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Background Machine learning has been increasingly used for skin cancer diagnosis, mainly of melanomas but also of non-melanoma skin cancers. Studies were further divided by skin cancer type, algorithm type, diagnostic gold standard, data set source, and data set size. Conclusion Insufficient evidence to draw the conclusion that an ML algorithm is more effective at NMSC screening than a specially trained dermatologist using dermoscopy for either BCC or SCC.
Source link: https://doi.org/10.1007/s00403-021-02236-9
Primary care physicians in the United States frequently cover these accessibility issues, and, consequently, play a vital role in the early detection of melanoma. However, most PCPs do not provide skin examinations. Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline, we conducted a systematic analysis and searched Ovid MEDLINE, EMBASE, and the Cochrane Library from 1946 to July 2019 to find skin screening barriers for skin testing by physicians, patients, and health services. Several obstacles persist that prevent the establishment of skin screening techniques in clinical research. A multi-faceted combination of efforts is essential for the introduction of safe and effective skin cancer-screening techniques, as well as reducing mortality risks and burden of disease for melanoma.
Source link: https://doi.org/10.1007/s00403-021-02224-z
We review the research conducted at the So Carlos Institute of Physics, which resulted in new technologies and protocols for diagnosis and treatment of non-melanoma skin lesions. Our research group pioneered the procedure in Brazil, and we've carried out numerous fundamental studies involving cell culture and animal studies that established the effectiveness of this technique in treating cancer. Multicenter clinical trials in Brazil have been performed for the treatment of non-melanoma skin cancer based on these findings. These studies resulted in commercial prodrugs and irradiation systems that are currently being considered for incorporation into Brazil's public health system, according to these studies. We also show the physical fundamentals of PDT, the latest advances that will enable us to tackle current challenges such as the use of ultrasound to treat cancer in this paper. Tumors will also be addressed.
Source link: https://doi.org/10.1007/s13538-022-01121-8
Introduction Atopic dermatitis is one of the most common skin disorders, and it may be accompanied by skin cancer risk. We wanted to investigate the connection between AD and skin cancer risk. Results We reviewed 16 studies involving a total of 9,638,093 participants examining the role of AD to skin cancers. The combined results of 16 studies revealed that AD was significantly connected to an elevated risk of skin cancer. AD was found to be an elevated risk of basal cell carcinoma and squamous cell carcinoma in reports by a review by a subgroup. AD was positively linked to an elevated risk of nonmelanoma skin cancer, BCC, and SCC, but not melanoma risk, according to a cohort study. The mechanism of AD leading to skin cancer is unclear, and further study is required to determine the possibility of a potential pathogenesis.
Source link: https://doi.org/10.1007/s13555-022-00720-2
Skin cancer is a dynamic public health issue and one of the most common forms of cancer worldwide. Skin cancer is a product of skin cancer, according to a skin biopsy. Skin cancer diagnosis is also fraught due to a lack of expertise and similar symptoms to other diseases. This work aims to provide early detection of melanoma and non-melanoma skin lesions early in life. The methodology reveals the phases of development of the proposed classification model and the benefits of each stage. In the first step, the set of photographs from the International Society for Digital Skin Imaging is processed to minimize the hair around the skin lesion. A Generative Adversarial Networks model, Then, a Generative Adversarial Networks model, creates synthetic images to adjust the number of samples per class in the preparation set. Skin lesions can be classified with the mask-based attention system.
Source link: https://doi.org/10.1007/s11265-022-01757-4
Skin cancer disease's death rate has risen in recent years. Several reports indicated that skin malignancy may rank third as a reason for death for any age group, after breast and lung cancer. In this paper, a hybrid approach was introduced that combined the advantages of the fuzzy logic system and the genetic algorithm. The modified genetic algorithm is used to select the best attributes that will be included in the fuzzy rules generation process. A new rule reduction algorithm is then used to minimize the number of rules to minimize the complexity of the fuzzy system's rule base. With a larger number of rules, the RR_algorithm reduces 20 rules from the FLS' rule base to a low accuracy percentage of 98%.
Source link: https://doi.org/10.1007/s11063-021-10656-x
According to the American Joint Committee on Cancer and National Comprehensive Cancer Network guidelines, prognostic evaluation of cutaneous melanoma depends on historical, clinicopathologic, and phenotypic risk factors, but does not take into account a patient's particular genetic risk factors. The Skin Cancer Prevention Working Group, an expert panel of dermatologists with special expertise in melanoma and non-melanoma skin cancer diagnosis and management, used a modified Delphi process to produce consensus statements on prognostic gene expression profile tests following the literature review. Conclusion: GEP tests provide additional, reproducible evidence for dermatologists to consider within the larger context of the AJCC and NCCN's eighth edition of the cutaneous melanoma recommendations when advising about prognosis and considering a sentinel lymph node biopsy.
Source link: https://doi.org/10.1007/s13555-022-00709-x
Background: The number of skin cancer cases is on the rise. However, no information is available about the risk factors of skin cancer in older people. Fitzpatrick's skin color, history of outdoor work, and socioeconomic status are all factors that could be correlated with recent skin cancers, sex, age, Fitzpatrick's skin type, history of outdoor work, and socioeconomic status. Methods In this retrospective cross-sectional analysis of a large, well documented historical cohort study, a total body skin examination was conducted for 552 patients aged between 70 and 93 years by dermatologists. Using logistic regression models, we investigated the relationships between skin cancer and its risk factors. Previous skin cancer risk increased the risk of subsequent skin cancer by 2. 6-fold and male sex nearly doubled compared to male sex. The first occurrence of skin cancer were specific risk factors for male sex and outdoor activities. Skin cancer, age, and socioeconomic status were also linked.
Source link: https://doi.org/10.1186/s12877-022-02964-1
The U. S. Food and Drug Administration has approved Enfortumab vedotin for use with urothelial cancer treatment. Also, preclinical studies have shown that ADCs against Nectin-4 against solid tumors other than urothelial cancer, and clinical trials investigating the effects of enfortumab vedotin are ongoing. The Nectin-4-targeted drugs and ADCs against Nectin-4 could also be innovative therapeutic options for skin cancers. This review highlights current knowledge of Nectin-4 in malignant tumors, the safety of enfortumab vedotin in clinical trials, and the potential of Nectin-4-targeted agents against skin cancers.
Source link: https://doi.org/10.1007/s11864-022-00940-w
Skin cancer is characterized as unregulated cell proliferation as a result of irreversible DNA damage. Melanoma is a form of skin cancer caused by melanocytes, and it could lead to severe health problems. Therapists who support the patient during the early detection of this skin cancer based on image processing are aided in its treatment. The unique property of computational pathology is the ability of spatially dissecting specific interfaces on digitized histology images. This study's primary aim is to identify skin cancer using a deep learning framework. This work focuses on a multi-aware convolution neural network with a recurrent neural network based on skin cancer histology results. This model converts the local representation of the histological images into high-dimensional details and, consequently, aggregates the features by considering their spatial configuration for performing final classification. The study of histological images of melanoma disease was conducted and verified with several classification schemes such as DarkNet-53, VGG-19, ResNet50, and Inception, using the hybrid CA-CNN-RNN model.
Source link: https://doi.org/10.1007/s00500-022-06989-x
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