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The development of new primary melanomas in patients treated with vemurafenib has been reported recently in a study by Perier-Muzet et al. The primary outcome of the study was to describe the dermoscopic changes that prompted excision of those melanomas. However, the crucial point raised by the study is the large number of melanomas that were detected.

We describe a fully automated system for the classification of acral volar melanomas. We used a total of 213 acral dermoscopy images (176 nevi and 37 melanomas). Our automatic tumor area extraction algorithm successfully extracted the tumor in 199 cases (169 nevi and 30 melanomas), and we developed a diagnostic classifier using these images. Our linear classifier achieved a sensitivity (SE) of 100%, a specificity (SP) of 95.9%, and an area under the receiver operating characteristic curve (AUC) of 0.993 using a leave-one-out cross-validation strategy (81.1% SE, 92.1% SP; considering 14 unsuccessful extraction cases as false classification). In addition, we developed three pattern detectors for typical dermoscopic structures such as parallel ridge, parallel furrow, and fibrillar patterns. These also achieved good detection accuracy as indicated by their AUC values: 0.985, 0.931, and 0.890, respectively. The features used in the melanoma-nevus classifier and the parallel ridge detector have significant overlap.

In 2002, we reported on three patients presenting with melanocytic nevi lacking pigmentation, which we named "white" dyplastic melanocytic nevi (DMN) due to their peculiar clinical appearance for white to pale red macules with accentuated skin markings and a silvery "shining" when observed with tangential light (Zalaudek et al.,2002). Notably, all three patients had melanoma, and in one patient white DMN were associated with two primary amelanotic melanomas (AMMs).