Dermoscopy of Actinic Keratosis: Is There a True Differentiation between Non-Pigmented and Pigmented Lesions?

Dermoscopic features of actinic keratosis (AK) have been widely studied, but there is still little evidence for their diagnostic accuracy. Our study investigates whether established dermoscopic criteria are reliable predictors in differentiating non-pigmented actinic keratosis (NPAK) from pigmented actinic keratosis (PAK). For this purpose, dermoscopic images of 83 clinically diagnosed AK (45 NPAK, 38PAK) were examined, and the sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) were assessed. Features with statistical significance were the red pseudo-network (p = 0.02) for NPAK and the pigmented pseudo-network (p < 0.001) with a pigment intensity value even less than 10% for PAK (p = 0.001). Pigmented pseudo-network (Se: 89%, Sp: 77%, PPV: 77%, NPV: 89%) with a pigment intensity value of more than 10% (Se: 90%, Sp: 86%, PPV: 79%, NPV: 93%) had excellent diagnostic accuracy for PAK. Scale and widened follicular openings with yellowish dots surrounded by white circles were equally represented in both variants of AK. Linear wavy vessels and shiny streaks were more prominently observed in NPAK, as were rosettes in PAK, but these results failed to meet statistical significance. The red starburst pattern was near statistical significance for PAK. Therefore, pigmentation is the strongest dermoscopic predictor for the differentiation between NPAK and PAK.

Prediction of Melanoma Risk in a Southern European Population Based on a Weighted Genetic Risk Score.

Many single nucleotide polymorphisms (SNPs) have been described as putative risk factors for melanoma. The aim of our study was to validate the most prominent genetic risk loci in an independent Greek melanoma case-control dataset and to assess their cumulative effect solely or combined with established phenotypic risk factors on individualized risk prediction. We genotyped 59 SNPs in 800 patients and 800 controls and tested their association with melanoma using logistic regression analyses. We constructed a weighted genetic risk score (GRSGWS) based on SNPs that showed genome-wide significant (GWS) association with melanoma in previous studies and assessed their impact on risk prediction. Fifteen independent SNPs from 12 loci were significantly associated with melanoma (P < 0.05). Risk score analysis yielded an odds ratio of 1.36 per standard deviation increase of the GRSGWS (P = 1.1 × 10(-7)). Individuals in the highest 20% of the GRSGWS had a 1.88-fold increase in melanoma risk compared with those in the middle quintile. By adding the GRSGWS to a phenotypic risk model, the C-statistic increased from 0.764 to 0.775 (P = 0.007). In summary, the GRSGWS is associated with melanoma risk and achieves a modest improvement in risk prediction when added to a phenotypic risk model.