).

BACKGROUND: Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model. METHODS: Using 875 melanoma cases and 765 controls from the population-based Minnesota Skin Health Study we compared the predictive ability of a clinical melanoma risk model (Model A) to an enhanced model (Model F) using receiver operating characteristic (ROC) curves. Model A used self-reported conventional risk factors including mole phenotype categorized as "none", "few", "some" or "many" moles. Model F added MC1R genotype and measures of indoor and outdoor UV exposure to Model A. We also assessed the predictive ability of these models in subgroups stratified by mole phenotype (e.g. nevus-resistant ("none" and "few" moles) and nevus-prone ("some" and "many" moles)). RESULTS: Model A (the reference model) yielded an area under the ROC curve (AUC) of 0.72 (95% CI = 0.69, 0.74). Model F was improved with an AUC = 0.74 (95% CI = 0.71-0.76, p<0.01). We also observed substantial variations in the AUCs of Models A & F when examined in the nevus-prone and nevus-resistant subgroups. CONCLUSIONS: These results demonstrate that adding genotypic information and environmental exposure data can increase the predictive ability of a clinical melanoma risk model, especially among nevus-prone individuals.