GenoMEL, comprising major familial melanoma research groups from North America, Europe, Asia, and Australia has created the largest familial melanoma sample yet available to characterize mutations in the high-risk melanoma susceptibility genes CDKN2A/alternate reading frames (ARF), which encodes p16 and p14ARF, and CDK4 and to evaluate their relationship with pancreatic cancer (PC), neural system tumors (NST), and uveal melanoma (UM). This study included 466 families (2,137 patients) with at least three melanoma patients from 17 GenoMEL centers. Overall, 41% (n = 190) of families had mutations; most involved p16 (n = 178). Mutations in CDK4 (n = 5) and ARF (n = 7) occurred at similar frequencies (2-3%). There were striking differences in mutations across geographic locales. The proportion of families with the most frequent founder mutation(s) of each locale differed significantly across the seven regions (P = 0.0009). Single founder CDKN2A mutations were predominant in Sweden (p.R112_L113insR, 92% of family's mutations) and the Netherlands (c.225_243del19, 90% of family's mutations). France, Spain, and Italy had the same most frequent mutation (p.G101W). Similarly, Australia and United Kingdom had the same most common mutations (p.M53I, c.IVS2-105A>G, p.R24P, and p.L32P). As reported previously, there was a strong association between PC and CDKN2A mutations (P < 0.0001). This relationship differed by mutation. In contrast, there was little evidence for an association between CDKN2A mutations and NST (P = 0.52) or UM (P = 0.25). There was a marginally significant association between NST and ARF (P = 0.05). However, this particular evaluation had low power and requires confirmation. This GenoMEL study provides the most extensive characterization of mutations in high-risk melanoma susceptibility genes in families with three or more melanoma patients yet available. (Cancer Res 2006; 66(20): 9818-28)

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Background Evolving dermoscopic terminology motivated us to initiate a new consensus. Objective We sought to establish a dictionary of standardized terms. Methods We reviewed the medical literature, conducted a survey, and convened a discussion among experts. Results Two competitive terminologies exist, a more metaphoric terminology that includes numerous terms and a descriptive terminology based on 5 basic terms. In a survey among members of the International Society of Dermoscopy (IDS) 23.5% (n = 201) participants preferentially use descriptive terminology, 20.1% (n = 172) use metaphoric terminology, and 484 (56.5%) use both. More participants who had been initially trained by metaphoric terminology prefer using descriptive terminology than vice versa (9.7% vs 2.6%, P < .001). Most new terms that were published since the last consensus conference in 2003 were unknown to the majority of the participants. There was uniform consensus that both terminologies are suitable, that metaphoric terms need definitions, that synonyms should be avoided, and that the creation of new metaphoric terms should be discouraged. The expert panel proposed a dictionary of standardized terms taking account of metaphoric and descriptive terms. Limitations A consensus seeks a workable compromise but does not guarantee its implementation. Conclusion The new consensus provides a revised framework of standardized terms to enhance the consistent use of dermoscopic terminology.

Prophylaxis with oral minocycline may be useful in decreasing the severity of the acneiform rash during the first month of cetuximab treatment. Topical tazarotene is not recommended for management of cetuximab-related rash.

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Summary Background Over the last few years, several articles on dermoscopy of non‐neoplastic dermatoses have been published, yet there is poor consistency in the terminology among different studies. Objectives We aimed to standardize the dermoscopic terminology and identify basic parameters to evaluate in non‐neoplastic dermatoses through an expert consensus. Methods The modified Delphi method was followed, with two phases: (i) identification of a list of possible items based on a systematic literature review and (ii) selection of parameters by a panel of experts through a three‐step iterative procedure (blinded e‐mail interaction in rounds 1 and 3 and a face‐to‐face meeting in round 2). Initial panellists were recruited via e‐mail from all over the world based on their expertise on dermoscopy of non‐neoplastic dermatoses. Results Twenty‐four international experts took part in all rounds of the consensus and 13 further international participants were also involved in round 2. Five standardized basic parameters were identified: (i) vessels (including morphology and distribution); (ii) scales (including colour and distribution); (iii) follicular findings; (iv) ‘other structures’ (including colour and morphology); and (v) ‘specific clues’. For each of them, possible variables were selected, with a total of 31 different subitems reaching agreement at the end of the consensus (all of the 29 proposed initially plus two more added in the course of the consensus procedure). Conclusions This expert consensus provides a set of standardized basic dermoscopic parameters to follow when evaluating inflammatory, infiltrative and infectious dermatoses. This tool, if adopted by clinicians and researchers in this field, is likely to enhance the reproducibility and comparability of existing and future research findings and uniformly expand the universal knowledge on dermoscopy in general dermatology. What's already known about this topic? Over the last few years, several papers have been published attempting to describe the dermoscopic features of non‐neoplastic dermatoses, yet there is poor consistency in the terminology among different studies. What does this study add? The present expert consensus provides a set of standardized basic dermoscopic parameters to follow when evaluating inflammatory, infiltrative and infectious dermatoses. This consensus should enhance the reproducibility and comparability of existing and future research findings and uniformly expand the universal knowledge on dermoscopy in general dermatology.

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Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists.