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Pathologic discordance affecting patient management may approach 20% in melanocytic cases following specialist review. The diagnostic utility of PRAME has been highlighted in several studies but interpretative challenges exist including its use in severely dysplastic compound nevi showing progression to melanoma in situ, nevoid melanoma, and coexisting nevi with melanoma. We examine the PRAME status of a broad spectrum of melanocytic lesions including challenging, dysplastic nevi with severe atypia from a large Irish patient cohort. Retrospective review of the dermatopathology database was conducted to evaluate the PRAME staining characteristics of two hundred and twenty-one melanocytic lesions using a commercially available PRAME antibody (EPR20330). The proportion of nuclear labeling and intensity of staining was recorded. The sensitivity and specificity of PRAME for in situ and malignant melanocytic lesions was 77% and 100%, respectively. Virtually all of our melanoma in situ from high-cumulative sun damaged (CSD) skin (22/23) and all acral lentiginous melanoma (5/5) were PRAME positive while 80% (8/10) of our lentigo maligna melanoma showed diffuse expression. None of our benign subgroup showed diffuse immunoexpression (0/82), including thirty-seven moderate or severely dysplastic nevi. In all cases of melanoma in situ arising in association with a dysplastic compound nevus (0/10), no immunoexpression was observed in the nevic component while in five cases of melanoma in situ with coexistent, intradermal nevus immunostaining was confined to the in situ component. A total of 100% (2/2) of desmoplastic melanomas and 50% (4/8) of nodular melanomas were PRAME positive. PRAME is a sensitive and highly specific immunostain in the diagnosis of in situ and invasive melanoma and we emphasize its application in the evaluation of high CSD and acral melanoma subtypes as well as in challenging threshold cases.

PRAME (PReferentially expressed Antigen in MElanoma) is a cancer testis antigen that is frequently expressed in melanoma compared to benign melanocytic proliferations and nevi. However, the interpretation of the intensity and distribution of PRAME immunostaining is not standardized a lot, which makes interpretation difficult. PRAME-stained histological slides of superficial spreading melanomas (SSM) and dysplastic nevi (DN) were digitized and analyzed using the digital pathology and image platform QuPath. t-tests and ROC AUCs were performed with SPSS. A p-value of <0.05 was used for statistical significance, and a ROC AUC score of >0.8 was considered a good result. A cut-off score was defined in an evaluation cohort and subsequently analyzed in an independent validation cohort. In total, 81 PRAME-stained specimens were included. The evaluation cohort included 32 (50%) SSM and 32 (50%) DN, and the mean of PRAME-positive cells/mm2 for the entire lesion was 455.3 (SD 428.2) in SSM and 60.5 (SD 130.1; p < 0.001) in DN. The ROC AUC of PRAME-positive cells of the entire lesion was 0.866, and in the epidermis it was 0.901. The defined cut-off score to distinguish between DN and SSM was 97.67 cells/mm2. In the validation cohort, 16 out of 17 cases (94.1%) were correctly classified by the cut-off score. The computer-aided assessment of PRAME immunostaining is a useful tool in dermatopathology to distinguish between DN and SSM. Lesions with a moderate expression and indifferent morphologic features will remain a challenge for dermatopathologists.

On examination, the largest papule extended from the temporal region of the scalp to the chin. Extensive evaluation did not reveal any associated systemic involvement. The surgical team completely excised the lesions in multiple operations over time and under aseptic conditions. Histopathological evaluation of the lesion showed histopathological features of nevus sebaceous (figure 2).

The clinical significance of nevus-associated melanoma compared with de novo melanomas remains controversial. It has been suggested that nevus-associated melanomas have a higher Breslow thickness and therefore worse prognosis. Over a 10-year period, this study evaluated the incidence of nevus-associated melanoma and its prognostic significance related to clinicopathologic features. Cross-sectional study from 1995 through 2004 in a dermatopathology referral center. With available data, we evaluated sex, primary location, histologic subtype, Breslow thickness, Clark level, presence of ulceration, associated lesion, and histologic subtype of the associated lesion. Of 135,653 pathologic records from skin biopsy specimens over a 10-year period, 1,190 melanoma records were selected. Nevus-associated melanomas corresponded to 390 (32.8%) melanomas, with thin melanomas having a nevus 1.52 times the association observed with thick melanomas (>1.01 mm; 95% confidence interval, 1.16-1.99; P < .001). Superficial spreading melanoma was the most frequent, while no lentigo maligna melanoma was associated with nevi. The median Breslow thickness of nevus-associated melanomas was lower than that of de novo melanomas. Nevus-associated melanomas, which represent one-third of the melanomas in southeast Brazil, are associated with intermittent sun exposure, superficial spreading melanomas, and lower Breslow thickness. This is one of the largest series describing nevus-associated melanomas in Latin America.

Introduction The MOLES score has been validated to clinically differentiate choroidal naevi from melanomas by ocular oncologists and community optometrists. However, its utility in a virtual choroidal naevi clinic at a tertiary eye hospital without specialist ocular oncology services has not yet been evaluated. Methods A retrospective case review of 385 choroidal lesions in the virtual choroidal naevus clinic at Bristol Eye Hospital during January–March 2020 and April–August 2021 was performed. Choroidal lesions were assessed using the TFSOM-UHHD risk factor index and MOLES score, respectively. For both study periods, clinical outcome and adherence data were analysed. Results Choroidal lesions scored higher with the TFSOM-UHHD index (median 2) compared to the MOLES score (median 0; p  < 0.001). Median required follow-up duration was 2 years for lesions assessed with the TFSOM-UHHD index, and 0 years for those graded with the MOLES score. Overall, 215 patients were appropriately discharged to community optometrists based on their MOLES score. Imaging requirements for the TFSOM-UHHD index and MOLES score protocols were met in 69.1% and 94.8% of cases, respectively. Conclusion The MOLES score was easily implemented in a virtual choroidal naevus clinic, with good adherence. It increased clinic capacity by facilitating appropriate discharges of low-risk naevi to community monitoring, allowing finite and specialist hospital-based services to monitor higher-risk naevi more closely.

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.

An artificial intelligence trained to classify images of skin lesions as benign lesions or malignant skin cancers achieves the accuracy of board-certified dermatologists. Neural network identifies skin cancers Andre Esteva et al . used 129,450 clinical images of skin disease to train a deep convolutional neural network to classify skin lesions. The result is an algorithm that can classify lesions from photographic images similar to those taken with a mobile phone. The accuracy of the system in detecting malignant melanomas and carcinomas matched that of trained dermatologists. The authors suggest that the technique could be used outside the clinic as a visual screen for cancer. Skin cancer, the most common human malignancy 1 , 2 , 3 , 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) 4 , 5 show potential for general and highly variable tasks across many fine-grained object categories 6 , 7 , 8 , 9 , 10 , 11 . 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 12 —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.

Conjunctival nevi (CN) are common benign ocular tumors. Given their low risk of malignancy, surgical removal of nevi is primarily requested by patients. Herein, we introduce Atmospheric Low-temperature Plasma (ALTP) as a novel noninvasive method for the removal of CN. A clinical case series was conducted from 2020 to 2021 at the Vision Health Ophthalmic Center in Tehran, Iran. CN in one of the eyes of seven patients was treated. In all patients, the benignity of CN was confirmed by ocular oncologists. The white handpiece of the Plexr device, which generates plasma with the lowest output, was used to apply plasma spots on the nevi. In addition, the Ocular Surface Disease Index (OSDI) questionnaire was completed by all patients before and six months after the treatment. In all patients, the nevi outside the limbus area completely disappeared. The mean size of pre- and post-operative nevi was 3.89×11.7 and 0.54×1.69 mm, respectively. Results of the OSDI questionnaire showed significantly lower scores after the surgery in all patients. The ALTP method is a simple, cost-effective, and office-based surgery to remove CN safely and effectively.

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.

A group of melanocytic benign nevi are prone to be misdiagnosed as nodular or superficial spreading melanoma. This review illustrates the most frequent forms of these nevi in direct comparison with their malignant morphologic counterparts. The nevi are: hyper-cellular form of common nevus to be distinguished from nevoid melanoma, Spitz nevus (vs spitzoid melanoma), Reed nevus (vs melanoma with features of Reed nevus), cellular atypical blue nevus (vs melanoma on blue nevus), acral nevus (vs acral melanoma), Clark dysplastic nevus (vs superficial spreading melanoma), desmoplastic nevi (vs desmoplastic melanoma), benign proliferative nodules in congenital nevi (vs melanoma on congenital nevi), epithelioid blue nevus (vs animal type melanoma) and regressed nevus (vs regressed melanoma). For each single 'pair' of morphological look-alikes, a specific set of morphological, immunohistochemical and genetic criteria is provided.