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The acid-sensing ion channels ASIC1 and ASIC2, as well as the transient receptor potential vanilloid channels TRPV1 and TRPV4, are proton-gated cation channels that can be activated by low extracellular pH (pH e ), which is a hallmark of the tumor microenvironment in solid tumors. However, the role of these channels in the development of skin tumors is still unclear. In this study, we investigated the expression profiles of ASIC1, ASIC2, TRPV1 and TRPV4 in malignant melanoma (MM), squamous cell carcinoma (SCC), basal cell carcinoma (BCC) and in nevus cell nevi (NCN). We conducted immunohistochemistry using paraffin-embedded tissue samples from patients and found that most skin tumors express ASIC1/2 and TRPV1/4. Striking results were that BCCs are often negative for ASIC2, while nearly all SCCs express this marker. Epidermal MM sometimes seem to lack ASIC1 in contrast to NCN. Dermal portions of MM show strong expression of TRPV1 more frequently than dermal NCN portions. Some NCN show a decreasing ASIC1/2 expression in deeper dermal tumor tissue, while MM seem to not lose ASIC1/2 in deeper dermal portions. ASIC1, ASIC2, TRPV1 and TRPV4 in skin tumors might be involved in tumor progression, thus being potential diagnostic and therapeutic targets.

Background: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses., Objectives: We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone., Methods: We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques. Furthermore, we tested a naive combination of patient data and an image classifier: for cases interpreted as 'uncertain' (CNN output score <0.7), the decision of the CNN was replaced by the decision of the patient data classifier., Results: The CNN on its own achieved the best performance (mean ± standard deviation of five individual runs) with AUROC of 92.30% ± 0.23% and balanced accuracy of 83.17% ± 0.38%. While the classification performance was not significantly improved in general by any of the tested fusions, naive strategy of replacing the image classifier with the patient data classifier on slides with low output scores improved balanced accuracy to 86.72% ± 0.36%., Conclusion: In most cases, the CNN on its own was so accurate that patient data integration did not provide any benefit. However, incorporating patient data for lesions that were classified by the CNN with low 'confidence' improved balanced accuracy., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Haferkamp S. reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Novartis, Roche, BMS, Amgen and MSD outside the submitted work. Hauschild A. reports clinical trial support, speaker's honoraria or consultancy fees from the following companies: Amgen, BMS, Merck Serono, MSD, Novartis, Oncosec, Philogen, Pierre Fabre, Provectus, Regeneron, Roche, OncoSec, Sanofi-Genzyme and Sun Pharma, outside the submitted work. BS reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Incyte, Novartis, Roche, BMS and MSD, research funding from BMS, Pierre Fabre Pharmaceuticals and MSD, and travel support from Novartis, Roche, BMS, Pierre Fabre Pharmaceuticals and Amgen, outside the submitted work. JSU is on the advisory board or has received honoraria and travel support from Amgen, Bristol Myers Squibb, GSK, LeoPharma, Merck Sharp and Dohme, Novartis, Pierre Fabre, Roche, outside the submitted work. WS received travel expenses for attending meetings and/or (speaker) honoraria from Abbvie, Almirall, Bristol-Myers Squibb, Celgene, Janssen, LEO Pharma, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi Genzyme and UCB outside the submitted work. FM has received travel support or/and speaker's fees or/and advisor's honoraria by Novartis, Roche, BMS, MSD and Pierre Fabre and research funding from Novartis and Roche. TJB reports owning a company that develops mobile applications (Smart Health Heidelberg GmbH, Handschuhsheimer Landstr. 9/1, 69120 Heidelberg, https://smarthealth.de). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Skin disease is a prevalent condition all over the world. Computer vision-based technology for automatic skin lesion classification holds great promise as an effective screening tool for early diagnosis. In this paper, we propose an accurate and interpretable deep learning pipeline to achieve such a goal. Comparing with existing research, we would like to highlight the following aspects of our model. 1) Rather than a single model, our approach ensembles a set of deep learning architectures to achieve better classification accuracy; 2) Generative adversarial network (GAN) is involved in the model training to promote data scale and diversity; 3) Local interpretable model-agnostic explanation (LIME) strategy is applied to extract evidence from the skin images to support the classification results. Our experimental results on real-world skin image corpus demonstrate the effectiveness and robustness of our method. The explainability of our model further enhances its applicability in real clinical practice., (©2019 AMIA - All rights reserved.)

Background: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25-26% of discordance for classifying a benign nevus versus malignant melanoma. A recent study indicated the potential of deep learning to lower these discordances. However, the performance of deep learning in classifying histopathologic melanoma images was never compared directly to human experts. The aim of this study is to perform such a first direct comparison., Methods: A total of 695 lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides of these lesions were digitalised via a slide scanner and then randomly cropped. A total of 595 of the resulting images were used to train a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison to 11 histopathologists. Three combined McNemar tests comparing the results of the CNNs test runs in terms of sensitivity, specificity and accuracy were predefined to test for significance (p < 0.05)., Findings: The CNN achieved a mean sensitivity/specificity/accuracy of 76%/60%/68% over 11 test runs. In comparison, the 11 pathologists achieved a mean sensitivity/specificity/accuracy of 51.8%/66.5%/59.2%. Thus, the CNN was significantly (p = 0.016) superior in classifying the cropped images., Interpretation: With limited image information available, a CNN was able to outperform 11 histopathologists in the classification of histopathological melanoma images and thus shows promise to assist human melanoma diagnoses., (Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level precision. However, a statistically significant improvement compared with dermatologist classification has not been reported to date., Methods: For this comparative study, 4204 biopsy-proven images of melanoma and nevi (1:1) were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated. For the experiment, an additional 804 biopsy-proven dermoscopic images of melanoma and nevi (1:1) were randomly presented to dermatologists of nine German university hospitals, who evaluated the quality of each image and stated their recommended treatment (19,296 recommendations in total). Three McNemar's tests comparing the results of the CNN's test runs in terms of sensitivity, specificity and overall correctness were predefined as the main outcomes., Findings: The respective sensitivity and specificity of lesion classification by the dermatologists were 67.2% (95% confidence interval [CI]: 62.6%-71.7%) and 62.2% (95% CI: 57.6%-66.9%). In comparison, the trained CNN achieved a higher sensitivity of 82.3% (95% CI: 78.3%-85.7%) and a higher specificity of 77.9% (95% CI: 73.8%-81.8%). The three McNemar's tests in 2 × 2 tables all reached a significance level of p < 0.001. This significance level was sustained for both subgroups., Interpretation: For the first time, automated dermoscopic melanoma image classification was shown to be significantly superior to both junior and board-certified dermatologists (p < 0.001)., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports 25-26% of discordance for classifying a benign nevus versus malignant melanoma. Deep learning was successfully implemented to enhance the precision of lung and breast cancer diagnoses. The aim of this study is to illustrate the potential of deep learning to assist human assessment for a histopathologic melanoma diagnosis., Methods: Six hundred ninety-five lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi and 345 melanomas). Only the haematoxylin and eosin stained (H&E) slides of these lesions were digitalised using a slide scanner and then randomly cropped. Five hundred ninety-five of the resulting images were used for the training of a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison with the original class labels., Findings: The total discordance with the histopathologist was 18% for melanoma (95% confidence interval [CI]: 7.4-28.6%), 20% for nevi (95% CI: 8.9-31.1%) and 19% for the full set of images (95% CI: 11.3-26.7%)., Interpretation: Even in the worst case, the discordance of the CNN was about the same compared with the discordance between human pathologists as reported in the literature. Despite the vastly reduced amount of data, time necessary for diagnosis and cost compared with the pathologist, our CNN archived on-par performance. Conclusively, CNNs indicate to be a valuable tool to assist human melanoma diagnoses., (Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Skin cancer is one of most deadly diseases in humans. According to the high similarity between melanoma and nevus lesions, physicians take much more time to investigate these lesions. The automated classification of skin lesions will save effort, time and human life. The purpose of this paper is to present an automatic skin lesions classification system with higher classification rate using the theory of transfer learning and the pre-trained deep neural network. The transfer learning has been applied to the Alex-net in different ways, including fine-tuning the weights of the architecture, replacing the classification layer with a softmax layer that works with two or three kinds of skin lesions, and augmenting dataset by fixed and random rotation angles. The new softmax layer has the ability to classify the segmented color image lesions into melanoma and nevus or into melanoma, seborrheic keratosis, and nevus. The three well-known datasets, MED-NODE, Derm (IS & Quest) and ISIC, are used in testing and verifying the proposed method. The proposed DCNN weights have been fine-tuned using the training and testing dataset from ISIC in addition to 10-fold cross validation for MED-NODE and DermIS-DermQuest. The accuracy, sensitivity, specificity, and precision measures are used to evaluate the performance of the proposed method and the existing methods. For the datasets, MED-NODE, Derm (IS & Quest) and ISIC, the proposed method has achieved accuracy percentages of 96.86%, 97.70%, and 95.91% respectively. The performance of the proposed method has outperformed the performance of the existing classification methods of skin cancer., Competing Interests: The authors have declared that no competing interests exist.

Epidermal nevi are hamartomatous lesions derived from the epidermis and/or adnexal structures of the skin; they have traditionally been classified according to their morphology. New variants have been described in recent years and advances in genetics have contributed to better characterization of these lesions and an improved understanding of their relationship with certain extracutaneous manifestations. In the first part of this review article, we will look at nevi derived specifically from the epidermis and associated syndromes., (Copyright © 2018 AEDV. Publicado por Elsevier España, S.L.U. All rights reserved.)

Epidermal nevi are hamartomatous lesions derived from the epidermis and/or adnexal structures of the skin; they have traditionally been classified according to their morphology. New variants have been described in recent years and advances in genetics have contributed to better characterization of these lesions and an improved understanding of their relationship with certain extracutaneous manifestations. In the second part of this review article, we will look at nevi derived from the adnexal structures of the skin and associated syndromes., (Copyright © 2018 AEDV. Publicado por Elsevier España, S.L.U. All rights reserved.)

Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images-two orders of magnitude larger than previous datasets-consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.

Melanocytic nevi (MN) encompass a range of benign tumors with varying microscopic and macroscopic features. Their development is a multifactorial process under genetic and environmental influences. The clinical importance of MN lies in distinguishing them from melanoma and in recognizing their associations with melanoma risk and cancer syndromes. Historically, the distinction between the different types of MN, as well as between MN and melanoma, was based on clinical history, gross morphology, and histopathological features. While histopathology with clinical correlation remains the gold standard for differentiating and diagnosing melanocytic lesions, in some cases, this may not be possible. The use of dermoscopy has allowed for the assessment of subsurface skin structures and has contributed to the clinical evaluation and classification of MN. Genetic profiling, while still in its early stages, has the greatest potential to refine the classification of MN by clarifying their developmental processes, biological behaviors, and relationships to melanoma. Here we review the most salient clinical, dermoscopic, histopathological, and genetic features of different MN subgroups.

Klippel-Trenaunay syndrome (KTS), originally described as a triad of cutaneous capillary malformation, bone and soft-tissue hypertrophy, as well as venous and lymphatic malformations, has been considered by dermatologists as a distinct diagnostic entity. However, cases with KTS have also been reported to have neurological disorders, developmental delay and digital abnormalities, indicating multisystem involvement. Recently, a number of overgrowth syndromes, with overlapping phenotypic features with KTS, have been identified; these include MCAP and CLOVES syndromes as well as fibroadipose hyperplasia. These conditions harbour mutations in the PIK3CA gene, and they have been included in the PIK3CA-related overgrowth spectrum (PROS). Based on recent demonstrations of PIK3CA mutations also in KTS, it appears that, rather than being a distinct diagnostic entity, KTS belongs to PROS. These observations have potential diagnostic and therapeutic implications for KTS., (© 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Eccrine nevus shows increase in number or size of eccrine glands, whereas hair follicle nevus is composed of densely packed normal vellus hairs, and eccrine-pilar angiomatous nevus reveals increase of eccrine, pilar, and angiomatous structures. No case with increased number of both eccrine glands and hair follicles only in the dermis has been previously reported. A 10-month-old girl presented with cutaneous hamartoma with overlying skin hyperpigmentation on her left hypochondrium since 3 months of age, in whom the lesion was completely excised. Histopathology demonstrated evidently increased number of both eccrine glands and hair follicles in the dermis with reactive hyperplasia of collagen fibers. No recurrence occurred after the tumor was completely excised. A term "hybrid eccrine gland and hair follicle hamartoma" is proposed for this unique lesion.

Seborrheic keratosis (SK) and epidermal nevi (EN) represent benign skin tumors and congenital lesions, respectively. Oncogenic mutations are fundamentally involved in their pathogenesis and SK is characterized by a broad spectrum of somatic mutations in the FGFR3, PIK3CA, RAS, AKT1 and EGFR genes. In contrast to malignant tumors, SK is genetically stable without alterations of tumor suppressor genes. The ENs are caused by postzygotic activating hot spot mutations in FGFR3, PIK3CA and particularly HRAS, resulting in a genetic mosaicism. The size of the lesions and the differentiation potential of the mutated cell into various tissue types depends on the time point of the mutation during embryogenesis. The genetic mosaic may predispose to a later growth of benign and malignant (adnexal) tumors.

Nevus anelasticus represents a rare entity that is most commonly classified as a connective tissue nevus. It typically presents before 20 years of age with asymmetrically distributed white-to-skin-toned or pink-to-red papules or plaques on the trunk and upper extremities. The lesion is defined histopathologically by the absence or degeneration of elastic fibers in the dermis. We report the case of a healthy 17-year-old female who presented with an asymptomatic slowly progressive plaque on the right inferior areola. Histopathologic examination showed the absence of elastic fibers in the papillary and upper reticular dermis and fragmented elastic tissue fibers in the deep reticular dermis. Although there is ongoing controversy regarding the nosology of this uncommon disorder, we propose that it is a distinct entity based on its histopathologic and clinical features., (© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Recent research suggests that scalp naevi differ with respect to their epidemiology, patient characteristics and morphological patterns, but currently a classification of scalp naevi is lacking., Objectives: To investigate the prevalence, together with clinical and dermoscopic features, of scalp naevi detected in persons attending a skin cancer screening programme, and to elaborate a classification of scalp naevi based on their most common morphological patterns., Methods: Participants were recruited during the melanoma prevention programme 'sun watch' of Austrian Cancer Aid in Styria. Each participant received a clinical and dermoscopic total-body skin examination including the scalp. For each participant, demographics and clinical characteristics including number of scalp naevi were recorded. Clinical and dermoscopic photographs of at least one scalp naevus per participant were taken and evaluated for specific clinical and dermoscopic features., Results: In total 867 subjects, including 119 participants (13·7%) with scalp naevi, participated in the study. Compared with those without scalp naevi, subjects with scalp naevi were significantly younger, were more often men and more often exhibited congenital naevi on the body (P < 0·01 for all). Analysis of the clinical and dermoscopic variability of scalp naevi allowed for a proposal to classify scalp naevi into six main groups, namely common, papillomatous, eclipse, congenital, blue and atypical naevus., Conclusions: Scalp naevi can be classified into six morphological groups; scalp lesions deviating from these six main patterns should be carefully managed to rule out melanoma., (© 2013 British Association of Dermatologists.)

This paper presents the first studies on Nevus and Melanoma classification by using Paraconsistent Artificial Neural Network (PANN). Nevus is usually a small growth on the skin while Melanoma is a dangerous skin cancer. The proposed automated process classifies a set of medical images as Nevus and Melanoma based on a methodology grounded on PANN which is able to deal with conflicting, paracomplete and imprecise data directly without trivialization. Such methodology performed promising results considering only border features to classify the sample.

A new skin-imaging method, which was developed recently, gives an informative image data about skin moles by collecting scattered polarized light reflected from their tissue area at different wavelengths and polarizations. This method is called as optical rotational spectropolarimetric imaging (ORSI), which scans the polarization states by continuously rotating a linearly polarized light incident on the lesion and collecting the reflected sequence of images. A novel method to distinguish cancerous from benign moles by analyzing the images obtained using this imaging system is proposed. The proposed method performs an automatic examination of the polarized images according to characteristics such as their cross-image local contrasts, large-scale homogeneity, border disorder, and asymmetry. The pilot study was conducted with 10 subjects, in which two ORSI image sequences at two different wavelengths were taken for each subject. Results show good separation between cancerous and benign moles and between benign moles with high dysplasia., (© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE))

Although nevi are highly polymorphous, it has been suggested that each individual is characterized by only a few dominant patterns of nevi. Therefore, a nevus that does not fit in with these patterns, the "ugly duckling" nevus, is suspicious. Our objective was to study the intra-individual diversity of nevi, using human ability to build "perceived similarity clusters" (PSCs). Nine dermatologists had to cluster all the nevi of 80 patients into PSCs, at the clinical scale (CS) and at the dermoscopic scale (DS) (subset of 30 patients). Nine novices did the same in a subset of 11 patients. The experts identified a mean of 2.8 PSCs/patient at CS. Concordance was higher between experts than between novices at CS and at DS. Despite a trend for more PSCs at DS than at CS, the number of nevus patterns per patient remained low, regardless of the number of nevi. Inter-expert concordance permits a consensus representation of nevus diversity in each individual. Nevus diversity is limited in each patient and constitutes an individual reference system, which we can intuitively perceive. This reference is probably crucial for nevus analysis and melanoma detection and opens perspectives for computer-aided diagnostics.

Background/purpose: Current non-invasive diagnostic procedures to detect skin cancer rely on two-dimensional (2D) views of the skin surface. For example, the most commonly-used ABCD features are extracted from the 2D images of skin lesion. However, because the skin surface is an object in three-dimensional (3D) space, valuable additional information can be obtained from a perspective of 3D skin objects. The aim of this work is to discover the new diagnostic features by considering 3D views of skin artefacts., Methods: A surface tilt orientation parameter was proposed to quantify the skin and the lesion in 3D space. The skin pattern was first extracted from simply captured white light optical clinical (WLC) skin images by high-pass filtering. Then the directions of the projected skin lines were determined by skin pattern analysis. Next the surface tilt orientations of skin and lesion were estimated using the shape from texture technique. Finally the difference of tilt orientation in the lesion and normal skin areas, combined with the ABCD features, was used as a lesion classifier., Results: The proposed method was validated by processing a set of images of malignant melanoma and benign naevi. The scatter plot of classification using the feature of surface tilt orientation alone showed the potential of the new 3D feature, enclosing an area of 0.78 under the ROC curve. The scatter plot of classification, combining the new feature with the ABCD features by use of Principal Component Analysis (PCA), demonstrated an excellent separation of benign and malignant lesions. An ROC plot for this case enclosed an area of 0.85. Compared with the ABCD analysis where the area under the ROC curve was 0.65, it indicated that the surface tilt orientation (3D information) was able to enhance the classification results significantly., Conclusions: The initial classification results show that the surface tilt orientation has a potential to increase lesion classifier accuracy. Combined with the ABCD features, it is very promising to distinguish malignant melanoma from benign lesions., (© 2012 John Wiley & Sons A/S.)

Clinical dermoscopy has provided new insights into the diagnosis and classification of melanocytic neoplasms. There are only limited data on its applications in dermatopathology. In our laboratory, we routinely photograph all skin biopsies with ex vivo dermoscopy (EVD). We retrospectively reviewed 517 cutaneous biopsies with corresponding EVD images to determine whether EVD provides useful ancillary information in the histopathologic diagnosis of melanocytic neoplasms. Four hundred eighty-three cases (93.4%) yielded usable images. The lesions could be categorized according to a published dermoscopic classification system of melanocytic proliferations. Reticular pigmentation correlated with dysplastic nevi, globular pigmentation with congenital nevi, homogenous blue pigmentation with blue nevi, starburst peripheral globular pigmentation with Spitz nevi, and atypical pigment patterns with melanoma. Eighteen of 25 cases (72%) with ambiguous histopathology were assigned a more definite diagnosis when reviewed contemporaneously with EVD images. The surgical margins in 40 cases (7.7%) were reclassified when EVD images were included in the review. We found EVD to be a useful technique and advocate its use for diagnosis and clinical-pathologic correlation.

The pathology of melanocytic tumours remains one of the most challenging and controversial fields in diagnostic histopathology, and it is one of the leading causes for litigation against pathologists. This is due largely to the wide morphological spectrum and often only subtle differentiating features with potential for both under- as well as overdiagnosis of melanoma. Particular pitfalls include the diagnosis of rare and unusual melanoma variants and melanoma resembling naevi. Furthermore, a subset of benign naevi may show concerning features associated more typically with a diagnosis of melanoma. These features include irregular junctional components with pagetoid spread, melanocyte atypia, lack of dermal maturation, dermal mitotic activity, intense melanin pigmentation, a desmoplastic stromal response and dermal regression. This paper focuses on a selected group of benign melanocytic lesions that are notoriously difficult to diagnose, and includes halo naevi, recurrent naevi, mitotically active naevi, desmoplastic naevi, clonal and deep penetrating naevi as well as cellular and plaque type blue naevi. The clinical and histological presentation is discussed with emphasis on clues to the correct diagnosis and distinguishing features from melanoma. Awareness of these entities and their distinguishing clinical and morphological characteristics is essential, as they present major diagnostic pitfalls., (© 2011 Blackwell Publishing Limited.)

The differentiation between benign and worrisome melanocytic lesions may be challenging in the absence of the glaringly obvious clinical features that define a cutaneous malignancy. In such situations, dermoscopy may prove useful in further defining characteristics that are more indicative of a benign lesion, which can ultimately help avoid an unnecessary biopsy. Recognizing of the dermoscopic findings of benign nevi, taking into consideration the predominant pigment pattern and its organization, may aid in the evaluation of pigmented lesions. Benign nevi tend to exhibit symmetry, regularity in shape, and uniformity of dermoscopic structures. This article reviews the clinical and dermoscopic features of common acquired nevi (dermal, compound, and junctional), blue nevi, and congenital nevi.

Objective: To subclassify acquired nevi by dermoscopic pattern., Design: Cross-sectional study with consecutive enrollment., Setting: Pigmented lesion clinics in referral academic medical centers., Participants: Individuals older than 2 years undergoing total skin examination were consecutively recruited between October 1, 2008, and May 31, 2009, and, based on their age, assigned to 1 of 8 groups. For each patient, the location and dermoscopic pattern of all nevi on the torso were recorded. Nevi were dermoscopically subclassified as globular, reticular, mixed (reticular-globular) pattern with peripheral or central globules, or unspecified pattern., Main Outcome Measure: Frequency of dermoscopic nevus subtypes stratified by patient age and location of the nevi., Results: A total of 5481 nevi in 480 individuals were evaluated. The number of all nevus subgroups, except for unspecified pattern nevi, significantly increased before and decreased after the fourth decade of life. Globular nevi were most prevalent on the upper trunk in children and adolescents; the number decreased consistently after the second decade of life. The reticular pattern was the most common nevus pattern after the second decade of life and the most common nevus subgroup on the upper and middle back. Although uncommon, central globular nevi also showed an age-dependent trend, similar to that of reticular nevi. Nevi with the peripheral globular pattern declined rapidly after the third decade of life and were no longer observed after the sixth decade. The number of unspecified pattern nevi was stable across all age groups., Conclusion: Age, dermoscopic pattern, and location of nevi should be jointly considered when evaluating melanocytic lesions.

Context: Until recently, the prevailing paradigm in classification and clinical management of melanocytic proliferations mandated dichotomous classification of all melanocytic lesions as either entirely benign (nevus) or entirely malignant (melanoma). However, some diagnostically challenging lesions cannot be unequivocally classified as nevus or melanoma by histologic evaluation of the primary tumor. Such lesions have been referred to as borderline or melanocytic tumors of uncertain malignant potential., Objective: To review and update the problem of diagnostically difficult melanocytic proliferations and recent concepts regarding borderline melanocytic tumors., Data Sources: Published literature and personal experience of the authors., Conclusions: Preliminary evidence indicates that it may be appropriate to expand the classification scheme of melanocytic neoplasms to include a third diagnostic category of melanocytic lesions of intermediate malignant potential that are capable of metastasis to regional lymph nodes but have limited potential for distant spread. We propose the term melanocytoma for this group of lesions. We believe that a nevus/melanocytoma/melanoma paradigm may provide a useful intellectual framework to understand, research, and clinically manage borderline melanocytic tumors.

Context: Melanocytic proliferations are among the most common neoplasms of the conjunctiva. They often represent challenging lesions for pathologists unfamiliar with unique histologic features of melanocytic proliferations in this location and with nomenclature used by ophthalmologists., Objective: To comprehensively review clinical aspects, pathologic features, and management of melanocytic proliferations of the conjunctiva., Data Sources: Review of the literature and personal experience of the authors., Conclusions: Classification, state of the art, and practical aspects of pathology of melanocytic proliferations of the conjunctiva are discussed.

Nevi or nests of cells may be made up of a variety of cell types. The cell types that live in the epidermis include epidermal cells or keratinocytes, sebaceous glands, hair follicles, apocrine and eccrine glands, and smooth muscle cells. This article discusses epidermal or keratinocyte nevi, nevus sebaceous, nevus comedonicus, smooth muscle hamartomas, and inflammatory linear verrucous epidermal nevi. Syndromes associated with epidermal nevi are also reviewed., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Unlabelled: In addition to the well established epidermal nevus syndromes, such as Schimmelpenning, Becker, Proteus, CHILD, or nevus comedonicus syndromes, the group of less well defined phenotypes associated with epidermal nevi presently includes nevus trichilemmocysticus syndrome, didymosis aplasticosebacea, SCALP syndrome (sebaceous nevus, central nervous system malformations, aplasia cutis congenita, limbal dermoid,and pigmented nevus), Gobello syndrome, Bäfverstedt syndrome, NEVADA syndrome (nevus epidermicus verrucosus with angio-dysplasia and aneurysms), and CLOVE syndrome (congenital lipomatous overgrowth, vascular malformations, and epidermal nevus). These syndromes can thus far be regarded as being in limbo. Future clinical and molecular research may show which of these disorders can be added to the list of well defined nosologic entities., Learning Objectives: After completing this learning activity, participants should be able to understand why the list of distinct epidermal nevus syndromes can thus far not be regarded as being complete; why clinicians, when seeing a patient with an epidermal nevus syndrome that is difficult to categorize, should bear in mind the group of less well defined epidermal nevus syndromes; and how practical dermatologists can help recognize rare forms of epidermal nevus syndromes and categorize them appropriately., (Copyright (c) 2010 American Academy of Dermatology, Inc. Published by Mosby, Inc. All rights reserved.)

Recently, initial studies describing the use of multicolor fluorescence in situ hybridization (FISH) for classifying melanocytic skin lesions have been published demonstrating a high sensitivity and specificity in discriminating melanomas from nevi. However, the majority of these studies included neither histologically ambiguous lesions nor a clinical long-term follow up. This study was undertaken to validate a special multicolor FISH test in histologically ambiguous melanocytic skin lesions with known clinical long-term follow up. FISH was scored by three independent pathologists in a series of 22 melanocytic skin lesions, including 12 ambiguous cases using four probes targeting chromosome 6p25, centromere 6, 6q23, and 11q13. The FISH results were compared with array comparative genomic hybridization data and correlated to the clinical long-term follow up (mean: 65 months). Pair-wise comparison between the interpretations of the observers showed a moderate to substantial agreement (kappa 0.47-0.61). Comparing the FISH results with the clinical behavior reached an overall sensitivity of 60% and a specificity of 50% (chi(2)=0.25; P=0.61) for later development of metastases. Comparison of array comparative genomic hybridization data with FISH analyses did not yield significant results but array comparative genomic hybridization data demonstrated that melanocytic skin lesions with the development of metastases showed significantly more chromosomal aberrations (P<0.01) compared with melanocytic skin lesions without the development of metastases. The FISH technique with its present composition of locus-specific probes for RREB1/MYB and CCND1 did not achieve a clinically useful sensitivity and specificity. However, a reassessment of the probes and better standardization of the method may lead to a valuable diagnostic tool.

Different types of nevi do exist in relation to their epidemiology, morphology, evolution, and associated melanoma risk. The introduction of dermoscopy opened a new dimension of the morphologic universe of nevi and allowed clinicians to observe colors and structures within nevi that are otherwise not visible to the unaided eye. Because most of these colors and structures correspond to well-defined histopathologic correlates, dermoscopy enables clinicians to date to more precisely predict the histopathology diagnosis and thereby improve on their clinical diagnostic accuracy. Besides the diagnostic impact, the in vivo observation of thousands of nevi using dermoscopy and digital dermoscopic follow-up has opened new understanding about the evolution of nevi and factors influencing the nevus pattern. In consequence, a new nevus classification has been proposed, subdividing nevi into 7 categories, which are as follows: (1) globular/cobblestone nevi, (2) reticular nevi, (3) starburst nevi, (4) homogeneous blue nevi, (5) nevi on special body sites, (6) nevi with special features, and (7) and unclassifiable melanocytic proliferations. This article provides an overview on the morphologic classification of nevi and the factors influencing the nevus pattern.

Mucinous nevus is a rare entity with only 11 cases reported previously. It may be divided into two histopathologic types: connective tissue nevus of the proteoglycan (CTNP) and combined epidermal-CTNP. We describe a boy with asymptomatic grouped brown papuloplaques on the lower back since birth. A diagnosis of mucinous nevus of the combined epidermal-CTNP type was made after a biopsy. We vaporized two lesions with carbon dioxide laser, and the wounds healed satisfactorily. We present a literature review indicating a striking preponderance of male patients (M : F = 5 : 1) for mucinous nevus. In half of the cases, mucinous nevus did not appear until childhood, adolescence or early adulthood. The predominantly affected site was the trunk. Half of the cases can be assigned to the CTNP type and the other half to the combined epidermal-CTNP type. We propose that carbon dioxide laser vaporization may be a treatment option for mucinous nevus of the combined epidermal-CTNP type with multiple lesions but not for the CTNP type., ((c) 2009 John Wiley & Sons A/S.)

Today, the designation "capillary malformation" is widely used as a modern name for what was formerly called a nevus flammeus or port-wine stain. This new terminology, however, is inaccurate and ambiguous. There are at least nine different skin disorders fulfilling the criteria of a capillary malformation. Examples include nevus anemicus, cutis marmorata telangiectatica congenita, angiokeratoma circumscriptum, and several vascular lesions that, in the author's view, do not represent nevi, such as the nuchal or glabellar salmon patch and the cutaneous changes of Rendu-Osler disease. Hence, I propose that we should use "capillary malformation" as an umbrella term and not as a name for a specific cutaneous entity.

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.

Purpose: To estimate size overlap between large choroidal nevi and small choroidal melanomas by using plotted frequency distributions of tumor size., Methods: Frequency distributions of largest linear basal diameter (LBD) and thickness (TH) of choroidal nevi and melanomas were plotted from published data and cases in the senior author's practice. Relative frequencies of choroidal nevi and melanomas were estimated from published data. Relative frequency distributions of the tumors were plotted to illustrate the extent of overlap between them., Results: Comparison of plotted frequency distribution curves for thickness indicated that there were approximately 125 nevi for every melanoma in the TH range 1.5 to 2 mm, approximately 25 nevi for every melanoma in the TH range 2 to 2.5 mm, and approximately 5 nevi for every melanoma in the TH range 2.5 to 3 mm. Similarly, comparison of the plotted frequency distribution curves for LBD of these tumor types indicated that there were approximately 70 nevi for every choroidal melanoma in the LBD range 5 to 6 mm, approximately 10 nevi for every melanoma in the LBD range 6 to 7 mm, and approximately 3 nevi for every melanoma in the LBD range 7 to 8 mm., Conclusions: Because of the markedly greater cumulative lifetime incidence of choroidal nevi, the results of this analysis suggest considerable size overlap between larger nevi and smaller melanomas. Attempts to classify small melanocytic choroidal tumors clinically as benign nevi versus malignant melanomas on the basis of tumor size appear likely to result in multiple misclassifications.

Epidermal nevi (EN) represent a heterogeneous group of mosaic skin lesions frequently following the lines of Blaschko. They are divided into organoid and nonorganoid types. Herein we report on a 35-year-old man with a rather unusual type of EN. A linear lesion involving the trunk and following Blaschko's lines was conspicuously bordered by dark brown papules. The flat reddish central area histopathologically showed sebaceous hyperplasia consistent with nevus sebaceus, an organoid EN type, whereas the elevated margin showed features of a common nonorganoid keratinocytic EN. For this peculiar disorder, we propose the term 'nevus marginatus'. So far it is not clear whether nevus marginatus represents a distinct entity or merely an unusual clinical variant of nevus sebaceus.

The term "epidermal nevus syndrome" (ENS) has been used to describe the association of epidermal hamartomas and extra-cutaneous abnormalities. Epidermal nevi follow the lines of Blaschko. The majority of the extra-cutaneous manifestations involve the brain, eye, and skeletal systems. Several subsets with characteristic features have been delineated including the nevus sebaceous syndrome, Proteus syndrome, CHILD syndrome, Becker nevus syndrome, nevus comedonicus syndrome, and phakomatosis pigmentokeratotica. Epidermal nevi have been associated with benign and malignant neoplasms. Advances in molecular biology have revealed that the manifestations of ENS are due to genomic mosaicism. It is likely that the varied clinical manifestations of ENS are due in great part to the functional effects of specific genetic defects. Optimal management of the patient with ENS involves an interdisciplinary approach. Amelioration of the cutaneous features of ENS has been difficult but there have been advances, especially in the use of lasers.

The potential role of stem cells in neoplasia is a subject of recent interest. Three markers of melanocytic stem cells have been described recently. CD166 is expressed on the surface of mesenchymal stem cells and has been found on human melanoma cell lines. CD133 is expressed on the surface of dermal-derived stem cells that are capable of differentiating into neural cells. Nestin is an intermediate filament expressed in the cytoplasm of neuroepithelial stem cells. In this study, we evaluate the expression of these markers and possible differences among banal nevi, primary melanoma, and metastastic melanoma. Tissue microarrays containing normal tissue and 226 melanocytic lesions (71 banal nevi, 71 in situ and invasive melanomas, and 84 metastatic melanomas) were studied by immunohistochemistry using monoclonal antibodies CD166, CD133, and nestin. A significantly greater percentage of melanomas (combined primary and metastatic) contained cells that expressed CD166 (P=0.005), CD133 (P=0.003), and nestin (P=0.03) than banal nevi. Only nestin showed a statistical difference when comparing primary and metastatic melanoma (P=0.05). A stepwise increase in the proportion of lesions expressing all three markers was observed from banal nevi (2/19) to primary melanomas (8/17) to metastatic melanoma (19/28), P=0.0005. All cases of metastatic melanoma expressed at least one stem cell marker. The increased expression of CD166, CD133, and nestin in melanoma suggests that progression to malignant melanoma likely involves genetic pathways instrumental to stem cell biology and normal tissue development. Further studies and characterization of these pathways may also reveal new prognostic markers for a disease whose prognosis in advanced stages is dismal.

We observed 4 individuals with the acanthosis nigricans (AN) form of epidermal nevus, with clinical and histopathologic features of AN oriented along Blaschko's lines. Three patients had stable lesions since childhood. One patient concurrently had typical bilateral AN lesions. This condition may represent a mosaic form of AN.

Epidermal nevi are common congenital skin lesions with an incidence of 1 in 1,000 people; however, their genetic basis remains elusive. Germline mutations of the FGF receptor 3 (FGFR3) cause autosomal dominant skeletal disorders such as achondroplasia and thanatophoric dysplasia, which can be associated with acanthosis nigricans of the skin. Acanthosis nigricans and common epidermal nevi of the nonorganoid, nonepidermolytic type share some clinical and histological features. We used a SNaPshot multiplex assay to screen 39 epidermal nevi of this type of 33 patients for 11 activating FGFR3 point mutations. In addition, exon 19 of FGFR3 was directly sequenced. We identified activating FGFR3 mutations, almost exclusively at codon 248 (R248C), in 11 of 33 (33%) patients with nonorganoid, nonepidermolytic epidermal nevi. In 4 of these cases, samples from adjacent histologically normal skin could be analyzed, and FGFR3 mutations were found to be absent. Our results suggest that a large proportion of epidermal nevi are caused by a mosaicism of activating FGFR3 mutations in the human epidermis, secondary to a postzygotic mutation in early embryonic development. The R248C mutation appears to be a hot spot for FGFR3 mutations in epidermal nevi.

Background: It has been clinically observed that patients' "normal" moles resemble each other. Whether this concept is applicable to dermoscopic practice has not been sufficiently studied., Objective: To investigate whether physicians evaluating dermoscopic images would identify common dermoscopic profiles of nevi within individual patients., Methods: Images of 205 nevi belonging to 18 patients were evaluated by 2 dermatologists for dermoscopic global pattern, color, and specific structures. We defined dermoscopic patterns as dominant if seen in >or= 40% of the patient's nevi; a minor pattern was defined as 20 to 39%., Results: A dominant pattern was seen in 15 patients (83%). In 13 (72%) of the patients, >or= 80% of the nevi were classified into one, two, or three global patterns. The reticular global pattern was the most prevalent dominant pattern, seen in 9 patients (50%); the homogeneous pattern was the most prevalent minor pattern, seen in 16 patients (89%)., Conclusion: Individuals tend to have one to three predominant dermoscopic nevus global patterns.