Visual inspection and dermoscopy, alone or in combination, for diagnosing keratinocyte skin cancers in adults.
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ABSTRACT:
SUBMITTER: Dinnes J
PROVIDER: S-EPMC6516870 | biostudies-literature |
REPOSITORIES: biostudies-literature
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| S-EPMC6517096 | biostudies-literature
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| S-EPMC6492463 | biostudies-literature
Project description:When it comes to skin tumors and cancers, melanoma ranks among the most prevalent and deadly. With the advancement of deep learning and computer vision, it is now possible to quickly and accurately determine whether or not a patient has malignancy. This is significant since a prompt identification greatly decreases the likelihood of a fatal outcome. Artificial intelligence has the potential to improve healthcare in many ways, including melanoma diagnosis. In a nutshell, this research employed an Inception-V3 and InceptionResnet-V2 strategy for melanoma recognition. The feature extraction layers that were previously frozen were fine-tuned after the newly added top layers were trained. This study used data from the HAM10000 dataset, which included an unrepresentative sample of seven different forms of skin cancer. To fix the discrepancy, we utilized data augmentation. The proposed models outperformed the results of the previous investigation with an effectiveness of 0.89 for Inception-V3 and 0.91 for InceptionResnet-V2.
| S-EPMC10217211 | biostudies-literature
Project description:IntroductionGlobally, incidence, prevalence and mortality rates of skin cancers are escalating. Earlier detection by well-trained primary care providers in techniques such as dermoscopy could reduce unnecessary referrals and improve longer term outcomes. A review of reviews is planned to compare and contrast the conduct, quality, findings and conclusions of multiple systematic and scoping reviews addressing the effectiveness of training primary care providers in dermoscopy, which will provide a critique and synthesis of the current body of review evidence.Methods and analysisFour databases (Cochrane, CINAHL, EMBASE and MEDLINE Complete) will be comprehensively searched from database inception to identify published, peer-reviewed English-language articles describing scoping and systematic reviews of the effectiveness of training primary care providers in the use of dermoscopy to detect skin cancers. Two researchers will independently conduct the searches and screen the results for potentially eligible studies using 'Research Screener' (a semi-automated machine learning tool). Backwards and forwards citation tracing will be conducted to supplement the search. A narrative summary of included reviews will be conducted. Study characteristics, for example, population; type of educational programme, including content, delivery method, duration and assessment; and outcomes for dermoscopy will be extracted into a standardised table. Data extraction will be checked by the second reviewer. Methodological quality will be evaluated by two reviewers independently using the Critical Appraisal Tool for Health Promotion and Prevention Reviews. Results of the assessments will be considered by the two reviewers and any discrepancies will be resolved by team consensus.Ethics and disseminationEthics approval is not required to conduct the planned systematic review of peer-reviewed, published articles because the research does not involve human participants. Findings will be published in a peer-reviewed journal, presented at leading public health, cancer and primary care conferences, and disseminated via website postings and social media channels.Prospero registration numberCRD42023396276.
| S-EPMC10729275 | biostudies-literature
Project description: Not available
| S-EPMC10718124 | biostudies-literature
Project description:Squamous cell carcinoma (SCC) is a common type of skin cancer that typically arises from premalignant precursor lesions named actinic keratoses (AK). Chronic inflammation is a well-known promoter of skin cancer progression. AK and SCC have been associated with an overabundance of the bacterium Staphylococcus aureus (S. aureus). Certain secreted products from S. aureus are known to promote cutaneous pro-inflammatory responses; however, not all S. aureus strains produce these. As inflammation plays a key role in SCC development, we investigated the pro-inflammatory potential and toxin secretion profiles of skin-cancer associated S. aureus. Sterile culture supernatants ("secretomes") of S. aureus clinical strains isolated from AK and SCC were applied to human keratinocytes in vitro. Some S. aureus secretomes induced keratinocytes to overexpress inflammatory mediators that have been linked to skin carcinogenesis, including IL-6, IL-8, and TNFα. A large phenotypic variation between the tested clinical strains was observed. Strains that are highly pro-inflammatory in vitro also caused more pronounced skin inflammation in mice. Proteomic characterization of S. aureus secretomes using mass spectrometry established that specific S. aureus enzymes and cytolytic toxins, including hemolysins, phenol-soluble modulins, and serine proteases, as well as currently uncharacterized proteins, correlate with the pro-inflammatory S. aureus phenotype. This study is the first to describe the toxin secretion profiles of AK and SCC-associated S. aureus, and their potential to induce a pro-inflammatory environment in the skin. Further studies are needed to establish whether these S. aureus products promote SCC development by mediating chronic inflammation.
| S-EPMC8822148 | biostudies-literature
Project description:ImportanceThe incidence of skin cancer is increasing and evaluation of the utility of total body skin examination (TBSE) in detecting incidental skin cancers is warranted.ObjectivesTo evaluate the proportion and rate of incidental skin cancer detection in urgent skin cancer clinics and investigate the rate of incidental skin cancer detection in 2 groups based on the degree of clinical suspicion of the index lesion for malignancy.Design, setting, and participantsA multicenter retrospective cohort study with a case note review of consecutive secondary care consultations was conducted using data from 2 urgent suspected skin cancer screening clinics in UK National Health Service trusts. The study was performed from January 1, 2015, to March 31, 2016, and data analysis was performed from October 14, 2018, to February 1, 2019. Patients included those presenting with a skin lesion suspicious of malignancy who were referred to the urgent suspected skin cancer clinic (N = 5944) over 15 months. Patients who accepted and received a TBSE were subsequently included in the analysis.Main outcomes and measuresThe proportion and rate of incidental skin cancer detection through TBSE and whether a clinically suspicious (malignant) index lesion was associated with a higher chance of having a malignant incidental lesion.ResultsOf the 5944 patients referred to the clinic, 4726 individuals (79.5%) were evaluated. In the cohort included in the analyses, the median age was 57 years (interquartile range, 39-73 years); 2567 patients (54.3%) were women. A total of 1117 skin cancers were identified; of these, 242 lesions (21.7%) were detected incidentally through TBSE, including 197 of 570 (34.6%) basal cell carcinomas, 16 of 250 (6.4%) squamous cell carcinomas, and 25 of 215 (11.6%) melanomas. The detection rate of incidental malignant lesions was 5.1 lesions per 100 patients examined (5.1%; 95% CI, 4.5%-5.8%). There was a higher detection rate of histologically confirmed incidental malignant lesions in individuals with clinically suspicious index lesions requiring biopsy (10.9%; 95% CI, 9.5%-12.5%) compared with those presenting with clinically benign index lesions (2.0%; 95% CI, 1.6%-2.5%) (P < .001).Conclusions and relevanceThe findings of this study support the use of TBSE for urgent skin cancer referrals, highlighting the potential harms of solitary lesion assessment in a subgroup. Individuals presenting with a clinically suspicious index lesion requiring biopsy are most likely to benefit from TBSE and should be counseled regarding the benefit.
| S-EPMC7745102 | biostudies-literature
Project description:BackgroundSkin cancer is a life-threatening disease, and early detection of skin cancer improves the chances of recovery. Skin cancer detection based on deep learning algorithms has recently grown popular. In this research, a new deep learning-based network model for the multiple skin cancer classification including melanoma, benign keratosis, melanocytic nevi, and basal cell carcinoma is presented. We propose an automatic Multi-class Skin Cancer Detection Network (MSCD-Net) model in this research.MethodsThe study proposes an efficient semantic segmentation deep learning model "DenseUNet" for skin lesion segmentation. The semantic skin lesions are segmented by using the DenseUNet model with a substantially deeper network and fewer trainable parameters. Some of the most relevant features are selected using Binary Dragonfly Algorithm (BDA). SqueezeNet-based classification can be made in the selected features.ResultsThe performance of the proposed model is evaluated using the ISIC 2019 dataset. The DenseNet connections and UNet links are used by the proposed DenseUNet segmentation model, which produces low-level features and provides better segmentation results. The performance results of the proposed MSCD-Net model are superior to previous research in terms of effectiveness and efficiency on the standard ISIC 2019 dataset.
| S-EPMC10495937 | biostudies-literature
Project description:BackgroundOlder adults with bacterial skin infections may present with atypical symptoms, making diagnosis difficult. There is limited authoritative guidance on how older adults in the community present with bacterial skin infections. To date there have been no systematic reviews assessing the diagnostic value of symptoms and signs in identifying bacterial skin infections in older adults in the community.MethodsWe searched Medline and Medline in process, Embase and Web of Science, from inception to September 2017. We included cohort and cross-sectional studies assessing the diagnostic accuracy of symptoms and signs in predicting bacterial skin infections in adults in primary care aged over 65?years. The QUADAS-2 tool was used to assess study quality.ResultsWe identified two observational studies of low-moderate quality, with a total of 7991 participants, providing data to calculate the diagnostic accuracy of 5 unique symptoms in predicting bacterial skin infections. The presence of wounds [LR+: 7.93 (CI 4.81-13.1)], pressure sores [LR+: 4.85 (CI 2.18-10.8)] and skin ulcers [LR+: 6.26 (CI 5.49-7.13)] help to diagnose bacterial skin infections. The presence of urinary incontinence does not help to predict bacterial skin infections (LR?+?'s of 0.99 and 1.04; LR-'s of 0.96 and 1.04).ConclusionsCurrently, there is insufficient evidence to inform the diagnosis of bacterial skin infections in older adults in the community; clinicians should therefore rely upon their clinical judgement and experience. Evidence from high quality primary care studies in older adults, including studies assessing symptoms traditionally associated with bacterial skin infections (e.g. erythema and warmth), is urgently needed to guide practice.
| S-EPMC6380032 | biostudies-literature
Project description: Not available
| S-EPMC10570117 | biostudies-literature