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.

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.

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.

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.

Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi-Network Fusion Object Detection Framework for dermoscopic pattern diagnosis.

BACKGROUND: Dermoscopy is a common method of scalp psoriasis diagnosis, and several artificial intelligence techniques have been used to assist dermoscopy in the diagnosis of nail fungus disease, the most commonly used being the convolutional neural network algorithm; however, convolutional neural networks are only the most basic algorithm, and the use of object detection algorithms to assist dermoscopy in the diagnosis of scalp psoriasis has not been reported. OBJECTIVES: Establishment of a dermoscopic modality diagnostic framework for scalp psoriasis based on object detection technology and image enhancement to improve diagnostic efficiency and accuracy. METHODS: We analyzed the dermoscopic patterns of scalp psoriasis diagnosed at 72nd Group army hospital of PLA from January 1, 2020 to December 31, 2021, and selected scalp seborrheic dermatitis as a control group. Based on dermoscopic images and major dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, we investigated a multi-network fusion object detection framework based on the object detection technique Faster R-CNN and the image enhancement technique contrast limited adaptive histogram equalization (CLAHE), for assisting in the diagnosis of scalp psoriasis and scalp seborrheic dermatitis, as well as to differentiate the major dermoscopic patterns of the two diseases. The diagnostic performance of the multi-network fusion object detection framework was compared with that between dermatologists. RESULTS: A total of 1876 dermoscopic images were collected, including 1218 for scalp psoriasis versus 658 for scalp seborrheic dermatitis. Based on these images, training and testing are performed using a multi-network fusion object detection framework. The results showed that the test accuracy, specificity, sensitivity, and Youden index for the diagnosis of scalp psoriasis was: 91.0%, 89.5%, 91.0%, and 0.805, and for the main dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, the diagnostic results were: 89.9%, 97.7%, 89.9%, and 0.876. Comparing the diagnostic results with those of five dermatologists, the fusion framework performs better than the dermatologists' diagnoses. CONCLUSIONS: Studies have shown some differences in dermoscopic patterns between scalp psoriasis and scalp seborrheic dermatitis. The proposed multi-network fusion object detection framework has higher diagnostic performance for scalp psoriasis than for dermatologists.

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-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.

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.

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.

Onychocytic Matricoma: A Clinical, Dermoscopic, and Pathological Analysis of 14 Cases.

ABSTRACT: Onychocytic matricoma (OCM) is a benign neoplasm of the nail matrix. Only 18 cases of this tumor have been reported in the literature to date. We retrospectively analyzed the clinical features of 14 patients with OCM. The most common clinical feature was longitudinal xanthopachyonychia (n = 9), followed by longitudinal leukopachyonychia (=3) and longitudinal pachymelanonychia (n = 2). The most common clinical findings identified following dermoscopy and analysis at high magnification of classical photographs were free-edge thickening of the nail plate without pitting (n = 14), longitudinal ridging (n = 7), round white clods (n = 7), white dots (n = 7), and filiform hemorrhages (n = 7), followed by oval and linear white clods (n = 5), fuzzy lateral border (n = 5), and red-purple blood clods (n = 3). Nail clipping histopathology showed a thickened nail plate with multiple, small, round-to-oval spaces. The tumor expressed immunopositivity for LEF-1. Dermoscopy of the nail plate and nail clipping histology provides useful information with regards to the differential diagnosis with subungual squamous cell carcinoma and nail melanoma. Ex vivo-in vivo correlation facilitates a better dermoscopic assessment of this unique underrecognized disease. However, the differential diagnosis between OCM and onychocytic carcinoma requires biopsy of the tumor. LEF-1 as an onychogenic marker can be used to resolve the differential diagnosis between OCM and subungual longitudinal acanthoma/seborrheic keratosis.

Photoaging assessment by Glogau classification: correlation of dermoscopy findings in the coastal population of Indonesia.

INTRODUCTION: To date, there is no gold standard for identifying photoaging. This study investigates the correlation of photoaging profiles based on the Glogau scale and the dermoscopy photoaging scale (DPAS) in a coastal population. METHODS: An analytical cross-sectional study was conducted at Cilincing Municipal Health Center in Jakarta in October 2022. Individuals living in the coastal area, 20 years and older, with Fitzpatrick skin types III-V, and with a mean daily sun exposure of ≥ 3 hours were included. The Glogau scale and DPAS were assessed through history taking, physical examination, and dermoscopic examination. A Spearman correlation test was used to assess the correlation between the Glogau scale and DPAS. RESULTS: Thirty individuals with a mean age of 41.5 ± 11.5 years participated in the study. The median Glogau score was 3 (range: 2-4). The mean DPAS score was 28.5 ± 5.6. Lentigo, hypo-hyperpigmented macules, telangiectasia, deep wrinkles, and superficial wrinkles were observed in all subjects. There was a moderate positive correlation between the Glogau scale and DPAS (r = 0.536, p = 0.002). CONCLUSIONS: The Glogau scale has a significant correlation with DPAS. DPAS can serve as a reliable, easy, practical, and fast diagnostic tool to assess the severity of aging.