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Skin cancer represents a significant global health concern, where early and precise diagnosis plays a pivotal role in improving treatment efficacy and patient survival rates. Nonetheless, the inherent visual similarities between benign and malignant lesions pose substantial challenges to accurate classification. To overcome these obstacles, this study proposes an innovative hybrid deep learning model that combines ConvNeXtV2 blocks and separable self-attention mechanisms, tailored to enhance feature extraction and optimize classification performance. The inclusion of ConvNeXtV2 blocks in the initial two stages is driven by their ability to effectively capture fine-grained local features and subtle patterns, which are critical for distinguishing between visually similar lesion types. Meanwhile, the adoption of separable self-attention in the later stages allows the model to selectively prioritize diagnostically relevant regions while minimizing computational complexity, addressing the inefficiencies often associated with traditional self-attention mechanisms. The model was comprehensively trained and validated on the ISIC 2019 dataset, which includes eight distinct skin lesion categories. Advanced methodologies such as data augmentation and transfer learning were employed to further enhance model robustness and reliability. The proposed architecture achieved exceptional performance metrics, with 93.48% accuracy, 93.24% precision, 90.70% recall, and a 91.82% F1-score, outperforming over ten Convolutional Neural Network (CNN) based and over ten Vision Transformer (ViT) based models tested under comparable conditions. Despite its robust performance, the model maintains a compact design with only 21.92 million parameters, making it highly efficient and suitable for model deployment. The Proposed Model demonstrates exceptional accuracy and generalizability across diverse skin lesion classes, establishing a reliable framework for early and accurate skin cancer diagnosis in clinical practice.

Non-melanoma skin cancers (NMSCs) are the most common malignancy worldwide, of which 99% are basal cell carcinomas (BCCs) and squamous cell carcinomas (SCCs) of skin. NMSCs are generally considered a curable diseases, yet they currently pose an increasing global healthcare problem due to rising incidence. This has led to a shift in emphasis on prevention of NMSCs with development of various skin cancer prevention programs worldwide. This article aims to summarize the most recent changes and advances made in NMSC management with a focus on prevention, screening, diagnosis, and staging.

Skin cancer is the most common type of cancer in the US with an increasing prevalence worldwide. While ultraviolet (UV) radiation is a well-known risk factor, there is emerging evidence that the microbiota may also contribute. In recent years, the human microbiota has become a topic of great interest, and its association with inflammatory skin diseases (i.e., atopic dermatitis, acne, rosacea) has been explored. Little is known of the role of microbiota in skin cancer, but with the recognized link between microbial dysbiosis and inflammation, and knowledge that microbiota modulates the effect of UV-induced immunosuppression, theories connecting the two have surfaced. In this paper, we provide a comprehensive review of the key literature on human microbiota, especially the skin microbiota, and skin cancer (i.e., non-melanoma skin cancer, melanoma, cutaneous T cell lymphoma). Also, mechanistic perspectives as to how our microbiota influence skin cancer development and treatment are offered.

Opinion statement The development and implementation of artificial intelligence is beginning to impact the care of dermatology patients. Although the clinical application of AI in dermatology to date has largely focused on melanoma, the prevalence of non-melanoma skin cancers, including basal cell and squamous cell cancers, is a critical application for this technology. The need for a timely diagnosis and treatment of skin cancers makes finding more time efficient diagnostic methods a top priority, and AI may help improve dermatologists’ performance and facilitate care in the absence of dermatology expertise. Beyond diagnosis, for more severe cases, AI may help in predicting therapeutic response and replacing or reinforcing input from multidisciplinary teams. AI may also help in designing novel therapeutics. Despite this potential, enthusiasm in AI must be tempered by realistic expectations regarding performance. AI can only perform as well as the information that is used to train it, and development and implementation of new guidelines to improve transparency around training and performance of algorithms is key for promoting confidence in new systems. Special emphasis should be placed on the role of dermatologists in curating high-quality datasets that reflect a range of skin tones, diagnoses, and clinical scenarios. For ultimate success, dermatologists must not be wary of AI as a potential replacement for their expertise, but as a new tool to complement their diagnostic acumen and extend patient care.

Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life.

Until now, the clinical differentiation between a nevus and a melanoma is still challenging in some cases. Line-field confocal optical coherence tomography (LC-OCT) is a new tool with the aim to change that. The aim of the study was to evaluate LC-OCT for the discrimination between nevi and melanomas. A total of 84 melanocytic lesions were examined with LC-OCT and 36 were also imaged with RCM. The observers recorded the diagnoses, and the presence or absence of the 18 most common imaging parameters for melanocytic lesions, nevi, and melanomas in the LC-OCT images. Their confidence in diagnosis and the image quality of LC-OCT and RCM were evaluated. The most useful criteria, the sensitivity and specificity of LC-OCT vs. RCM vs. histology, to differentiate a (dysplastic) nevus from a melanoma were analyzed. Good image quality correlated with better diagnostic performance (Spearman correlation: 0.4). LC-OCT had a 93% sensitivity and 100% specificity compared to RCM (93% sensitivity, 95% specificity) for diagnosing a melanoma (vs. all types of nevi). No difference in performance between RCM and LC-OCT was observed (McNemar’s p value = 1). Both devices falsely diagnosed dysplastic nevi as non-dysplastic (43% sensitivity for dysplastic nevus diagnosis). The most significant criteria for diagnosing a melanoma with LC-OCT were irregular honeycombed patterns (92% occurrence rate; 31.7 odds ratio (OR)), the presence of pagetoid spread (89% occurrence rate; 23.6 OR) and the absence of dermal nests (23% occurrence rate, 0.02 OR). In conclusion LC-OCT is useful for the discrimination between melanomas and nevi.

Opinion statementDermatoscopy (dermoscopy) improves the diagnosis of benign and malignant cutaneous neoplasms in comparison with examination with the unaided eye and should be used routinely for all pigmented and non-pigmented cutaneous neoplasms. It is especially useful for the early stage of melanoma when melanoma-specific criteria are invisible to the unaided eye. Preselection by the unaided eye is therefore not recommended. The increased availability of polarized dermatoscopes, and the extended use of dermatoscopy in non-pigmented lesions led to the discovery of new criteria, and we recommend that lesions should be examined with polarized and non-polarized dermatoscopy. The “chaos and clues algorithm” is a good starting point for beginners because it is easy to use, accurate, and it works for all types of pigmented lesions not only for those melanocytic. Physicians, who use dermatoscopy routinely, should be aware of new clues for acral melanomas, nail matrix melanomas, melanoma in situ, and nodular melanoma. Dermatoscopy should also be used to distinguish between different subtypes of basal cell carcinoma and to discriminate highly from poorly differentiated squamous cell carcinomas to optimize therapy and management of non-melanoma skin cancer. One of the most exciting areas of research is the use of dermatoscopic images for machine learning and automated diagnosis. Convolutional neural networks trained with dermatoscopic images are able to diagnose pigmented lesions with the same accuracy as human experts. We humans should not be afraid of this new and exciting development because it will most likely lead to a peaceful and fruitful coexistence of human experts and decision support systems.

PurposeWe report the prevalence and economic cost of skin cancer treatment compared to other cancers overall in the USA from 2012 to 2018.MethodsUsing the Medical Expenditure Panel Survey full-year consolidated data files and associated medical conditions and medical events files, we estimate the prevalence, total costs, and per-person costs of treatment for melanoma and non-melanoma skin cancer among adults aged ≥ 18 years in the USA. To understand the changes in treatment prevalence and treatment costs of skin cancer in the context of overall cancer treatment, we also estimate the prevalence, total costs, and per-person costs of treatment for non-skin cancer among US adults.ResultsDuring 2012–15 and 2016–18, the average annual number of adults treated for any skin cancer was 5.8 (95% CI: 5.2, 6.4) and 6.1 (95% CI: 5.6, 6.6) million, respectively, while the average annual number of adults treated for non-skin cancers rose from 10.8 (95% CI: 10.0, 11.5) to 11.9 (95% CI: 11.2, 12.6) million, respectively. The overall estimated annual costs rose from $8.0 (in 2012–2015) to $8.9 billion (in 2016–18) for skin cancer treatment and $70.2 to $79.4 billion respectively for non-skin cancer treatment.ConclusionThe prevalence and economic cost of skin cancer treatment modestly increased in recent years. Given the substantial cost of skin cancer treatment, continued public health attention to implementing evidence-based sun-safety interventions to reduce skin cancer risk may help prevent skin cancer and the associated treatment costs.

Skin optical inspection is an imperative procedure for a suspicious dermal lesion since very early skin cancer detection can guarantee total recovery. Dermoscopy, confocal laser scanning microscopy, optical coherence tomography, multispectral imaging, multiphoton laser imaging, and 3D topography are the most outstanding optical techniques implemented for skin examination. The accuracy of dermatological diagnoses attained by each of those methods is still debatable, and only dermoscopy is frequently used by all dermatologists. Therefore, a comprehensive method for skin analysis has not yet been established. Multispectral imaging (MSI) is based on light–tissue interaction properties due to radiation wavelength variation. An MSI device collects the reflected radiation after illumination of the lesion with light of different wavelengths and provides a set of spectral images. The concentration maps of the main light-absorbing molecules in the skin, the chromophores, can be retrieved using the intensity values from those images, sometimes even for deeper-located tissues, due to interaction with near-infrared light. Recent studies have shown that portable and cost-efficient MSI systems can be used for extracting skin lesion characteristics useful for early melanoma diagnoses. This review aims to describe the efforts that have been made to develop MSI systems for skin lesions evaluation in the last decade. We examined the hardware characteristics of the produced devices and identified the typical structure of an MSI device for dermatology. The analyzed prototypes showed the possibility of improving the specificity of classification between the melanoma and benign nevi. Currently, however, they are rather adjuvants tools for skin lesion assessment, and efforts are needed towards a fully fledged diagnostic MSI device.

Background The disease burden of non-melanoma skin cancer (NMSC) has become a significant public health threat. We aimed to conduct a comprehensive analysis to mitigate the health hazards of NMSC. Methods This study had three objectives. First, we reported the NMSC-related disease burden globally and for different subgroups (sex, socio-demographic index (SDI), etiology, and countries) in 2019. Second, we examined the temporal trend of the disease burden from 1990 to 2019. Finally, we used the Bayesian age-period-cohort (BAPC) model integrated nested Laplacian approximation to predict the disease burden in the coming 25 years. The Norpred age-period-cohort (APC) model and the Autoregressive Integrated Moving Average (ARIMA) model were used for sensitivity analysis. Results The disease burden was significantly higher in males than in females in 2019. The results showed significant differences in disease burden in different SDI regions. The better the socio-economic development, the heavier the disease burden of NMSC. The number of new cases and the ASIR of basal cell carcinoma (BCC) were higher than that of squamous cell carcinoma (SCC) in 2019 globally. However, the number of DALYs and the age-standardized DALYs rate were the opposite. There were statistically significant differences among different countries. The age-standardized incidence rate (ASIR) of NMSC increased from 54.08/100,000 (95% uncertainty interval (UI): 46.97, 62.08) in 1990 to 79.10/100,000 (95% UI: 72.29, 86.63) in 2019, with an estimated annual percentage change (EAPC) of 1.78. Other indicators (the number of new cases, the number of deaths, the number of disability-adjusted life years (DALYs), the age-standardized mortality rate (ASMR), and the age-standardized DALYs rate) showed the same trend. Our predictions suggested that the number of new cases, deaths, and DALYs attributable to NMSC would increase by at least 1.5 times from 2020 to 2044. Conclusions The disease burden attributable to NMSC will continue to increase or remain stable at high levels. Therefore, relevant policies should be developed to manage NMSC, and measures should be taken to target risk factors and high-risk groups.