Melanocytic Nevi Research Articles

Exploring the Potential of DALL-E 2 in Pediatric Dermatology: A Critical Analysis.

Artificial intelligence (AI) is becoming increasingly explored for its potential applications in dermatology. Among various AI models, DALL-E 2 (San Francisco, CA: OpenAI), which generates de novoimages from textual inputs, has garnered significant attention for its remarkable photorealism. In our study, we aimed to analyze the performance of DALL-E 2 in the context of dermatology. The following 12 pediatric dermatological conditions common to ages <15 years were selected as tests: acne, atopic dermatitis, contact dermatitis, vitiligo, congenital melanocytic nevus, warts, molluscum contagiosum, seborrheic dermatitis, alopecia areata, infantile hemangioma, impetigo, and dermatophytosis, specifically tinea corporis. Representative morphological descriptions of each diagnosis, along with their corresponding names, were inputted into DALL-E 2 as textual prompts and subsequently compared. The accuracy of the generated images and their alignment with the intended descriptions were assessed. Among the total of 24 images reported, 18 were photorealisticand six were cartoons. More cartoons were generated when providing the model with morphological descriptions as textual inputs compared to when diagnoses were inputted. While not entirely accurate, acne stood out as the only diagnosis that was the most consistent and closest to the actual diagnosis. Both images of acne portrayed erythematous papules scattered across the face. However, DALL-E 2 resulted in poor performance for the remaining eleven diagnoses. They did not accurately represent the intended diagnoses nor align with their counterpart image. Moreover, most of the generated images featured lighter skin tones. In assessing DALL-E 2's applications in dermatology, our study highlights the need for more domain-specific and demographically inclusive training data in its algorithms to improve performance.

Innovative Approaches in the Surgical Management of Giant Facial Melanocytic Nevus: A Focus on Tissue Expanders

Innovative Approaches in the Surgical Management of Giant Facial Melanocytic Nevus: A Focus on Tissue Expanders

Skin Lesion Prediction and Classification Using Innovative Modified Long Short-Term Memory-Based Hybrid Optimization Algorithm

Identification of pigmented skin lesions is necessary for the detection of severe diseases associated with the skin organ, notably malignancy. Accurate skin cancer diagnosis can be improved with the use of image detection approaches and computer classification skills. Therefore, this research work plans to perform skin lesion prediction and classification using a novel deep learning methodology. Initially, the data related to the skin lesion are gathered from the ISIC dataset. After collecting the images, the pre-processing is performed using hair removal and filtering hair removed images via median filtering. These pre-processed images undergo segmentation process accomplished using the U-Net method. Next, the features are extracted from these segmented images with the help of color features, and texture features by GLCM and RGB histogram features. These extracted features undergo the prediction phase that is accomplished using the MLSTM model, in which the parameter optimization is done by the nature inspired novel hybrid metaheuristic algorithm referred as SC-STBO algorithm with the consideration of accuracy maximization and RMSE minimization as the major fitness for the objective function. If the predicted output is returned as the presence of skin lesion, the same novel MLSTM model classifies the final skin lesion output into seven types, such as Vascular Lesions, Melanocytic Nevi, Melanoma, Dermatofibroma, Benign Keratosis-like Lesions, BCC, and Actinic Keratoses, respectively. Seven groups of skin diseases can be identified early thanks to the suggested effort, which can then be tested and properly handled by medical professionals. With an accuracy of 0.9931, the recommended methodology clearly outperforms traditional techniques. Similarly, the suggested methodology clearly beats the conventional methods, with a recall of 0.9825.

Machine Learning Approaches for Skin Neoplasm Diagnosis.

Approaches for skin neoplasm diagnosis include physical exam, skin biopsy, lab tests of biopsy samples, and image analyses. These approaches often involve error-prone and time-consuming processes. Recent studies show that machine learning shows promise in effectively classifying skin images into different categories such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of computer-aided diagnosis (CADx) systems to diagnose skin diseases. In the proposed CADx system, generative adversarial network (GAN) discriminator is used to identify (and remove) fake images. Exploratory data analysis (EDA) is applied to normalize the original data set for preventing model overfitting. Synthetic minority oversampling technique (SMOTE) is employed to rectify class imbalances in the original data set. To accurately classify skin images, the following machine learning models are utilized: linear discriminant analysis (LDA), support vector machine (SVM), convolutional neural network (CNN), and an ensemble CNN-SVM. Experimental results using the HAM10000 data set demonstrate the ability of the machine learning models to improve CADx performance in treating skin neoplasm. Initially, the LDA, SVM, CNN, and ensemble CNN-SVM show 49%, 72%, 77%, and 79% accuracy, respectively. After applying GAN (discriminator) and SMOTE, the LDA, SVM, CNN, and ensemble CNN-SVM show 76%, 83%, 87%, and 94% accuracy, respectively. We plan to explore other machine learning models and data sets in our next endeavor.

Risk factors for the development of congenital giant nevi in children

BACKGROUND: Congenital giant melanocytic nevi (CMN) occur approximately in 1 of 250,000–500,000 newborns. According to foreign literature, the risk of malignancy of a melanocytic nevus varies widely within 5%–42%. AIM: This study aimed to identify possible risk factors for CMN in children and determine the most common location and actual list of examination. MATERIALS AND METHODS: The study enrolled 104 mother–child pairs, where the children had CMN. The control group included 60 mother–child pairs, in which the children had no CMN. RESULTS: CMN were located on the head in 42.4% of cases, which was the most frequent localization. The most frequent localizations also included the trunk and simultaneous location of nevi on several body segments. Abnormalities in thyroid hormone levels were noted in 12.5% of the mothers. The frequency of large nevi in the grandparents of children with CMN (13.5%) was significantly higher than that in their parents (mother, 1.9%; father, 2.9%). In addition, 19.2% of the children were examined by an oncologist or are on a dispensary register with an oncologist. Moreover, 4.8% of the patients were observed by a neurologist. Magnetic resonance imaging was performed once in 19.2% of the children, who underwent genetic testing. No foci of melaniform cell accumulation in the nervous tissue were detected in any examined child. CONCLUSIONS: The most common location of CMN is the head and torso – areas of risk of damage to the central nervous system by melanoform cells. The survey results of parents of the main group identified the following as risk factors for CMN development in children: a history of miscarriage or frozen pregnancy, thyroid hormone abnormalities, CMN in grandparents, ARVI during pregnancy, particularly in the first trimester, and visiting a solarium and using long-lasting gel polishes during pregnancy.

Deep Learning for Skin Cancer Classification: A Comparative Study of CNN and Vgg16 on HAM10000 Dataset

Skin cancer is one of the most dangerous types of cancer among the cancers. The early detection of skin cancer helps resolve it. Hence, it is necessary to diagnose the disease as early as possible. This paper presents Convolutional Neural Networks and the Vgg16 algorithm to recognize skin cancer types. The HAM10000 dataset, which comprises seven distinct forms of skin cancer, melanocytic nevi (nv), Melanoma (mel), basal cell carcinoma (bcc), actinic keratoses (akiec), vascular lesions (vasc), and dermatofibroma (df). This system aims to improve classification accuracy; the methodology necessitates extensive dataset preparation, including scaling, normalization, and augmentation. The Vgg16 algorithm, when combined with the CNN architecture, offers a robust basis for the classification of skin cancer. Comprehensive details on regularization techniques, optimization strategies, and training parameters are included to ensure openness and reproducibility. The system's performance is evaluated using accuracy, precision, recall, and F1-score for every type of skin cancer. This paper highlights the usefulness of the proposed method in skin cancer diagnosis and looks at challenges, constraints, and prospects for further research. New methods for identifying skin cancer are being developed with the help of this research, which can improve patient outcomes and clinical decision-making.

A rare case of giant congenital melanocytic nevus associated with ipsilateral developmental dysplasia of the hip

A rare case of giant congenital melanocytic nevus associated with ipsilateral developmental dysplasia of the hip

Peripunctal melanocytic nevus involving the lacrimal canaliculus: A case report

A 56-year-old woman complained of a mass in the right eye’s lower eyelid. Slit-lamp examination revealed a dark mass with pigment in the inner canthus of the right lower eyelid involving the lacrimal punctum. The mass was removed and confirmed to be peripunctal melanocytic nevus. We have presented the first case of peripunctal melanocytic nevus involving the lacrimal canaliculus with histologic evidence. No recurrence occurred.

Dermatopathological Analysis of Common Skin Lesions Encountered in Cadavers

This study served as a purpose for medical students to gain experience in both dermatology and pathology, which is a common barrier that prevents first and second year medical students from refining skills that are not later taught until residency. This prompted a study to develop gross differential diagnostic skills and how to analyze histopathology slides to diagnose common skin lesions to refine skills in both clinical and histology presentation. A cadaveric case series was designed to examine multiple shave biopsies on all abnormal skin lesions observed from nine cadavers used for the first year medical students gross anatomy lab during the year 2022-2023. Biopsies were stained using hematoxylin and eosin. Histopathology slides, though initially viewed by medical students, were confirmed by a pathologist at a later time. 25 samples were taken from the 9 cadavers. The most commonly encountered lesion was macular seborrheic keratosis, with nine of the 25 (32%). Seven of the sample lesions (26.9%) were melanocytic nevus. Two sample lesions (8.0%) from the same cadaver were a superficial cutaneous cyst and one sample lesion (3.8%) from a separate cadaver indicated verruca vulgaris. Common dermatological lesions were identified among the nine cadavers used for analysis. This provided opportunities to develop and refine skills in dermatopathology. A further increase in sample size is needed to gain exposure to a larger variety of lesions and to identify common dermatological lesions grossly based on differing race, age, and gender.

Two-Stage Approach to Managing a Giant Congenital Melanocytic Nevus of the Scalp in an Infant

Congenital melanocytic nevus (CMN) is a benign skin condition that affects the epidermis and dermis. Large to giant CMNs are associated with a higher risk of malignancy over a lifetime, underscoring the importance of assessing and monitoring their potential for malignant transformation. We present a case of a large to giant CMN on an infant’s scalp, emphasizing its potential risk for malignancy. The infant underwent a successful two-stage surgical procedure, resulting in excellent aesthetic outcomes.

Line‐field confocal optical coherence tomography and optical coherence tomography for distinguishing basal cell carcinoma from dermal nevus: A case report

AbstractBasal cell carcinoma (BCC) is the most frequent skin cancer with a rising incidence. It can imitate benign lesions for example, dermal nevus clinically and dermoscopically. Both entities show specific morphologic features in line‐field confocal optical coherence tomography (LC‐OCT) and optical coherence tomography (OCT). The aim was to differentiate both lesions via noninvasive imaging to prevent unnecessary surgery. A 63‐year‐old woman presented with a light brownish nodule on the left cheek and a brownish nodule on the right nasal slope/cheek, which were both evident since a few months. The clinical and dermoscopic appearance of both lesions was very similar including the differential diagnosis of a dermal nevus or BCC. Dynamic OCT of the lesion on the left cheek showed multiple hyporeflective nodules in the dermis with hypervascularization. Using LC‐OCT several hyporeflective round to ovoid nodules with hyporeflective clefting embedded in a hyperreflective surrounding connective tissue could be observed. These specific morphological criteria called millefeuille pattern led to the diagnosis of a nodular BCC. The lesion of the right cheek presented with a thickened epidermis and elongated rete ridges in dynamic OCT. Upon LC‐OCT examination a so‐called large wave like pattern presenting as undulating hyporeflective and hyperreflective lobular structures could be identified. Therefore, the diagnosis of a dermal nevus was made. Both lesions were identified correctly as confirmed histopathologically after shave excision. Since BCC can imitate benign lesions for example, dermal nevi, the use of OCT and LC‐OCT can facilitate the identification of BCC and its imitators. Noninvasive imaging can help to avoid unnecessary biopsy especially in areas where the skin is vulnerable to punch biopsy.

Skin Lesions Identification and Analysis with Deep Learning Model Using Transfer Learning

Sunlight has beneficial as well as harmful rays. Environmental pollution occurs as a result of the depletion of the ozone layer caused by the damage caused by humans to the environment. As a result of these pollutants, skin diseases can be seen in areas exposed to direct sunlight, such as the head and neck. Early detection of actinic keratosis (akiec), basal cell carcinoma (bcc), bening keratosis (bkl), dermafibroma (df), melanoma (mel), melanocytic nevi (nv), and vascular (vasc) skin cancer types, which is one of the most common skin diseases, is important for medical intervention. Otherwise, severe spread, called metastasis, may occur as a result of aggressive growths. For the stated reasons, a deep learning model based on transfer learning, which can classify skin cancer types, has been proposed to assist the medical personnel who serve in this field. With this proposed model, the aim is to classify at high accuracy rates without any pre-processing. As a result of the experimental studies carried out as a result of the stated goals, an accuracy rate of 99,51% was achieved with the proposed model.

Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation.

Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

Co-occurrence of posterior chest wall pilonidal sinus with melanocytic nevus: a challenging presentation: a case report

IntroductionTo date, only a limited number of case reports have documented the co-occurrence of PNS and melanocytic nevus in the medical literature. This study aims to report an exceptionally rare case of posterior chest wall PNS in conjunction with a melanocytic nevus.Case presentationA 46-year-old female presented with a long-standing black lesion on her left upper posterior chest wall, that had become painful in the two months prior to presentation. There was a painful, dark blue, non-erythematous, and non-tender nodule on the left upper posterior chest wall. Based on the patient’s desire for cosmetic purposes, the lesion was excised totally with primary closure under local anaesthesia. Histopathological examination revealed intradermal melanocytic nevus with inflamed pilonidal sinus.DiscussionThe rarity of posterior chest wall PNS associated with nevi poses unique diagnostic and therapeutic challenges for clinicians. The distinct anatomical location, different from the conventional region, and the rare association between the two conditions may delay accurate diagnosis and result in mismanagement or inappropriate interventions.ConclusionThe posterior chest wall PNS is another type of atypical PNS that is extremely rare. The association between PNS and blue nevus is a fascinating medical finding that deserves further investigation.

PRAME expression in genital melanocytic lesions – Potential diagnostic pitfall of intermediate expression in atypical genital nevi

IntroductionThe Preferentially Expressed Antigen in Melanoma (PRAME) immunostain has seen significant diagnostic use in confirming malignancy for melanocytic lesions. However, the expression of PRAME in genital melanocytic lesions have not been reported. In this study, PRAME staining was performed on a cohort of genital melanocytic lesions, aiming to investigate the diagnostic role of PRAME in genital melanocytic lesions and its expression in atypical genital nevi. MethodologyA cohort including genital invasive melanoma, melanoma-in-situ, atypical genital nevus (AGN), compound nevus, intradermal nevus, blue nevus, lentigo and melanosis was retrieved with histology reviewed and PRAME immunostaining performed. ResultsA total of 66 cases were reviewed. The average proportion expression of PRAME were 56.75 % and 57.43 % for invasive melanoma and melanoma-in-situ, with average H-scores of 153.5/300 and 163.14/300 respectively, which were greater than AGN (3.25 %, 7.75/300, p<0.001), compound/intradermal nevi, lentigo/melanosis, and background junctional melanocytes (<1 %, <1/300, p<0.001). The different cutoffs of PRAME expression, the sensitivity and specificity were 65.22 % and 100 % (>100/300); 69.57 % and 95.83 % (>10/300); and 82.61 % and 93.75 % (≥1/300) respectively. Low level PRAME expression was seen in half of the cases of AGN (n=2/4, 50 %), and at low cutoffs (>10/300 and ≥1/300) unable to differentiate invasive melanoma from AGN (p>0.05). ConclusionsFor genital melanocytic lesions, PRAME immunostain shows high specificity at strong and diffuse staining. AGN not uncommonly display low level expression. Focal and/or weak PRAME expression should not be considered as an absolute indication of malignancy, and comprehensive histological assessment remains the key to accurate diagnosis of melanocytic lesions.

Encorafenib and cetuximab versus irinotecan/cetuximab or FOLFIRI/cetuximab in Chinese patients with BRAF V600E mutant metastatic colorectal cancer: The NAUTICAL CRC study.

LBA3559 Background: BRAF V600E mutations are present in 8-12% worldwide of patients with metastatic colorectal cancer (mCRC) and linked with a poor prognosis. Encorafenib + cetuximab (E+C) was approved by the FDA and EMA for patients with BRAF V600E-mutant mCRC who received prior systemic therapy, based on data from the BEACON CRC study. Methods: This Phase II, multicenter, randomized, open-label, 2-arm study evaluated E+C vs irinotecan + cetuximab or FOLFIRI + cetuximab (control arm) in Chinese patients with BRAF V600E mutant mCRC whose disease progressed after 1 or 2 prior treatment lines in the metastatic setting. A safety lead-in initially conducted in 10 patients reported no DLTs. Eligible patients had mCRC and a BRAF V600E mutation in tumor tissue determined by a local assay before screening and centrally confirmed. Patients were randomly assigned to the doublet arm (E+C) or the control arm in a 2:1 ratio, respectively. Randomization was stratified by baseline ECOG performance status (0 vs 1) and prior use of irinotecan (yes vs no). The primary objective of the randomized phase was to compare the efficacy of E+C vs the control arm, as measured by PFS (assessed by blinded independent central review). Secondary objectives included PFS assessed by the investigator, ORR, DOR, DCR, TTR, OS, QoL, and safety and tolerability of E+C. Results of the randomized phase of the study are reported. Results: At data cut-off (19 Dec 2023), 65 patients were enrolled in the E+C arm and 32 in the control arm. Median patient age was 56 years, the primary cancer site was the left colon in 56.7% of patients, 48.5% had metastases to ≥3 organs, 56.7% had liver metastases, 77.3% received one prior metastatic treatment, and 22.7% received 2 prior metastatic treatments. Results are shown below for E+C vs the control arm, respectively. Median PFS assessed by BICR was 4.2 mo vs 2.5 mo (HR 0.37; 95% CI: 0.20, 0.68; P=0.0004). Median OS was 11.6 mo vs 8.2 mo (HR for death 0.55; 95% CI: 0.31, 0.99). Confirmed ORR was 24.6% (95% CI: 14.8, 36.9) vs 6.3% (95% CI: 0.8, 20.8). Treatment emergent adverse events (TEAEs) of grade 3 or higher occurred in 47.7% vs 51.9% of patients. Treatment-related grade 3 or higher occurred in 24.6% and 44.4% of patients. The most frequent TEAEs were anemia (30.8% vs 37%), vomiting (26.2% vs 33.3%), rash (24.6% vs 29.6%), weight loss (23.1% vs 18.5%), hypoalbuminemia (21.5% vs 22.2%), and melanocytic naevus (21.5% vs 0%). Three patient deaths were reported during treatment: unknown cause (n=1) and pneumonia (n=1) in the E+C arm and septic shock (n=1) in the control arm. Conclusions: Treatment with a combination of encorafenib and cetuximab is effective and well tolerated in Chinese patients with BRAF V600E mutant mCRC, resulting in a significantly longer PFS than standard therapies. These results are consistent with those previously reported in the BEACON study. Clinical trial information: NCT05004350 .

JAAD Game Changers: “Dermoscopic changes in melanocytic nevi in patients receiving immunosuppressive and biologic treatments: Results of a prospective case-control study”

JAAD Game Changers: “Dermoscopic changes in melanocytic nevi in patients receiving immunosuppressive and biologic treatments: Results of a prospective case-control study”

The W-Plasty Serial Excision Method for Treating Medium Congenital Melanocytic Nevi: A Retrospective Analytical Study.

Serial excision remains the most commonly used surgical procedure for treating congenital melanocytic nevus (CMN). It is critical to remove as much of the lesion as possible with each procedure to reduce the number of procedures and to shorten the treatment duration. To investigate the clinical efficacy of W-plasty serial excision for the repair of postoperative CMN defects. A retrospective analysis of patients with medium CMN was conducted from April 2018 to March 2022. Treatment options were divided into elliptical serial excision (10 cases) and W-plasty serial excision (10 cases). Follow-up occurred over 6 months. The number of elliptical excision procedures was 2 to 4 (mean 2.9). The scar-to-lesion length ratio was 1.5 to 2.0 (mean 1.7). The mean Vancouver Scar Scale (VSS) score was 5.40 ± 0.42. The number of W-plasty excision procedures was 2 to 3 (mean 2.2). The scar-to-lesion length ratio was 1.2 to 1.5 (mean 1.4). The mean VSS score was 2.70 ± 0.26. W-plasty excision was superior to elliptical excision regarding the number of procedures and the effect on postoperative scars. W-plasty serial excision can be considered a suitable option for the excision of medium CMN, leading to excellent results.

Evaluation of tubulin β-3 and 5 hydroxy-methyl cytosine as diagnostic and prognostic markers in malignant melanoma

Tubulin β-3 staining pattern and staining intensity of 5-hydroxymethyl cytosine (5-hmC) are potential diagnostic and prognostic markers in melanocytic lesions that need further evaluation. Melanocytic nevi and primary cutaneous melanomas were immunohistochemically stained for tubulin-β-3 and 5-hmC. Immunoreactivity and staining patterns were correlated with Breslow-thickness, clinical and pathological characteristics, and progression-free survival. Melanocytes showed positive tubulin β-3 staining. However, in most nevi, tubulin β-3 staining appeared as a gradient with intense cytoplasmic staining in cells of the superficial part of the lesion that faded to weak staining in the deep dermal part, while no gradient was found in deep penetrating nevi and melanomas. In 53 % of the melanomas, areas with loss of tubulin β-3 staining were found. 5-hmC staining intensity was significantly higher in melanocytic nevi compared to melanomas. Breslow thickness in combination with low 5-hmC score and loss of tubulin-β-3 staining was predictive for poor prognosis. As single markers, tubulin-β-3 and 5-hmC can be useful to distinguish between melanocytic nevi and melanoma, but staining variability limits the use of 5-hmC. In melanomas measuring >1.5 mm, combination of low 5-hmC score and loss of tubulin-β-3 staining may have prognostic value.

An invitro nevus explant model for studying the effects of ultraviolet radiation.

Ultraviolet radiation (UVR) has been recognized as a potential trigger for the transformation of benign melanocytic nevi into melanoma. However, the mechanisms governing the formation and progression of melanocytic nevi remain poorly understood. This lack of understanding is partly due to the difficulty in isolating and culturing nevus tissues invitro, resulting in a dearth of robust exvivo models for nevi. Therefore, the establishment of a reliable melanocytic nevus model is imperative. Such a model is essential for elucidating nevus pathogenesis and facilitating the development of effective therapeutic interventions. Therefore, we have sought to establish an exvivo nevus explant model to study UVR stimulation. And the structural integrity and tissue activity of the exvivo nevi explant model was evaluated. We then observed melanogenesis and proliferation activity of the explants after UVR stimulation. There was less blister formation after Day 3 in nevi explants under our modified medium conditions. The nevi explant was able to maintain almost the same morphological structure and tissue activity as invivo tissue within 24 h. Following UVR stimulation, we observed increased melanogenesis and proliferation activity in nevi explants. Nevi explants could serve as an exvivo model for UVR-induced nevi stimulation research.