Mohs micrographic surgery challenges and new technologies to optimize care of cutaneous malignancies of the ear.

Cutaneous malignancies affecting the ear, exacerbated by extensive ultraviolet (UV) exposure, pose intricate challenges owing to the organ's complex anatomy. This article investigates how the anatomy contributes to late-stage diagnoses and ensuing complexities in surgical interventions. Mohs Micrographic Surgery (MMS), acknowledged as the gold standard for treating most cutaneous malignancies of the ear, ensures superior margin control and cure rates. However, the ear's intricacy necessitates careful consideration of tissue availability and aesthetic outcomes. The manuscript explores new technologies like Reflectance Confocal Microscopy (RCM), Optical Coherence Tomography (OCT), High-Frequency, High-Resolution Ultrasound (HFHRUS), and Raman spectroscopy (RS). These technologies hold the promise of enhancing diagnostic accuracy and providing real-time visualization of excised tissue, thereby improving tumor margin assessments. Dermoscopy continues to be a valuable non-invasive tool for identifying malignant lesions. Staining methods in Mohs surgery are discussed, emphasizing hematoxylin and eosin (H&E) as the gold standard for evaluating tumor margins. Toluidine blue is explored for potential applications in assessing basal cell carcinomas (BCC), and immunohistochemical staining is considered for detecting proteins associated with specific malignancies. As MMS and imaging technologies advance, a thorough evaluation of their practicality, cost-effectiveness, and benefits becomes essential for enhancing surgical outcomes and patient care. The potential synergy of artificial intelligence with these innovations holds promise in revolutionizing tumor detection and improving the efficacy of cutaneous malignancy treatments.

[Skin reactions related to molluscum contagiosum infection]. / Reacciones cutáneas relacionadas a la infección por molusco contagioso.

Molluscum contagiosum (MC) is a common viral infection in children, immunocompromised, and sexually active adults. Its usual clinical presentation is 2-5 mm, whitish or skin-colored papules, with a shiny surface and central umbilication, generally clustered and randomly distributed over the skin surface. Dermoscopy reveals yellowish-white polylobulated structures with peripheral telangiectasia. Diagnosis is usually clinical supported by dermoscopy. However, in some cases, inflammatory manifestations can be associated with this infection and can mimic other dermatological conditions, making the diagnosis difficult and leading to unnecessary treatments. The objective of this article is to describe the main skin reactions associated with MC infection in order to provide a diagnostic and initial management tool for clinicians dealing with these conditions. Reported manifestations include the BOTE sign, perilesional eczema, Gianotti-Crosti syndrome-like reaction, ID reaction, erythema annulare centrifugum, erythema multiforme, folliculitis, white halo, and atypical manifestations (giant, disseminated, necrotic, polypoidal, and nodular lesions, pseudocysts, abscesses). In pediatric patients with the clinical manifestations described above, infection by molluscum contagiosum pox virus should be considered among the differential diagnoses, and referral to a dermatologist should be made in selected cases.

Dermatophytosis in cats: A comprehensive study on diagnostic methods and their accuracy.

Background: Dermatophytosis is a contagious fungal infection that affects mainly cats. It poses significant challenges in veterinary medicine due to its zoonotic potential and impact on animal and public health. Rapid and reliable diagnosis is crucial for preventing the spread of the disease, guiding treatment decisions, and monitoring disease control efforts. Although there are several studies on diagnostic methods in feline dermatophytosis, the comparison between them from the same sample lacks data. The absence of a universally accepted gold standard diagnostic method highlights the need for a multifaceted approach to diagnosing feline dermatophytosis. Aim: This study aims to assess the accuracy and efficacy of different diagnostic techniques comprehensively. Methods: For this, 48 samples of cats were analyzed by dermoscopy, direct hair examination, fungal culture using various media (Mycosel, Sabouraud, and Dermatophyte Test Medium), and polymerase chain reaction (PCR). Results: Direct examination and dermoscopy yielded unsatisfactory results. Mycosel and Sabouraud were suboptimal. DTM demonstrated superior selectivity, making it the most reliable among traditional methods. PCR was the top performer, exhibiting singular sensitivity, specificity, and accuracy. Conclusion: The study suggests that PCR may be the preferred choice for diagnosing feline dermatophytosis in clinical practice, especially when rapid and accurate results are essential.

Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.

BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermoscopy images for model training. This study aims to compare the classification performance for melanoma of three types of CNN models: those trained on clinical images, dermoscopy images, and a combination of paired clinical and dermoscopy images from the same lesion. MATERIALS AND METHODS: We divided 914 image pairs into training, validation, and test sets. Models were built using pre-trained Inception-ResNetV2 convolutional layers for feature extraction, followed by binary classification. Training comprised 20 models per CNN type using sets of random hyperparameters. Best models were chosen based on validation AUC-ROC. RESULTS: Significant AUC-ROC differences were found between clinical versus dermoscopy models (0.661 vs. 0.869, p < 0.001) and clinical versus clinical + dermoscopy models (0.661 vs. 0.822, p = 0.001). Significant sensitivity differences were found between clinical and dermoscopy models (0.513 vs. 0.799, p = 0.01), dermoscopy versus clinical + dermoscopy models (0.799 vs. 1.000, p = 0.02), and clinical versus clinical + dermoscopy models (0.513 vs. 1.000, p < 0.001). Significant specificity differences were found between dermoscopy versus clinical + dermoscopy models (0.800 vs. 0.288, p < 0.001) and clinical versus clinical + dermoscopy models (0.650 vs. 0.288, p < 0.001). CONCLUSION: CNN models trained on dermoscopy images outperformed those relying solely on clinical images under our study conditions. The potential advantages of incorporating paired clinical and dermoscopy images for CNN-based melanoma classification appear less clear based on our findings.

A novel approach for melanoma detection utilizing GAN synthesis and vision transformer.

BACKGROUND AND OBJECTIVE: Melanoma, a malignant form of skin cancer, is a critical health concern worldwide. Early and accurate detection plays a pivotal role in improving patient's conditions. Current diagnosis of skin cancer largely relies on visual inspections such as dermoscopy examinations, clinical screening and histopathological examinations. However, these approaches are characterized by low efficiency, high costs, and a lack of guaranteed accuracy. Consequently, deep learning based techniques have emerged in the field of melanoma detection, successfully aiding in improving the accuracy of diagnosis. However, the high similarity between benign and malignant melanomas, combined with the class imbalance issue in skin lesion datasets, present a significant challenge in further improving the diagnosis accuracy. We propose a two-stage framework for melanoma detection to address these issues. METHODS: In the first stage, we use Style Generative Adversarial Networks with Adaptive discriminator augmentation synthesis to generate realistic and diverse melanoma images, which are then combined with the original dataset to create an augmented dataset. In the second stage, we utilize a vision Transformer of BatchFormer to extract features and detect melanoma or non-melanoma skin lesions on the augmented dataset obtained in the previous step, specifically, we employed a dual-branch training strategy in this process. RESULTS: Our experimental results on the ISIC2020 dataset demonstrate the effectiveness of the proposed approach, showing a significant improvement in melanoma detection. The method achieved an accuracy of 98.43%, an AUC value of 98.63%, and an F1 value of 99.01%, surpassing some existing methods. CONCLUSION: The method is feasible, efficient, and achieves early melanoma screening. It significantly enhances detection accuracy and can assist physicians in diagnosis to a great extent.

Multi-scale feature fusion and class weight loss for skin lesion classification.

Skin cancer is one of the common types of cancer. It spreads quickly and is not easy to detect in the early stages, posing a major threat to human health. In recent years, deep learning methods have attracted widespread attention for skin cancer detection in dermoscopic images. However, training a practical classifier becomes highly challenging due to inter-class similarity and intra-class variation in skin lesion images. To address these problems, we propose a multi-scale fusion structure that combines shallow and deep features for more accurate classification. Simultaneously, we implement three approaches to the problem of class imbalance: class weighting, label smoothing, and resampling. In addition, the HAM10000_RE dataset strips out hair features to demonstrate the role of hair features in the classification process. We demonstrate that the region of interest is the most critical classification feature for the HAM10000_SE dataset, which segments lesion regions. We evaluated the effectiveness of our model using the HAM10000 and ISIC2019 dataset. The results showed that this method performed well in dermoscopic classification tasks, with ACC and AUC of 94.0% and 99.3%, on the HAM10000 dataset and ACC of 89.8% for the ISIC2019 dataset. The overall performance of our model is excellent in comparison to state-of-the-art models.

Leg skin ulcer with atypical features: vascular wound or advanced melanoma?

BACKGROUND: Leg ulcers have various etiologies, including malignancy, although vascular issues are the most frequent cause. Malignant wounds present diagnostic challenges, with a reported prevalence rate ranging from 0.4% to 23%. This significant variability in reported prevalence appears to be due to the different settings in which data are collected, which suggests potential influence by medical specialty. Consequently, the misdiagnosis of neoplastic ulcers (eg, ulcerated melanoma) as vascular wounds is relatively common, leading to delayed diagnosis, inadequate treatment, and a dramatic worsening of the patient's prognosis. Identifying malignancy in nonresponsive wounds involves recognizing signs such as hypertrophic granulation tissue, bleeding, unusual pigmentation, and raised edges. The appearance of the perilesional skin, together with dermoscopic observation, is also crucial to differentiation. Ultimately, a biopsy may provide valuable diagnostic clarification. CASE REPORT: A case is presented of lower limb melanoma that for years was misdiagnosed as a vascular wound by multiple specialists, with delayed referral to a dermatologist and resulting recognition and diagnosis, at which time nodular satellite metastases were found. Dermoscopy and biopsy confirmed the diagnosis. The disease was already advanced, with in-transit and distant site metastases, and the prognosis was regrettably poor. CONCLUSION: This case underscores the importance of early detection and accurate diagnosis of malignant wounds, emphasizing the need to refer patients with suspicious nonresponsive ulcers to a dermatologist.

From diagnosis to intervention: a review of telemedicine's role in skin cancer care.

Skin cancer treatment is a core aspect of dermatology that relies on accurate diagnosis and timely interventions. Teledermatology has emerged as a valuable asset across various stages of skin cancer care including triage, diagnosis, management, and surgical consultation. With the integration of traditional dermoscopy and store-and-forward technology, teledermatology facilitates the swift sharing of high-resolution images of suspicious skin lesions with consulting dermatologists all-over. Both live video conference and store-and-forward formats have played a pivotal role in bridging the care access gap between geographically isolated patients and dermatology providers. Notably, teledermatology demonstrates diagnostic accuracy rates that are often comparable to those achieved through traditional face-to-face consultations, underscoring its robust clinical utility. Technological advancements like artificial intelligence and reflectance confocal microscopy continue to enhance image quality and hold potential for increasing the diagnostic accuracy of virtual dermatologic care. While teledermatology serves as a valuable clinical tool for all patient populations including pediatric patients, it is not intended to fully replace in-person procedures like Mohs surgery and other necessary interventions. Nevertheless, its role in facilitating the evaluation of skin malignancies is gaining recognition within the dermatologic community and fostering high approval rates from patients due to its practicality and ability to provide timely access to specialized care.

Pigmented fungiform papillae of the tongue.

INTRODUCTION: Pigmented fungiform papillae of the tongue is a benign condition frequent in dark skin patients. It usually appears in the second or third decade of life, and it has been reported as autosomal dominant inheritance pattern. The diagnosis is clinical, but dermoscopy could be helpful: a classical rose petal pattern is observed. The pathogenesis is unknown, and no treatments are effective. CASE REPORT: We report a case of a 15-year-old girl with a pigmented fungiform papillae and a compatible dermatoscopy pattern. CONCLUSIONS: Knowing the existence of this entity and its characteristic dermoscopy, avoids additional invasive medical test. We have to know this entity because it is a variant of normality.

INTRODUCCIÓN: La pigmentación de las papilas fungiformes linguales es una condición benigna y relativamente frecuente en pacientes con piel oscura. Suele aparecer en la segunda o tercera décadas de la vida y se han descrito casos de herencia autosómica dominante. El diagnóstico es clínico, pero la dermatoscopia es de gran ayuda: presenta un patrón clásico en pétalos de rosa. La patogénesis se desconoce y no hay tratamientos efectivos. CASO CLÍNICO: Reportamos el caso de una niña de 15 años con pigmentación de las papilas fungiformes y con patrón dermatoscópico compatible. CONCLUSIONES: Conocer la existencia de esta afección y su característica dermatoscopia evita realizar pruebas invasivas adicionales, ya que se trata una variante de la normalidad.

Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.

PURPOSE: A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growths can vary in appearance. On the other hand, malignant skin lesions correspond to skin cancer, which happens to be the most prevalent form of cancer in the United States. Skin cancer involves the unusual proliferation of skin cells anywhere on the body. The conventional method for detecting skin cancer is relatively more painful. METHODS: This work involves the automated prediction of skin cancer and its types using two stage Convolutional Neural Network (CNN). The first stage of CNN extracts low level features and second stage extracts high level features. Feature selection is done using these two CNN and ABCD (Asymmetry, Border irregularity, Colour variation, and Diameter) technique. The features extracted from the two CNNs are fused with ABCD features and fed into classifiers for the final prediction. The classifiers employed in this work include ensemble learning methods such as gradient boosting and XG boost, as well as machine learning classifiers like decision trees and logistic regression. This methodology is evaluated using the International Skin Imaging Collaboration (ISIC) 2018 and 2019 dataset. RESULTS: As a result, the first stage CNN which is used for creation of new dataset achieved an accuracy of 97.92%. Second stage CNN which is used for feature selection achieved an accuracy of 98.86%. Classification results are obtained for both with and without fusion of features. CONCLUSION: Therefore, two stage prediction model achieved better results with feature fusion.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection.

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease's therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder-decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Dermoscopic-Guided Shave Removal of Acquired Facial Melanocytic Nevi in Dark-Skinned Individuals.

BACKGROUND: Acquired melanocytic nevi are common benign skin lesions that require removal under certain circumstances. Shave removal is a straightforward treatment modality with a risk of recurrence. OBJECTIVE: To evaluate the outcome of dermoscopy-guided shave removal of acquired melanocytic nevi in the face of dark-skinned individuals who are more liable to postsurgical complications. METHODS: The study was conducted on 64 patients with acquired facial melanocytic nevi. Serial shave removal using a razor blade guided by dermoscopic examination was done until nevus-free tissue was seen, followed by electrocauterization of the base. Cosmetic outcome, patients' satisfaction, and recurrence rate were evaluated during follow-up. RESULTS: Excellent cosmetic outcome was achieved in 54.69% of patients, while 39.06% had an acceptable outcome, and 6.25% of patients had poor cosmetic outcome. Meanwhile, the recurrence rate was noticed in 5 cases only (7.8%). CONCLUSION: Dermoscopic-guided shave removal provides an easy procedure of treating common melanocytic nevi with an acceptable cosmetic result and a lower rate of recurrence even in patients with darker skin phenotypes.

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques.

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases' diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer's diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

Dermoscopy to differentiate clinically similar inflammatory and neoplastic skin lesions.

INTRODUCTION: Over the few last decades, dermoscopy has become an invaluable and popular imaging technique that complements the diagnostic armamentarium of dermatologists, being employed for both tumors and inflammatory diseases. Whereas distinction between neoplastic and inflammatory lesions is often straightforward based on clinical data, there are some scenarios that may be troublesome, e.g., solitary inflammatory lesions or tumors superimposed to a widespread inflammatory condition that may share macroscopic morphological findings. EVIDENCE ACQUISITION: We reviewed the literature to identify dermoscopic clues to support the differential diagnosis of clinically similar inflammatory and neoplastic skin lesions, also providing the histological background of such dermoscopic points of differentiation. EVIDENCE SYNTHESIS: Dermoscopic differentiating features were identified for 12 relatively common challenging scenarios, including Bowen's disease and basal cell carcinoma vs. psoriasis and dermatitis, erythroplasia of Queyrat vs. inflammatory balanitis, mammary and extramammary Paget's disease vs. inflammatory mimickers, actinic keratoses vs. discoid lupus erythematosus, squamous cell carcinoma vs. hypertrophic lichen planus and lichen simplex chronicus, actinic cheilitis vs. inflammatory cheilitis, keratoacanthomas vs. prurigo nodularis, nodular lymphomas vs. pseudolymphomas and inflammatory mimickers, mycosis fungoides vs. parapsoriasis and inflammatory mimickers, angiosarcoma vs granuloma faciale, and Kaposi sarcoma vs pseudo-Kaposi. CONCLUSIONS: Dermoscopy may be of aid in differentiating clinically similar inflammatory and neoplastic skin lesions.

Diagnosis of invasive cutaneous squamous cell carcinoma, imaging and staging.

The assessment of patients with a lesion raising the suspicion of an invasive cutaneous squamous cell carcinoma (cSCC) is a frequent clinical scenario. The management of patients with cSCC is a multistep approach, starting with the correct diagnosis. The two main diagnostic goals are to differentiate from other possible diagnoses and correctly recognize the lesion as cSCC, and then to determine the tumor spread (perform staging), that is if the patient has a common primary cSCC or a locally advanced cSCC, or a metastatic cSCC (with in-transit, regional lymph nodal, or rarely distant metastasis). The multistep diagnostic approach begins with the clinical characteristics of the primary cSCC, it is complemented with features with dermoscopy and, if available, reflectance confocal microscopy and is confirmed with histopathology. The tumor spread is assessed by physical examination and, in some cases, ultrasound and/or computed tomography or magnetic resonance imaging, mainly to investigate for regional lymph node metastasis or for local infiltration into deeper structures. In the last step, the clinical, histologic and radiologic findings are incorporated into staging systems.

Dermoscopic structures and patterns used in melanoma detection.

Melanoma is the leading cause of skin cancer-related deaths. Yet, early detection remains the most cost-effective means of preventing death from melanoma. Early detection can be achieved by a physician and/or the patient (also known as a self-skin exam). Skin exams performed by physicians are further enhanced using dermoscopy. Dermoscopy is a non-invasive technique that allows for the visualization of subsurface structures that are otherwise not visible to the naked eye. Evidence demonstrates that dermoscopy improves the diagnostic accuracy for skin cancer, including melanoma; it decreases the number of unnecessary skin biopsies of benign lesions and improves the benign-to-malignant biopsy ratio. Yet, these improvements are contingent on acquiring dermoscopy training. Dermoscopy is used by clinicians who evaluate skin lesions and perform skin cancer screenings. In general, under dermoscopy nevi tend to appear as organized lesions, with one or two structures and colors, and no melanoma-specific structures. In contrast, melanomas tend to manifest a disorganized pattern, with more than two colors and, usually, at least one melanoma-specific structure. This review is intended to familiarize the reader with the dermoscopic structures and patterns used in melanoma detection.