Early Detection Of Melanoma Research Articles

Classification Optimization of Skin Cancer Using the Adaboost Algorithm

Early detection of melanoma skin cancer is crucial in improving prognosis and saving lives. This research aimed to optimize the classification of melanoma images using the Adaboost algorithm. Employing a dataset of 10,000 melanoma images, the study combined the Canny method for image segmentation, Hu Moments for feature extraction, and the Adaboost algorithm for classification. The 5-fold cross-validation results revealed an average accuracy of 61.52%. While the precision consistently surpassed recall, indicating the model's conservative nature in predicting positive cases. The outcomes align with previous research, emphasizing the challenges in melanoma classification. This study contributes to the domain by showcasing the potential and areas of improvement for machine learning in early melanoma detection. Future research is recommended to explore hybrid models and diversify data sources for enhanced robustness and generalizability.

Melanoma Skin Cancer Classification based on CNN Deep Learning Algorithms

Melanoma, the deadliest form of skin cancer, is on the rise. The goal of this study is to present a deep learning system implementation for the detection of melanoma lesions on a server equipped with a graphics processing unit (GPU). When applied by a dermatologist, the recommended method might aid in the early detection of this kind of skin cancer. Evidence shows that deep learning may be used in a variety of settings to successfully extract patterns from data such as signals and images. This research presents a convolution neural network–based strategy for identifying early-stage melanoma skin cancer. Images are input into a deep learning model known as a convolutional neural network (CNN) that has already been pre-trained. The CNN classifier, which is trained with large amounts of data, can discriminate between malignant and nonmalignant melanoma. The method's success in the lab bodes well for its potential to aid dermatologists in the early detection of melanoma. However, the experimental results show that the proposed technique excels beyond the state-of-the-art methods in terms of diagnostic accuracy.

The Role of Biomarkers in the Diagnosis and Prognosis of Different Stages of Melanoma.

Melanomais a skin cancer arising from melanocytes, the cells responsible for synthesizing melanin pigment, which gives the skin its color. Early diagnosis and treatment of melanoma increase survival rates. Clinical examination and biopsy are the primary tools used to diagnose melanoma. However, distinguishing between pre-malignant melanocytic lesions and early invasive melanoma histopathologically remains challenging. Therefore, additional modalities such as a detailed clinical history, imaging, genetic testing, and biomarkers have been applied to diagnose melanoma. This review discusses the current trends in biomarker advancements over the last 10 years to assist in the early detection and diagnosis of melanoma. Biomarkers such as melanoma-associated antigens (MAAs), S100B, microRNAs (miRNAs), and circulating tumor cells (CTCs) have the potential to aid in the detection, diagnosis, and prognosis of melanoma. However, the application of biomarkers in the diagnosis of melanoma is still evolving.

Transcriptome Analysis Identifies Oncogenic Tissue Remodeling during Progression from Common Nevi to Early Melanoma

Early detection and treatment of melanoma, the most aggressive skin cancer, improves the median 5-year survival rate of patients from 25% to 99%. Melanoma development involves a stepwise process during which genetic changes drive histological alterations within nevi and surrounding tissue. Here, we perform a comprehensive analysis of publicly available gene expression datasets of melanoma, common or congenital nevi (CN), and dysplastic nevi (DN), and assess molecular & genetic pathways leading to early melanoma. Our results demonstrate several pathways reflective of ongoing local structural tissue remodeling activity likely involved during the transition from benign to early-stage melanoma. These processes include the gene expression of cancer associated fibroblasts (CAF), collagens, extracellular matrix (ECM) and integrins, which assist early melanoma development and the immune surveillance which plays a substantial role at this early stage. Further, we show genes upregulated in dysplastic nevi (DN) are also overexpressed in melanoma tissue, supporting the notion DN may serve as a transitional phase towards oncogenesis. Lastly, we reveal CN collected from healthy individuals exhibit different gene signatures compared to histologically benign nevi tissue located adjacent to melanoma (adjacent nevi, AN). We show the expression profile of micro-dissected AN tissue is more similar to melanoma compared to CN, revealing the melanoma influence on this annexed tissue.

Früherkennung eines Aderhautmelanoms – ein Fallbericht

Purpose. This case report describes the accidental early detection of a small choroidal melanoma during a routine optometric examination. The following is a report of a rare case of choroidal melanoma, in a 58-year-old male Caucasian. He wanted to have glasses for computer work and complained of occasional flickering, when seeing in the morning after getting up. Material and Methods. The following optometric measure- ment methods were used: confrontation visual field test, ex- traocular muscle function test, cover test, retinoscopy (Heine Beta 200 Retinoscope), refraction, slit lamp examination (slit lamp Zeiss SL 120) with evaluation of the anterior eye, evalua- tion of the posterior eye (Heine Beta 200 Ophthalmoscope) and handheld fundus camera (Zeiss Visuscout 100). Results. On fundus examination of the left eye, a light pigmented retinal lesion with orange pigment and retinal eleva- tion was seen temporally on the upper vascular arch. Due to the suspected diagnosis of choroidal melanoma, immediate contact was made with a local ophthalmology practice. This confirmed the suspicion and the patient was referred to the University Eye Clinic in Munich. Conclusion. Uveal melanoma is the most common intraocular malignancy and arises from melanocytes in the iris, ciliary body or choroid. Early diagnosis and local treatment are crucial, as survival correlates with primary tumor size. Approx- imately 50 percent of patients will develop metastatic disease with survival of 6-12 months after metastatic diagnosis. The University Eye Clinic Munich confirmed the diagnosis of choroidal melanoma. A robotic Cyberknife radiosurgery was performed. Keywords Uveal Melanoma, Choroidal Melanoma, Cyberknife Radio-surgery

A Systematic Review of Evidence-Based High School Melanoma Prevention Curricula.

Incorporation of melanoma prevention behaviors into daily lifestyles is difficult. Data suggest that high school educational programs on skin cancer prevention can be successful and should incorporate evidence-based teaching and learning strategies to achieve greatest impact. The goal of this systematic review is to describe evidence-based educational practices for a high-school melanoma curriculum through a comprehensive review of the literature. Ovid MEDLINE, Embase, CINAHL, and PyscINFO were searched in June 2020 for all original articles published between June 18, 1946 and June 17, 2020. All studies that used an educational curriculum to promote sun safety, skin exams, and early detection to high school students were included. A total of 25 studies with 22,683 adolescent participants were analyzed. Sixteen studies showed a significant increase in knowledge, twenty-one studies showed changes in behavior, and fifteen studies showed significant changes in attitudes. Limitations of this review include the heterogeneity of implementation and outcome reporting of educational curricula. These findings support incorporating active learning strategies as key aspects of creating an effective curriculum aimed at the prevention and early detection of melanoma.

Advances in Early Detection of Melanoma and the Future of At-Home Testing.

The past decade has seen numerous advancements in approaches to melanoma detection, each with the common goal to stem the growing incidence of melanoma and its mortality rate. These advancements, while well documented to increase early melanoma detection, have also garnered considerable criticism of their efficacy for improving survival rates. In this review, we discuss the current state of such early detection approaches that do not require direct dermatologist intervention. Our findings suggest that a number of at-home and non-specialist methods exist with high accuracy for detecting melanoma, albeit with a few notable concerns worth further investigation. Additionally, research continues to find new approaches using artificial intelligence which have promise for the future.

Dermoscopic features associated with 3-GEP PLA: LINC00518, PRAME, and TERT expression in suspicious pigmented lesions.

Utilization of dermoscopy and novel molecular triage technologies augments visual triage of pigmented skin lesions, promoting early detection of melanoma. One emerging in vivo genomic test, 3-GEP pigmented lesion assay (3-GEP PLA) aids in pigmented lesion triage by noninvasively detecting the presence of three genes associated with melanoma: LINC00518, PRAME, and TERT. The purpose of our retrospective case-control study was to identify dermoscopic features uniquely associated with the presence of LINC00518, PRAME, or TERT in the stratum corneum as determined by 3-GEP PLA testing. Images of suspicious pigmented lesions that had undergone 3-GEP PLA testing and received a definitive positive or negative result (n = 393) were evaluated for the presence of specific clinical and dermoscopic features associated with melanoma. We found that asymmetry of color was a significant predictor for PRAME expression (Odds Ratio (OR) 5.5, 95% Confidence Interval (CI) 1.6-34.5, p = 0.004), blue color and negative pigment network were significant predictors for LINC00518 expression (adjusted OR 2.7, 95% CI 1.2-5.5, p = 0.014 and adjusted OR 5.4, 95% CI 1.6-16.9, p = 0.010, respectively), and atypical polymorphous vessels present in a pigmented skin lesion were a significant predictor for TERT promoter mutations (OR 5.8, 95% CI 1.3-23.4, p = 0.022). The results presented suggest a hierarchy in the significance of these dermoscopic features and may help guide evaluation and management of pigmented skin lesions.

Study protocol for a randomised controlled trial to evaluate the use of melanoma surveillance photography to the Improve early detection of MelanomA in ultra-hiGh and high-risk patiEnts (the IMAGE trial)

IntroductionMelanoma surveillance photography (MSP) is a comprehensive surveillance method that comprises two- or three-dimensional total body photography with tagged digital dermoscopy, performed at prescribed intervals. It has the potential to reduce unnecessary biopsies and enhance early detection of melanoma, but it is not yet standard care for all high-risk patients in Australia. This protocol describes a randomised controlled trial (RCT) designed to evaluate the clinical impact and cost-effectiveness of using MSP for the surveillance of individuals at ultra-high or high risk of melanoma from a health system perspective.Methods and designThis is a registry-based, unblinded, multi-site, parallel-arm RCT that will be conducted over 3 years. We aim to recruit 580 participants from three Australian states: Victoria, New South Wales and Queensland, via state cancer registries or direct referral from clinicians. Eligible participants within 24 months of a primary cutaneous melanoma diagnosis will be randomised 1:1 to receive either MSP in addition to their routine clinical surveillance (intervention group) or routine clinical surveillance without MSP (control group). Most participants will continue surveillance with their usual care provider, and the frequency of follow-up visits in both groups will depend on the stage of their primary melanoma and risk factors. The primary outcome measure of the study is the number of unnecessary biopsies (i.e. false positives, being cases where a lesion is biopsied due to suspected melanoma on clinical examination, either with or without MSP, but the resulting histopathology finding is negative for melanoma). Secondary outcomes include the evaluation of health economic outcomes, quality of life and patient acceptability. Two sub-studies will explore the benefit of MSP in high-risk patients prior to a melanoma diagnosis and the diagnostic performance of MSP in the teledermatology setting compared to the en face clinical setting.DiscussionThis trial will determine the clinical efficacy, cost-effectiveness and affordability of MSP to facilitate policy decision-making at the national and local levels, across primary and specialist care.Trial registrationClinicalTrials.gov NCT04385732. Registered on May 13, 2020.

Routine imaging guided by a 31-gene expression profile assay results in earlier detection of melanoma with decreased metastatic tumor burden compared to patients without surveillance imaging studies

Patients with early-stage disease typically have a good prognosis, but still have a risk of recurrence, even with negative sentinel lymph node biopsy (SLNB). This study explores the utility of routine imaging to detect metastases in patients with negative SLNB but high-risk 31 gene expression profile (31-GEP) scores. We retrospectively identified melanoma patients with negative SLNBs. Patients with high-risk GEP results were placed in the experimental group and patients without GEP testing were placed in the control group. Among both cohorts, recurrent melanoma groups were identified. The tumor burden at the time of recurrence and the time to recurrence were compared between experimental group patients with routine imaging and control group patients without imaging schedules. We identified 327 control patients and 307 experimental patients, of which 14.1% versus 20.5% had melanoma recurrence, respectively. Of the patients with recurrent melanoma, those in the experimental group were older (65.75 versus 59.20), had higher Breslow depths (3.72 mm versus 3.31 mm), and had advanced tumor staging (89.5% versus 71.4% of patients presenting clinical stage ≥ II) compared to the control group at primary diagnosis. However, melanoma recurrence was detected earlier (25.50 months versus 35.35 months) in the experimental group at a lower overall tumor burden (73.10 mm versus 27.60 mm). A higher percentage of experimental patients started immunotherapy when offered (76.3% and 67.9%). Patients who received routine imaging after high-risk GEP test scores had an earlier recurrence diagnosis with lower tumor burden, leading to better clinical outcomes.

A Comparative Study of Melanoma Images Using CNN And Resnet 50

Melanoma is a specific type of skin cancer that can be lethal if not diagnosed and treated early. This paper presents a deep-learning approach for the automatic identification of melanoma on dermoscopic images from the ISIC Archive dataset and non-dermoscopic images from the MED-NODE dataset. The method involves the development of Convolutional Neural Network (CNN) and ResNet50 models, along with various pre-processing techniques. The CNN and ResNet50 models detect melanoma from dermoscopic images with 98.07% and 99.83% accuracy respectively, using hair removal and augmentation techniques. For non-dermoscopic images, the CNN and ResNet50 models achieve an accuracy of 97.06% and 100% respectively, using the hair removal technique. Furthermore, combining age and gender as additional factors in identifying melanoma in dermoscopic images, leads to an accuracy of 96.40% using CNN. The results of this research suggest that the developed models when combined with various pre-processing techniques and the integration of age and gender as additional factors, can be an efficient tool in the early detection of melanoma.

A novel hybrid Extreme Learning Machine and Teaching–Learning-Based​ Optimization algorithm for skin cancer detection

Skin cancers, such as melanoma, can be difficult to spot in their early stages because they often resemble benign moles. Early detection of melanoma is crucial as it increases the chances of successful treatment and prevents cancer from spreading to other areas of the body. Machine learning algorithms and computer vision techniques are versatile techniques for detecting melanoma. However, current research has limitations, such as inaccurate detection and longer computation times. This paper proposes a novel hybrid Extreme Learning Machine (ELM) and Teaching–Learning-Based Optimization (TLBO) algorithm as a versatile technique for detecting melanoma. ELM is a single-hidden layer feed-forward neural network that can be trained quickly and accurately, while TLBO is an optimization algorithm used to fine-tune the network’s parameters for improved performance. Together, these techniques can classify skin lesions as benign or malignant images, potentially improving melanoma detection accuracy.

MicroRNA as a Diagnostic Tool, Therapeutic Target and Potential Biomarker in Cutaneous Malignant Melanoma Detection-Narrative Review.

Melanoma is the most serious type of skin cancer, causing a large majority of deaths but accounting for only ~1% of all skin cancer cases. The worldwide incidence of malignant melanoma is increasing, causing a serious socio-economic problem. Melanoma is diagnosed mainly in young and middle-aged people, which distinguishes it from other solid tumors detected mainly in mature people. The early detection of cutaneous malignant melanoma (CMM) remains a priority and it is a key factor limiting mortality. Doctors and scientists around the world want to improve the quality of diagnosis and treatment, and are constantly looking for new, promising opportunities, including the use of microRNAs (miRNAs), to fight melanoma cancer. This article reviews miRNA as a potential biomarker and diagnostics tool as a therapeutic drugs in CMM treatment. We also present a review of the current clinical trials being carried out worldwide, in which miRNAs are a target for melanoma treatment.

Methods of Using Artificial Intelligence to Detect the Boundaries of Melanoma

Melanoma is the sixth leading cause of death in the United States, with more than nine thousand people succumbing to the disease each year. The earlier melanoma is detected and treated, the longer a person can expect to live after being diagnosed with the disease more analytical developments are still necessary. The skin injury limit anomaly, which addresses the "B"component included in the "ABCD rule,"is regarded as an essential clinical component for the early detection of melanoma. In addition, the blue-white line structure evacuation is another method that helps further strengthen the capability of recognition. In this study, we provide an AI-based localization approach for recognizing skin disease boundary discrepancies. The system was developed for use in dermatological research. The process entails removing the skin disease from the dermoscopic images, identifying the skin lesion, estimating line inconsistency, preparing learning models known as SVM, RF, DT, and KNN gathering move learning to distinguish line anomaly naturally, which ultimately leads to a decision as to whether or not the skin lesion boundary is predictable or not. The approach is very favorable outcomes, with an accuracy rate of 93 percent, a sensitivity rate of 91.6 percent, a specificity rate of 92.8 percent, and an F-score of 95.4 percent, respectively.

The rise in thick melanomas: can early detection reduce the burden?

We sought to describe trends in the incidence of invasive melanoma by histological subtype and thickness in two populations with different approaches to the early detection of melanoma (the US White population and Queensland, Australia). The incidence of thick melanoma (> 4.00 mm) increased over the period 1999–2018 at a faster rate than thinner melanomas in both populations, and the increase was driven primarily by increases in melanoma of the nodular subtype. Thin melanomas (< 1.00 mm) were overwhelmingly of the superficial spreading subtype, whereas thick melanomas (> 4.00 mm) were dominated by nodular melanomas.

Early Malignant Melanoma Detection, Especially in Persons with Pigmented Skin

Early Malignant Melanoma Detection, Especially in Persons with Pigmented Skin

Early Malignant Melanoma Detection, Especially in Persons with Pigmented Skin.

To present a comprehensive gap analysis of podiatric melanoma literature. This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care. After participating in this educational activity, the participant will:1. Select the appropriate assessment techniques for screening patients, especially those with skin of color, for melanoma.2. Compare and contrast the various types of melanoma.3. Discuss the results of the literature review that offer insight to clinicians screening patients for melanoma.

Consumer views of melanoma early detection using 3D total-body photography: cross-sectional survey.

Three-dimensional (3D) total-body photography is a recent advance in melanoma early detection that can aid in monitoring and identifying new and changing skin lesions over time. A cross-sectional survey of adults living in metropolitan and rural areas of Australia was conducted to assess perceptions of 3D total-body photography. Participants completed a survey detailing their previous use of skin cancer photography, personal skin checking history, perceptions of 3D total-body photography, and willingness to pay. Participants were also asked to describe the potential barriers and facilitators of 3D total-body photography in their own words. A total of 1056 participants completed the survey, with 739 (70%) from metropolitan areas of Australia and 317 (30%) from rural areas. Most participants (95%, n=1004/1056) indicated they would consider using 3D total-body photography if it became commercially available at their regular medical practice. Most participants indicated 3D total-body photography would be effective to identify suspicious skin spots (94%, n=995/1056), monitor lesion changes (94%, n=997/1056), and reduce skin cancer related anxiety (90%, n=950/1055). In open-ended feedback, participants (87%, n=918/1056) identified perceived benefits, including more comprehensive screenings, earlier detection, and less human error. Participants (84%, n=889/1056) also identified potential barriers to 3D total-body photography, including cost, accessibility and availability, trust in the technology, and digital security concerns. Participant feedback indicated a high level of acceptance and confidence in the technology. To facilitate clinical translation, addressing consumer-identified barriers to 3D total-body photography will be vital.

Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data

Melanoma is a deadly malignant skin cancer that generally grows and spreads rapidly. Early detection of melanoma can improve the prognosis of a patient. However, large-scale screening for melanoma is arduous due to human error and the unavailability of trained experts. Accurate automatic melanoma classification from dermoscopy images can help mitigate such issues. However, the classification task is challenging due to class-imbalance, high inter-class, and low intra-class similarity problems. It results in poor sensitivity scores when it comes to the disease classification task. The work proposes a novel knowledge-distilled lightweight Deep-CNN-based framework for melanoma classification to tackle the high inter-class and low intra-class similarity problems. To handle the high class-imbalance problem, the work proposes using Cost-Sensitive Learning with Focal Loss, to achieve better sensitivity scores. As a pre-processing step, an in-painting algorithm is used to remove artifacts from dermoscopy images. New CutOut variants, namely, Sprinkled and microscopic Cutout augmentations, have been employed as regularizers to avoid over-fitting. The robustness of the model has been studied through stratified K-fold cross-validation. Ablation studies with test time augmentation (TTA) and the addition of various noises like salt & pepper, pepper-only, and Gaussian noises have been studied. All the models trained in the work have been evaluated on the SIIM-ISIC Melanoma Classification Challenge - ISIC-2020 dataset. With our EfficientNet-B5 (FL) teacher model, the EfficientNet-B2 student model achieved an Area under the Curve (AUC) of 0.9295, and a sensitivity of 0.8087 on the ISIC-2020 test data. The sensitivity value of 0.8087 for melanoma classification is the current state-of-the-art result in the literature for the ISIC-2020 dataset which is a significant 49.48% increase from the best non-distilled standalone model, EfficientNet B5 (FL) teacher with 0.5410.

Evaluation of dynamic dermoscopic features of melanoma and benign naevi by sequential digital dermoscopic imaging and total body photography in a high-risk Australian cohort.

Sequential digital dermoscopic imaging (SDDI) and total body photography (TBP) are recommended as a two-step surveillance method for individuals at high-risk of developing cutaneous melanoma. Dermoscopic features specific to melanoma have been well described, however, dynamic changes on serial imaging are less understood. This study aims to identify and compare dermoscopic features in developing melanomas and benign naevi that underwent SDDI and TBP to understand which dermoscopic features may be associated with a malignant change. Histopathology reports from a private specialist dermatology clinic from January 2007 to December 2019 were reviewed. Histopathologically confirmed melanoma and benign naevi that underwent SDDI and TBP with a minimum follow-up interval of 3 months were included. Eighty-nine melanomas (38.2% invasive, median Breslow thickness 0.35 mm, range: 0.2-1.45 mm) and 48 benign naevi were evaluated by three experienced dermatologists for dermoscopic changes. Features most strongly associated with melanoma included the development of neovascularisation, asymmetry and growth in pigment network, additional colours, shiny white structures, regression, structureless areas and change to a multi-component pattern. The presence of atypical vessels (p= 0.02) and shiny white structures (p= 0.02) were significantly associated with invasive melanoma. Evaluation for certain evolving dermoscopic features in melanocytic lesions monitored by SDDI and TBP is efficient in assisting clinical decision making. SDDI with TBP is an effective tool for early detection of melanoma.