Type Of Skin Cancer Research Articles

Redox homeostasis and pH monitoring: a step forward to personalized medicine and dermatology

Skin is continually subjected to various environmental stressors, such as pollution, UV rays, and other oxidative damage factors. It is obligatory to ascertain the exact importance of redox status and ROS indicators, which may be assessed in diverse physiological fluids and tissues. Reactive oxygen species (ROS) can cause lipid peroxidation, which can compromise the integrity and functionality of skin cells by degrading membrane lipids. This may compromise the skin barrier, making the skin more prone to infections and environmental irritants, increasing water loss and dryness. Collagen in the dermis can covalently link to malondialdehyde (MDA), a byproduct of lipid peroxidation generated by oxidatively damaged skin lipids, to form MDA-collagen adducts. Several types of skin cancer are associated with a heightened vulnerability of DNA purine and pyrimidine bases to oxidative damage and altered DNA repair capacity (DRC). Damage from ROS may cause hyaluronate to become less viscous, which increases its susceptibility to degradation. Elastin hydrodynamic radius decreased the rate of self-assembly in response to ROS. Alterations in redox homeostasis can lead to various diseases and conditions, including vitiligo, actinic keratosis, psoriasis, contact dermatitis, systemic sclerosis, the onset of wrinkles, and skin aging. The most recent formulations' capacity to improve pH and Redox simultaneously can be attained by carefully selecting proposed antioxidant formulations. The combination of modern preparations is currently available and should be used in accordance with the suggested guidelines.

Skin melanoma detection and classification with deep learning

Because of its rapid growth and high mortality rate, melanoma skin cancer is among the most dangerous types of skin cancer. Consequently, melanoma treatment relies heavily on early detection. Based on the U-Net architecture with VGG-16 encoder and semantic segmentation, we present a skin lesion segmentation approach for dermoscopic pictures in this study. Diagnostic imaging systems can assess the characteristics of the segmented skin lesion and assign them a classification based on those aspects. Even on computers without powerful GPUs, the training accuracy is still high enough (over 95%) using the suggested strategy, which uses fewer resources. We use the ISIC dataset, which contains dermoscopy images, to train the model in our trials. We compare the suggested skin lesion segmentation method to others that use deep learning and analyze the Sorensen-Dice and Jaccard scores to determine how well it performs. In terms of skin lesion segmentation, the experimental findings demonstrated that the proposed method outperformed the alternatives.

Naturally-derived phenethyl isothiocyanate modulates apoptotic induction through regulation of the intrinsic cascade and resulting apoptosome formation in human malignant melanoma cells

Malignant melanoma is the most aggressive type of skin cancer with increasing incidence rates worldwide. On the other hand, watercress is a rich source of phenethyl isothiocyanate (PEITC), among others, which has been widely investigated for its anticancer properties against various cancers. In the present study, we evaluated the role of a watercress extract in modulating apoptotic induction in an in vitro model of human malignant melanoma consisting of melanoma (A375, COLO-679, COLO-800), non-melanoma epidermoid carcinoma (A431) and immortalized, non-tumorigenic keratinocyte (HaCaT) cells. Moreover, the chemical composition of the watercress extract was characterized through UPLC MS/MS and other analytical methodologies. In addition, cytotoxicity was assessed by the alamar blue assay whereas apoptosis was determined, initially, by a multiplex activity assay kit (measuring levels of activated caspases −3, −8 and −9) as well as by qRT-PCR for the identification of major genes regulating apoptosis. In addition, protein expression levels were evaluated by western immunoblotting. Our data indicate that the extract contains various phytochemicals (e.g. phenolics, flavonoids, pigments, etc.) while isothiocyanates (ITCs; especially PEITC) were the most abundant. In addition, the extract was shown to exert a significant time- and dose-dependent cytotoxicity against all malignant melanoma cell lines while non-melanoma and non-tumorigenic cells exhibited significant resistance. Finally, expression profiling revealed a number of genes (and corresponding proteins) being implicated in regulating apoptotic induction through activation of the intrinsic apoptotic cascade. Overall, our data indicate the potential of PEITC as a promising anti-cancer agent in the clinical management of human malignant melanoma.

Anti-Inflammatory Benefits of Grape Pomace and Tomato Bioactives as Ingredients in Sun Oils against UV Radiation for Skin Protection

Daily exposure to solar light is not so harmless as previously thought, since UVA and UVB radiation has a significant effect on human health, by inducing skin-related inflammatory manifestations, such as hyperpigmentation, dermatitis, premature aging, erythema, and sunburn, among the most common skin conditions. At the same time, irreversible diseases such as various types of skin cancers, like basal cell carcinoma, melanoma, and squamous cell carcinoma, have begun to increase at dramatic rates, due to inflammatory and oxidative malignant effects of UV radiation exposure. Within this article, the health promoting effects of sunscreen for skin protection and especially of sunscreen enriched with natural antioxidant, anti-inflammatory, anti-allergic, and anti-cancer bio-functional ingredients are thoroughly reviewed. Emphasis is given to bioactives from grape by-products, such as bio-functional phytochemicals like flavonoids, stilbenes, and phenolic acids, as well as to tomato-derived bioactives like lycopene, which act either solely or synergistically and significantly enhance the antioxidant capacity of the composition product, as well as its photo-protection. The promising outcomes from in vitro studies and those reported from in vivo approaches, as well as the mechanisms of the obtained antioxidant, anti-inflammatory, anti-thrombotic, and antitumor action(s) of sunscreens infused with such natural bioactives, are also outlined. Limitations, such as the difficulties in incorporating lipophilic compounds like lycopene and/or amphiphilic phenolics into sunscreen cosmetic formulations, as well as future perspectives on the overall benefits that these compounds give to sunscreens, helping to improve properties such as smell, color, homogeneity, water resistance, and especially the Sun Protection Factor (SPF), are also discussed.

Retrospective Single-Center Case Study of Clinical Variables and the Degree of Actinic Elastosis Associated with Rare Skin Cancers.

(1) Background: Rare skin cancers include epithelial, neuroendocrine, and hematopoietic neoplasias as well as cutaneous sarcomas. Ultraviolet (UV) radiation and sunburns are important drivers for the incidence of certain cutaneous sarcomas; however, the pathogenetic role of UV light is less clear in rare skin cancers compared to keratinocyte cancer and melanoma. In this study, we compared the degree of actinic elastosis (AE) as a surrogate for lifetime UV exposure among selected rare skin cancers (atypical fibroxanthoma [AFX], pleomorphic dermal sarcoma [PDS], dermatofibrosarcoma protuberans [DFSP], Kaposi sarcoma [KS], Merkel cell carcinoma [MCC], and leiomyosarcoma [LMS]) while taking into account relevant clinical variables (age, sex, and body site). (2) Methods: We newly established a semi-quantitative score for the degree of AE ranging from 0 = none to 3 = total loss of elastic fibers (basophilic degeneration) and multiplied it by the perilesional vertical extent (depth), measured histometrically (tumor-associated elastosis grade (TEG)). We matched the TEG of n = 210 rare skin cancers from 210 patients with their clinical variables. (3) Results: TEG values were correlated with age and whether tumors arose on UV-exposed body sites. TEG values were significantly higher in AFX and PDS cases compared to all other analyzed rare skin cancer types. As expected, TEG values were low in DFSP and KS, while MCC cases exhibited intermediate TEG values. (4) Conclusions: High cumulative UV exposure is more strongly associated with AFX/PDS and MCC than with other rare skin cancers. These important results expand the available data associated with rare skin cancers while also offering insight into the value of differentiating among these tumor types based on their relationship with sun exposure, potentially informing preventative, diagnostic and/or therapeutic approaches.

Solitary cutaneous neoplasms: analysing the uncertain behaviour with the aid of histopathology

BACKGROUND: Skin tumours can be classified as either benign or malignant, resulting due to the proliferation of one or more components of the skin. Reportedly, there has been a rise in the prevalence of skin cancer in recent decades, which has led to rely on histological evidence to distinguish between various types of skin cancer. AIM: This study aims to provide a comprehensive description of the occurrence, symptoms, unpredictable nature, and range of histopathological spectrum in different types of skin tumours. METHODS: This prospective research was conducted in the outpatient department of the Department of Dermatology at the Government General Hospital in Anantapur from July 2019 to July 2023. Patients who did not provide informed consent, those with infectious or cystic swellings, or those with multiple lesions were excluded from this study. Histopathological confirmation is obtained from all excisional biopsies of single cutaneous swellings, and tumours are classified based on the criteria established by the World Health Organization (WHO). RESULTS: The study included a total of 123 individual cutaneous tumours, with 98 cases (79.67%) being classified as benign and 25 cases (20.32%) classified as malignant. The age group most affected is adults between the ages of 26 and 44, with a prevalence rate of 31.7%. Following closely behind are middle-aged individuals, with a prevalence rate of 30.08%. The males constitute 46.34% (57 cases) and the females constitutes 53.65% (66 cases). The extremities were the most frequently affected site, accounting for 53 cases (43.08%), followed by the head and neck region (29.26%). Based on the WHO classification of skin tumours, there were 42 cases (34.14%) of subcutaneous tissue tumours and 31 cases (25.20%) of soft tissue tumours. The prevalence of keratinocyte tumours is 26 (21.13%), whereas appendageal tumours account for 16 (13%) of cases. Melanocytic and neural tumours are the least prevalent, each representing 4 (3.25%) of cases. The majority of benign tumours arise from the subcutaneous tissues, whereas malignant tumours grow from keratinocytic differentiation. CONCLUSION: Our study revealed that the majority of tumours displayed ambiguous clinical behaviour, which resulted in erroneous diagnoses. Hence confirmation by histopathology is crucial for accurate diagnosis and prompt management.

MORTALITY FROM MALIGNANT MELANOMA IN BRAZIL BETWEEN THE YEARS 2013 AND 2022: Regional trends, in relation to gender, ethnicity and age group

Introduction: Skin cancer is the most common type of cancer in Brazil, with a high cure rate if treated early. Melanoma, an aggressive type of skin cancer, accounts for 79% of deaths from skin cancer. Its incidence is increasing globally due to sun exposure and other behavioral changes. This study analyzes mortality from cutaneous melanoma in Brazil, considering regional, gender, and ethnic factors. Methodology: Retrospective and observational study based on data from the Mortality Information System (SIM) from 2013 to 2022. Data on deaths from malignant melanoma of the skin were analyzed across various regions, genders, age groups, and ethnicities. Relevant literature selection was conducted through searches in BVS and PubMed. Results: The mortality rate from melanoma ranged from 1,547 to 1,959 deaths annually from 2013 to 2022. The Southeast region had the highest number of deaths, followed by the South and Northeast. The Southeast recorded a total of 7,821 deaths during the period, while the North had the lowest number, with 471 deaths. Men were more affected than women, accounting for 57.5% of deaths. White ethnicity accounted for 82.1% of deaths. Mortality increased progressively with age, stabilizing after age 70. Discussion: Melanoma mortality exhibited fluctuating patterns in Brazil, with peaks in 2019 and declines in 2020-2021. Regional disparities reflect differences in healthcare access, cultural habits, and socioeconomic factors. Higher mortality among men may be linked to greater exposure to risk factors and lower rates of early diagnosis. The high incidence among white individuals underscores the need for specific health policies. Preventive strategies should focus on raising awareness about sun exposure, early diagnosis, and improving healthcare infrastructure. Conclusion: The study highlights the need for public health policies tailored to regional specificities to reduce melanoma mortality in Brazil. Awareness campaigns, screening programs, and improved treatment access are essential to mitigate the effects of cutaneous melanoma.

Cutting-edge Advances in Nanocarrier-Facilitated Topical Drug Delivery Systems for Targeted Skin Cancer Therapy: A Comprehensive Review.

Skin cancer is one of the most common and complex types of the disease, resulting in a high mortality rate worldwide. Skin cancer can be treated with chemotherapy, surgery, radiotherapy, etc. In most cases, a patient's condition and the type of skin cancer determine the recommended treatment options. As a result of poor penetration of the drug into stratum corneum or lesions, low efficacy, and higher concentrations of active pharmaceutical ingredients required to achieve a therapeutic effect, the efficacy of skin cancer therapy has been limited. The high dose requirement, as well as poor bioavailability at the site of action, causes skin inflammation, which greatly hinders drug absorption. This review mainly focuses on research on nanocarriers for sitespecific and controlled delivery of therapeutics for skin cancer treatment. The information related to various nanocarriers systems for skin cancer will be illustrated. This also focused on patents, clinical trials, and research carried out in the field of liposomes, niosomes, ethosomes, nanoparticles, microemulsion, nanoemulsions, gels, nanogels, hydrogels, dendrimers, and nanofibers for treating skin cancer. Nanotechnology-based therapy has shown great promise in controlling skin cancer and can be used to deliver drugs more effectively.

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.

Transformer-based decoder of melanoma classification using hand-crafted texture feature fusion and Gray Wolf Optimization algorithm

Melanoma is a type of skin cancer that poses significant health risks and requires early detection for effective treatment. This study proposing a novel approach that integrates a transformer-based model with hand-crafted texture features and Gray Wolf Optimization, aiming to enhance efficiency of melanoma classification. Preprocessing involves standardizing image dimensions and enhancing image quality through median filtering techniques. Texture features, including GLCM and LBP, are extracted to capture spatial patterns indicative of melanoma. The GWO algorithm is applied to select the most discriminative features. A transformer-based decoder is then employed for classification, leveraging attention mechanisms to capture contextual dependencies. The experimental validation on the HAM10000 dataset and ISIC2019 dataset showcases the effectiveness of the proposed methodology. The transformer-based model, integrated with hand-crafted texture features and guided by Gray Wolf Optimization, achieves outstanding results. The results showed that the proposed method performed well in melanoma detection tasks, achieving an accuracy and F1-score of 99.54% and 99.11% on the HAM10000 dataset, and an accuracy of 99.47%, and F1-score of 99.25% on the ISIC2019 dataset.• We use the concepts of LBP and GLCM to extract features from the skin lesion images.• The Gray Wolf Optimization (GWO) algorithm is employed for feature selection.• A decoder based on Transformers is utilized for melanoma classification.

The causal relationship between immune cells mediating FIT3L, CCL4, OSM, and skin-derived deteriorated tumors.

Observational studies have identified a dual effect of circulating inflammatory proteins and immune cells on cancer progression. However, the specific mechanisms of action have not been clarified in the exacerbation of cutaneous-origin tumors. Therefore, this study aims to investigate whether the causal relationship between circulating inflammatory factors and basal cell carcinoma (BCC), cutaneous malignant melanoma (SKCM), and cutaneous squamous cell carcinoma (cSCC) is regulated by immune cells. This study employed the Two-Sample Mendelian Randomization (TSMR) approach to investigate the causal relationships between 91 circulating inflammatory factors and three prevalent types of skin cancer from a genetic perspective. Bayesian Weighted Mendelian Randomization (BWMR) was also used to validate correlation and reverse MR to assess inverse relationships. Subsequent sensitivity analyses were conducted to limit the impact of heterogeneity and pleiotropy. Finally, the two-step Mendelian Randomization (two-step MR) method was utilized to ascertain the mediating effects of specific immune cell traits in the causal pathways linking circulating inflammatory factors with BCC, SKCM, and cSCC. The Inverse Variance Weighted (IVW) method and the Bayesian Weighted Algorithm collectively identified nine inflammatory factors causally associated with BCC, SKCM, and cSCC. The results from Cochran's Q test, mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), and MR-Egger intercept were not statistically significant (p<0.05). Additionally, the proportions mediated by CD4+ CD8dim T cell %leukocyte, CD4-CD8-Natural Killer T %T cell, and CD20 on IgD-CD38-B cell for FIt3L, CCL4, and OSM were 9.26%, 8.96%, and 10.16%, respectively. Immune cell levels potentially play a role in the modulation process between circulating inflammatory proteins and cutaneous-origin exacerbated tumors. This finding offers a new perspective for the in-depth exploration of cutaneous malignancies.

Subcutaneous Basal Cell Carcinoma arising from Surgical Drain Insertion Site: A Case Report

Basal cell carcinoma (BCC) is the most common type of skin cancer and primarily affects the sun-exposed areas of the body. It usually presents as visible skin lesions; cases of subcutaneous BCC without apparent skin involvement are extremely rare. Here, we present the case of an 81-year-old female who underwent hysterectomy for an ectopic pregnancy 50 years prior and presented with a subcutaneous mass in the lower abdomen that gradually increased in size over the previous 6 months. The mass only displayed a mild hypertrophic scar the color of skin at the site where a surgical drain had been inserted. Ultrasonography revealed a nonspecific soft tissue tumor, for which we recommended an ultrasound-guided biopsy. Histopathological analysis indicated the need to differentiate between BCC and trichoblastoma. Subsequent wide excision with a 5-mm margin confirmed the BCC diagnosis. This case highlights the challenges in diagnosing and managing subcutaneous BCC with unusual clinical symptoms.

Advanced and Metastatic Non-Melanoma Skin Cancer: Epidemiology, Risk Factors, Clinical Features, and Treatment Options.

Non-melanoma skin cancers (NMSC) form the majority of skin cancers, with basal cell carcinoma (BCC) being the most common and cutaneous squamous cell carcinoma (cSCC) being second. Prolonged ultraviolet (UV) exposure, aging, male gender, and immunosuppression represent most of the causes of this category of diseases. BCCs and cSCCs both include different types of skin cancers, such as nodular or morpheaform BCC or flat cSCC. Locally advanced and metastatic NMSCs cannot be treated surgically; thus, systemic therapy (TKI and Immunotherapy) is needed. Interestingly, NMSCs are frequently linked to abnormal Hedgehog (HH) signaling which most systemic immunotherapies for these cancers are based upon. Of note, the first line therapies of BCC, sonidegib and vismodegib, are HH inhibitors. Programmed death receptor 1 antibody (PD-1) inhibitors such as cemiplimab, pembrolizumab, and nivolumab have been approved for the treatment of cSCC. Thus, this paper reviews the epidemiology, risk factors, clinical features, and treatment options for both BCC and cSCC.

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.

Discovery of Mitogen-Activated Protein Kinase for Melanoma Using Melphalan Based Hybrid Classic 3-Point Pharmacophore Virtual Screening and Molecular Docking Approach

Melanoma, a highly aggressive type of skin cancer, poses a significant health risk due to its proclivity for metastasis and poor response to conventional treatments. Mutations in the MEPK are one of the key molecular alterations driving melanoma progression, resulting in constitutive activation of the MAP2K signaling pathway. Targeting this pathway, specifically the MAP2K, has emerged as a promising approach to melanoma treatment. This study investigates the therapeutic potential of Mitogen-activated protein kinase, which are compounds designed to inhibit the abnormal activity of mutated MEK, disrupting the oncogenic signaling cascade and stopping melanoma progression. The ChEMBL 2D database was used to conduct a comprehensive pharmacophore-based screening to identify potential mitogen-activated protein kinase with the desired molecular properties. Afterwards the initial screening, selected compounds underwent rigorous molecular docking studies to verify their binding affinity and interaction patterns with the MAP2K substance. Among the compounds tested, CHEMBL852 (Melphalan), CHEMBL250892(R)-Melphalan and CHEMBL1200863 (Metyrosine) had particularly high binding affinities, indicating potential efficacy as mitogen-activated protein kinase. Toxicity tests were carried out to assess the safety profiles of these compounds. Notably, metyrosine had a favorable toxicity profile across multiple endpoints, including hepatotoxicity, carcinogenicity, immunotoxicity, and cytotoxicity, indicating its potential as a safer therapeutic candidate. Metyrosine pharmacokinetics were further evaluated using ADME studies, which demonstrated its high water solubility and moderate lipophilicity, indicating favorable drug-like properties. These findings suggest that metyrosine may have higher bioavailability and fewer side effects than other potential inhibitors. Subsequently, this study identifies promising lead compounds, specifically metyrosineas, potential candidates for the research and development of novel mitogen-activated protein kinase for melanoma therapy. These compounds require additional experimental validation, optimization, and preclinical research to determine their therapeutic efficacy, safety, and potential for clinical application. This study highlights the importance of targeted therapies in the growing melanoma treatment landscape, paving the way for the development of novel strategies to combat this devastating disease.

Innovations in Skin Cancer Nanotechnology: A Comprehensive Review.

Skin cancer is the most common type of cancer among white people, according to the World Health Organisation. The incidence of melanoma and non-melanoma skin cancers has increased to epidemic levels, making them the most widespread type of skin cancer. Melanoma is a very aggressive form of cancer, characterized by limited treatment choices due to multidrug resistance and an extremely low probability of patient survival. This article explores the various impediments and limitations associated with conventionally available treatments. Chemotherapy, radiation, immunotherapy, and targeted therapy are among the conventional treatments for melanoma; however, each of these approaches has several adverse reactions. Recently, there has been a focus on biological and pharmacological research on developing alternative, site-specific therapy approaches. Nanotechnology offers several benefits in this regard, with the potential to enhance the longevity of melanoma patients while minimizing adverse effects. Nanoparticles serve as effective drug carrier systems due to their capacity to improve the solubility of medications with low water solubility, modify pharmacokinetics, prolong drug half-life by reducing immunogenicity, boost bioavailability, and decrease drug metabolism. This article highlights recent advancements in utilizing several nanotechnological techniques, including solid lipid nanoparticles, nanostructured lipid carriers, liposomes, transferosomes, ethosomes, and nanoemulsion polymeric mixed micelles.

Bluish veil detection and lesion classification using custom deep learnable layers with explainable artificial intelligence (XAI)

Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a critical feature for diagnosing melanoma, yet research into detecting BWV in dermatological images is limited. This study utilizes a non-annotated skin lesion dataset, which is converted into an annotated dataset using a proposed imaging algorithm (color threshold techniques) on lesion patches based on color palettes. A Deep Convolutional Neural Network (DCNN) is designed and trained separately on three individual and combined dermoscopic datasets, using custom layers instead of standard activation function layers. The model is developed to categorize skin lesions based on the presence of BWV. The proposed DCNN demonstrates superior performance compared to the conventional BWV detection models across different datasets. The model achieves a testing accuracy of 85.71 % on the augmented PH2 dataset, 95.00 % on the augmented ISIC archive dataset, 95.05 % on the combined augmented (PH2+ISIC archive) dataset, and 90.00 % on the Derm7pt dataset. An explainable artificial intelligence (XAI) algorithm is subsequently applied to interpret the DCNN's decision-making process about the BWV detection. The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.

Non-surgical Synergistic Interventions for the Treatment of Skin Cancer.

Skin cancer is broadly classified into two categories i.e., non-melanoma skin cancer (NMSC) and malignant melanoma (MM), with MM having a greater fatality rate than NMSC. A large number of treatment strategies currently exist for these skin cancer types, ranging from monotherapies to complex multifaceted synergistic interventions including dual therapies, trimodality therapy, and multicomponent combinations therapy. These combinatorial cancer treatments have delivered more favorable results when compared with monotherapies, and although combination treatments increase the cost of treatment, these regimens have lower side effect profiles, decreased resistance, high efficacy and an improved long-term response. Synergistic combination treatments for skin cancer are often complex, wide-ranging and encompass diverse platforms with various mechanisms of action. An understanding of the physiological potential, as well the efficacy of such treatments, is therefore vital to ensure patients receive the best possible treatment. This review therefore focuses on the current advancements and existing non-surgical combinative drug delivery methods utilized for treating skin cancer. It encompasses the diverse pharmaceutical delivery systems, clinical outcomes, and oncology strategies employed and aims to highlight the role of non-surgical combination therapies in enhancing patient compliance, reducing treatment durations, and improving overall survival rates while addressing relapses and metastasis. The promising outlook of the research being conducted in this field has also been provided, as well as the barriers to the effective treatment of this complex condition.

RWHO: A hybrid of CNN architecture and optimization algorithm to predict basal cell carcinoma skin cancer in dermoscopic images

Basal cell carcinoma (BCC) is a type of skin cancer that initiates from the epithelial cells of our skin. Compared to other forms of cancer, BCC infrequently spreads to other parts of the body. It has a risk of local attack and demolition of surrounding tissues. Typically, BCC shows as one or numerous small, glowing nodules exhibiting central depressions. These knots are commonly found on the sun-exposed skin areas of older adults. Many dermatoscopic methods are available for diagnosing and predicting such kinds of skin cancers. But, medical professionals find it difficult to diagnose at some kind of images at the early stages. An automated methodology to predict such types of skin lesions would be better for such a diagnosis. In the present work, a new computer-assisted algorithm called RESNET50-WHO (RWHO) has been introduced to predict and diagnose BCC skin cancer. The method uses a combination of deep learning algorithm RESNET 50 and a metaheuristic algorithm, called wildebeest herd optimization (WHO) Algorithm to do prediction. The initial features from the images are extracted using RESNET 50. The output is given to the WHO algorithm to extract the beneficial features to reduce the time complexity. The method is tested using the PH2 dataset. The results obtained using the proposed algorithm is compared with the state-of-art optimization algorithms and evaluated. The conclusive findings specify that the proposed algorithm beats the comparative methods, yielding superior results

Skin Disease Recognition Based on Deep Learning Algorithms: A Review

The sharp increase in cases of melanoma and other skin cancers worldwide highlights the urgent need for improved diagnostic methods. Because skin lesions vary widely and access to dermatological knowledge is limited in resource-poor areas, traditional methods - which rely on visual inspection and clinical experience - have difficulty identifying diseases accurately. This situation requires innovative approaches to improve accessibility and diagnostic accuracy. To address these issues, this work uses deep learning (DL) and convolutional neural networks (CNNs). This paper is trying to transform skin cancer diagnosis through the use of large databases of dermoscopic images and advanced artificial intelligence algorithms. In order to evaluate the effectiveness of CNNs and DL in identifying skin diseases, we conducted a comprehensive analysis of the literature, focusing on the accuracy of skin cancer type classification. Our approach focused on model architectures, data preparation methods, and performance indicators while examining existing research using AI algorithms to diagnose skin cancer. With the ultimate goal of improving patient outcomes through early detection and accurate classification of skin conditions, this approach not only underscores the great potential of DL and CNN in transcending traditional diagnostic limitations, but also highlights the continued development of AI-based tools in pathology. Dermatology. Diagnosis.