Early Diagnosis Of Melanoma Research Articles

An innovative cellular medicine approach via the utilization of novel nanotechnology-based biomechatronic platforms as a label-free biomarker for early melanoma diagnosis

Innovative cellular medicine (ICM) is an exponentially emerging field with a promising approach to combating complex and ubiquitous life-threatening diseases such as multiple sclerosis (MS), arthritis, Parkinson’s disease, Alzheimer’s, heart disease, and cancer. Together with the advancement of nanotechnology and bio-mechatronics, ICM revolutionizes cellular therapy in understanding the essence and nature of the disease initiated at a single-cell level. This paper focuses on the intricate nature of cancer that requires multi-disciplinary efforts to characterize it well in order to achieve the objectives of modern world contemporary medicine in the early detection of the disease at a cellular level and potentially arrest its proliferation mechanism. This justifies the multidisciplinary research backgrounds of the authors of this paper in advancing cellular medicine by bridging the gap between experimental biology and the engineering field. Thus, in pursuing this approach, two novel miniaturized and highly versatile biomechatronic platforms with dedicated operating software and microelectronics are designed, modeled, nanofabricated, and tested in numerous in vitro experiments to investigate a hypothesis and arrive at a proven theorem in carcinogenesis by interrelating cellular contractile force, membrane potential, and cellular morphology for early detection and characterization of melanoma cancer cells. The novelties that flourished within this work are manifested in sixfold: (1) developing a mathematical model that utilizes a Heaviside step function, as well as a pin-force model to compute the contractile force of a living cell, (2) deriving an expression of cell-membrane potential based on Laplace and Fourier Transform and their Inverse Transform functions by encountering Warburg diffusion impedance factor, (3) nano-fabricating novel biomechatronic platforms with associated microelectronics and customized software that extract cellular physics and mechanics, (4) developing a label-free biomarker, (5) arrive at a proved theorem in developing a mathematical expression in relating cancer cell mechanobiology to its biophysics in connection to the stage of the disease, and (6) to the first time in literature, and to the best of the authors’ knowledge, discriminating different stages and morphology of cancer cell melanoma based on their cell-membrane potentials, and associated contractile forces that could introduce a new venue of cellular therapeutic modalities, preclinical early cancer diagnosis, and a novel approach in immunotherapy drug development. The proposed innovative technology-based versatile bio-mechatronic platforms shall be extended for future studies, investigating the role of electrochemical signaling of the nervous system in cancer formation that will significantly impact modern oncology by pursuing a targeted immunotherapy approach. This work also provides a robust platform for immunotherapy practitioners in extending the study of cellular biophysics in stalling neural-cancer interactions, of which the FDA-approved chimeric antigen receptor (CAR)-T cell therapies can be enhanced (genetically engineered) in a lab by improving its receptors to capture cancer antigens. This work amplifies the importance of studying neurotransmitters and electrochemical signaling molecules in shaping the immune T-cell function and its effectiveness in arresting cancer proliferation rate (mechanobiology mechanism).

426 Development of a novel assistive tool for early diagnosis of malignant melanoma using thermal conductivity measurements

426 Development of a novel assistive tool for early diagnosis of malignant melanoma using thermal conductivity measurements

The method for evaluating the symmetry of the globule pattern in artificial intelligence systems for the diagnosis of skin neoplasms

Methods for early non-invasive diagnosis of melanoma using computer vision systems are considered. Existing computer vision systems using neural networks for classifying dermoscopic images do not allow tracking which diagnostic features are used to assign images to a particular class, reducing physicians' trust in the results. As an alternative, an image analysis algorithm is proposed with the ability to present justifications for decisions made at each processing stage. The implementation of this algorithm is based on the medical algorithm of modified globular pattern analysis. A significant sign of malignancy in a neoplasm is its asymmetry. This criterion is widely used by doctors in visual assessment of skin neoplasms. However, currently, the issues of evaluating the symmetry of globular patterns in artificial intelligence systems are not fully studied and described. A method for evaluating the symmetry of globular patterns in artificial intelligence systems for diagnosing skin neoplasms has been developed. A dataset of dermoscopic images was formed, containing 50 images each of neoplasms with symmetrically and asymmetrically arranged globular patterns. Methods for isolating the neoplasm area and globules are described. A classification system based on a set of 12 quantitative symmetry characteristics has been developed. The Random Forest algorithm was used to classify images based on symmetry features. In the conducted experiment, a classification accuracy of 85% was achieved. The presented results contribute to the development of computer vision methods in dermatology and demonstrate the possibility of using the proposed method in clinical decision support systems for modified analysis of dermoscopic patterns for diagnosing skin neoplasms.

Deep Learning With Optical Coherence Tomography for Melanoma Identification and Risk Prediction.

Malignant melanoma is the most severe skin cancer with a rising incidence rate. Several noninvasive image techniques and computer-aided diagnosis systems have been developed to help find melanoma in its early stages. However, most previous research utilized dermoscopic images to build a diagnosis model, and only a few used prospective datasets. This study develops and evaluates a convolutional neural network (CNN) for melanoma identification and risk prediction using optical coherence tomography (OCT) imaging of mice skin. Longitudinal tests are performed on four animal models: melanoma mice, dysplastic nevus mice, and their respective controls. The CNN classifies melanoma and healthy tissues with high sensitivity (0.99) and specificity (0.98) and also assigns a risk score to each image based on the probability of melanoma presence, which may facilitate early diagnosis and management of melanoma in clinical settings.

An attractor state zone precedes neural crest fate in melanoma initiation.

The field cancerization theory suggests that a group of cells containing oncogenic mutations are predisposed to transformation1, 2. We previously identified single cells in BRAF V600E ;p53 -/- zebrafish that reactivate an embryonic neural crest state before initiating melanoma3-5. Here we show that single cells reactivate the neural crest fate from within large fields of adjacent abnormal melanocytes, which we term the "cancer precursor zone." These cancer precursor zone melanocytes have an aberrant morphology, dysplastic nuclei, and altered gene expression. Using single cell RNA-seq and ATAC-seq, we defined a distinct transcriptional cell attractor state for cancer precursor zones and validated the stage-specific gene expression initiation signatures in human melanoma. We identify the cancer precursor zone driver, ID1, which binds to TCF12 and inhibits downstream targets important for the maintenance of melanocyte morphology and cell cycle control. Examination of patient samples revealed precursor melanocytes expressing ID1, often surrounding invasive melanoma, indicating a role for ID1 in early melanomagenesis. This work reveals a surprising field effect of melanoma initiation in vivo in which tumors arise from within a zone of morphologically distinct, but clinically covert, precursors with altered transcriptional fate. Our studies identify novel targets that could improve early diagnosis and prevention of melanoma.

Hunting for Early Melanomas With a Maximum Clinical Surface Diameter up to 6 mm: A Prospective Study of Reflectance Confocal Microscopy Features in 68 Consecutive Cases.

Early diagnosis of melanoma and prompt effective therapy optimizes prognosis. Reflectance confocal microscopy (RCM) facilitates diagnosis by providing immediate 3D single cell resolution down into the papillary dermis. Consecutive cases were examined using a Vivascope 1500 confocal microscope at a single referral medical practice in Sydney, Australia 2019-2023. Melanoma clinical surface diameters were recorded by 0.1 mm increments up to 6.0 mm. The RCM features recorded were: pagetoid single cells or nests, pleomorphic cell shape, atypical dendritic cells, non-edged papillae, variation in melanocyte size and confluent sheets of cells. All cases required diagnostic agreement by two dermatohistopathologists using hematoxylin and eosin staining followed by SOX 10 and/or PRAME stains if required. Total cases were 68: 38 males (mean age 57) and 30 females (mean age 64). Melanoma in situ (n = 65) compared to invasive melanoma (n = 3), all males, invasion depth (0.4-0.5 mm). Most frequent RCM features found in 50% or more of cases within all diameter increments were: pagetoid single cells n = 64/68 (94%), pleomorphic cell shape n = 63/68 (93%), epidermal disarray n = 58/68 (85%), and atypical dendritic cells n = 45/68 (66%). Non-edged dermal papillae were n = 42/68 (62%). Melanoma RCM features were found throughout the diameter ranges. Confocal examination may facilitate early melanoma recognition in these ranges.

S2785 Beating the Odds: Unusual Early Diagnosis of Primary Rectal Melanoma

S2785 Beating the Odds: Unusual Early Diagnosis of Primary Rectal Melanoma

Pigment network detection and classification in dermoscopic images using directional imaging algorithms and convolutional neural networks

Early diagnosis of melanoma, which can save thousands of lives, relies heavily on the analysis of dermoscopic images. One crucial diagnostic criterion is the identification of unusual pigment network (PN). However, distinguishing between regular (typical) and irregular (atypical) PN is challenging. This study aims to automate the PN detection process using a directional imaging algorithm and classify PN types using machine learning classifiers. The directional imaging algorithm incorporates Principal Component Analysis (PCA), contrast enhancement, filtering, and noise reduction. Applied to the PH2 dataset, this algorithm achieved a 96% success rate, which increased to 100% after pixel intensity adjustments. We created a new dataset containing only PN images from these results. We then employed two classifiers, Convolutional Neural Network (CNN) and Bag of Features (BoF), to categorize PN into atypical and typical classes. Given the limited dataset of 200 images, a simple and effective CNN was designed, featuring two convolutional layers and two batch normalization layers. The proposed CNN achieved 90% accuracy, 90% sensitivity, and 89% specificity. When compared to state-of-the-art methods, our CNN demonstrated superior performance. Our study highlights the potential of the proposed CNN model for effective PN classification, suggesting future research should focus on expanding datasets and incorporating additional dermatological features to further enhance melanoma diagnosis.

Melanoma overdiagnosis: What do we know and what do we do?

Melanoma overdiagnosis occurs when melanomas, not destined to cause morbidity or death in a patient's lifetime, are identified and treated. This study considers the causes and magnitude of melanoma overdiagnosis in Australia. We also speculate about a possible benefit of overdiagnosis in Australia; namely, a reduction in excess deaths in the geographical areas where melanoma is diagnosed most frequently. Overdiagnosis can arguably be mitigated by factors that reduce the number of lesions treated for each melanoma identified. Data from the Australian Cancer Atlas show that there is a reduction in excess deaths from melanoma in geographical areas where diagnostic rates are higher (Pearson correlation coefficient r=-0.5978, 95% CI: -0.6243 to -0.5699, P<0.0001); this being the strongest inverse correlation observed among the 20 cancer types in the Atlas. Is early diagnosis of actual life-threatening melanomas in these geographical regions impacting survival? Further research is planned.

Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis

Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis

Rapidly progressing scalp melanoma in an elderly patient.

A rapidly progressing scalp melanoma incorrectly diagnosed and managed as a 'chronic wound', lead to delayed referral to dermatologist and correct diagnosis. This case highlights the importance of early diagnosis of scalp melanoma and of frailty screening in elderly patients.

Exploring of miR-155-5p, miR-181b-5p, and miR-454-3p Expressions in Circulating Cell-Free RNA: Insights from Peripheral Blood of Uveal Malignant Melanoma Patients.

The identification of novel non-invasive biomarkers is imperative for the early diagnosis and monitoring of malignant melanoma. The objective of this study is to examine the expression levels of miR-155-5p, miR-181b-5p, and miR-454-3p in circulating cell-free RNA obtained from plasma samples of the 72 uveal malignant melanoma patients and to compare these levels with those of 72 healthy controls. The analysis showed that the expression level of the miR-181b-5p has increased 9.25 fold, and expression level of miR-155-5p has increased 6.67 fold, and miR-454-3p expression level has increased 4.14 fold in the patient group compared with the levels in the healthy control group (p = 0.005). It was found that the high expression levels of the three miRNAs were statistically significant in patients compared with in the healthy control group. The statistical evaluations between miRNA expression levels and clinical data showed that miR-155-5p had significant association with radiation therapy (p = 0.040), and miR-454-3p showed a significant association with smoking and alcohol use respectively (p = 0.009, and p = 0.026). The significantly elevated expression levels of miR-181b-5p, miR-155-5p, and miR-454-3p in the circulating cell-free RNA of plasma from uveal melanoma patients, in comparison to those in the healthy control group, suggest the potential usefulness of these biomarkers for both early diagnosis and disease monitoring. However, more extensive and future studies are needed to use these molecules in early diagnosis and disease monitoring.

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.

Early diagnosis of melanoma: a randomized trial assessing the impact of the transmission of photographs taken with a smartphone from the general practitioner to the dermatologist on the time to dermatological consultation

BackgroundDifficulty obtaining a dermatological consultation is an obstacle to the early diagnosis of melanoma. On the one hand, patients survival depends on the lesion thickness at the time of diagnosis. On the other hand, dermatologists treat many patients with benign lesions. Optimizing patient care pathways is a major concern. The aim of the present study was to assess whether the e-mail transmission of photographs of suspected melanoma lesions between general practitioners (GPs) and dermatologists reduces the time to dermatological consultation for patients whose suspicious skin lesions ultimately require resection.MethodsWe conducted a cluster-randomized controlled study in primary care involving 51 French GPs between April 2017 and August 2019. A total of 250 patients referred to a dermatologist for a suspected melanoma lesion were included GPs were randomized to either the smartphone arm or the usual care arm. In the smartphone arm, the GPs referred patients to the dermatologist by sending 2 photographs of the suspicious lesion using their smartphone. The dermatologist then had to set up an appointment at an appropriate time. In the usual care arm, GPs referred patients to a dermatologist according to their usual practice. The primary outcome was the time to dermatological consultation for patients whose lesion ultimately required resection.Results57 GPs volunteered were randomized (27 to the smartphone arm, and 30 to the usual care arm). A total of 125 patients were included in each arm (mean age: 49.8 years; 53% women) and followed 8 months. Twenty-three dermatologists participated in the study. The time to dermatological consultation for patients whose suspicious skin lesion required resection was 56.5 days in the smartphone arm and 63.7 days in the usual care arm (mean adjusted time reduction: -18.5 days, 95% CI [-74.1;23.5], p = .53).ConclusionsThe e-mail transmission of photographs from GPs to dermatologists did not improve the dermatological management of patients whose suspicious skin lesions ultimately required resection. Further research is needed to validate quality criteria that might be useful for tele-expertise in dermatology.Trial RegistrationRegistered on ClinicalTrials.gov under reference number NCT03137511 (May 2, 2017).

Early diagnosis of skin oncologic diseases using artificial intelligence technologies

The last decade has seen significant progress in computer-aided image analysis and recognition, with modern computer-aided diagnostic algorithms not only catching up with, but in many aspects surpassing human abilities. At the heart of this breakthrough is the development of deep convolutional neural networks, which have given a new impetus to medical diagnosis, particularly of skin cancers. In this paper, we analyzed photo-based skin disease classification systems developed using algorithms based on deep learning convolutional neural networks. Such methods have been variously reported to enable automated diagnosis of skin neoplasms with high sensitivity and specificity. A disease that requires more detailed analysis of graphic images — skin melanoma — was chosen as the main object of study. Early diagnosis of melanoma is of great socio-economic importance, as in this case the prognosis of patients is significantly improved. The aim of this work is to analyze the results of artificial intelligence (AI) applications in dermatology, especially in the context of early detection of skin melanoma. Scientific articles were searched in PubMed, Scopus and eLIBRARY databases using the keywords “convolutional neural networks”, “skin cancer” and “artificial intelligence”. The depth of the search was 10 years. The final analysis included 38 sources where the results of our own research were presented. The advantages of artificial intelligence methods for dermatologists were analyzed. Artificial intelligence can significantly assist dermatologists in developing visual neoplasm diagnosis skills and improve diagnostic accuracy. The use of AI to process dermatoscopic data in conjunction with the analysis of anamnestic and clinical information from medical records will reduce the burden on the healthcare system through correctly diagnosed benign skin tumors. All of this promises to have a significant impact on the future development of dermatovenerology.

Persistent poverty and incidence-based melanoma mortality in Texas.

Previous studies have shown that individuals living in areas with persistent poverty (PP) experience worse cancer outcomes compared to those living in areas with transient or no persistent poverty (nPP). The association between PP and melanoma outcomes remains unexplored. We hypothesized that melanoma patients living in PP counties (defined as counties with ≥ 20% of residents living at or below the federal poverty level for the past two decennial censuses) would exhibit higher rates of incidence-based melanoma mortality (IMM). We used Texas Cancer Registry data to identify the patients diagnosed with invasive melanoma or melanoma in situ (stages 0 through 4) between 2000 and 2018 (n = 82,458). Each patient's PP status was determined by their county of residence at the time of diagnosis. After adjusting for demographic variables, logistic regression analyses revealed that melanoma patients in PP counties had statistically significant higher IMM compared to those in nPP counties (17.4% versus 11.3%) with an adjusted odds ratio of 1.35 (95% CI 1.25-1.47). These findings highlight the relationship between persistent poverty and incidence-based melanoma mortality rates, revealing that melanoma patients residing in counties with persistent poverty have higher melanoma-specific mortality compared to those residing in counties with transient or no poverty. This study further emphasizes the importance of considering area-specific socioeconomic characteristics when implementing place-based interventions to facilitate early melanoma diagnosis and improve melanoma treatment outcomes.

A novel NIR fluorescent probe inhibits melanoma progression through apoptosis and ERK/DRP1-mediated mitochondrial fission

Melanoma, a highly metastatic malignant tumour, necessitated early detection and intervention. This study focuses on a hemicyanine fluorescent probe activated by near-infrared (NIR) light for bioimaging and targeted mitochondrial action in melanoma cells. IR-418, our newly designed hemicyanine-based NIR fluorescent probe, demonstrated effective targeting of melanoma cell mitochondria for NIR imaging. In vitro and in vivo experiments revealed IR-418′s inhibition of melanoma growth through the promotion of mitochondrial apoptosis (Bax/Bcl-2/Cleaved Caspase pathway). Moreover, IR-418 inhibited melanoma metastasis by inhibiting mitochondrial fission through the ERK/DRP1 pathway. Notably, IR-418 mitigated abnormal ATL and ASL elevations caused by tumours without inflicting significant organ damage, indicating its high biocompatibility. In conclusion, IR-418, a novel hemicyanine-based NIR fluorescent probe targeting the mitochondria, exhibits significant fluorescence imaging capability, anti-melanoma proliferation, anti-melanoma lung metastasis activities and high biosafety. Therefore, it has significant potential in the early diagnosis and treatment of melanoma.

Evaluation of a training course for general practitioners within the melanoma multimedia education programme of the Italian Melanoma Intergroup: a study protocol.

The text discusses the role of general practitioners (GPs) in the prevention and early diagnosis of melanoma, a type of skin cancer. It highlights the need for GPs to be able to recognize suspicious skin lesions and refer patients to specialist dermatology centers. However, many GPs lack comprehensive training in diagnosing melanoma. The text mentions that various training courses have been conducted for GPs, but their impact on clinical practice has been limited. The MelaMEd Programme is an e-learning course developed by the Italian Melanoma Intergroup (IMI). The programme aims to provide GPs with comprehensive knowledge of melanoma prevention, diagnosis, and treatment. It includes an e-learning section, and a dedicated platform called MelaMEd platform, which offers a multimedia atlas of melanoma. The objective of the study is to evaluate the impact of the MelaMEd programme on GPs' diagnostic accuracy, knowledge of melanoma, and management of suspicious lesions. The methodology involves administering pre-training and post-training questionnaires to participants, assessing their diagnostic skills and evaluating the training course's effectiveness. The study aims to demonstrate the effectiveness of the MelaMEd programme in improving GPs' ability to recognize and manage melanoma. It also seeks to identify areas for improvement and recommend interventions to enhance diagnostic accuracy. The results will be analyzed statistically using descriptive, univariate, and multivariate analysed methods.

Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study.

Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge. To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management. In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM. We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample. These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.

Exploring Relationships in Melanocytic Data Sets

Early diagnosis of malignant melanoma can cure up to 90% of patients. Thus, our goal is to improve a specialized, multi-strategic diagnostic system supporting dermatologists and, in the long run, general practitioners, ensuring objective, reliable, and user-independent results. This research was concerned with extending the idea of Micro-AI models, deep learning detectors selected for each feature instead of a single AI model used for the entire image. Two types of relationships were defined for the melanocytic strategies used: impact on diagnosis (direct, indirect, selective), as well as interaction between features. Only strong causal relationships, with a probability above 0.9, between the features of the lesion were identified. The indicated features with significant dependencies, such as the border of the change and the diversity of its structure, will be included in the deep learning detector to achieve better classification/detection.