Melanoma is an aggressive disease with rapid progression and fast relapse, representing one of the formidable challenges in clinic. Current systemic therapies for melanoma exhibit limited anticancer ...potential due to the lack of specificity and limited efficacy. Herein, we design a cationic polymer (SCP-HA-PAE) by conjugating skin/cell penetrating peptide (SCP) and hyaluronic acid (HA) to the amphipathic polymer (poly β-amino esters, PAE), then fabricate the nanocarriers (SHP) composed by SCP-HA-PAE for delivering siRNA to skin melanoma by transdermal application. SHP not only manifests the excellent ability in penetrating through skin stratum corneum (SC), targeting melanoma and being sensitive to pH, but also expresses the advantages in compacting the vector/siRNAs nanocomplexes and stimulating their endosome escape inside cells, which ensure the enhanced siRNA delivery efficiency. SHP/siRNA induce the strong efficacy in retarding the progression and relapse of skin melanoma through the enhanced apoptosis effect both in vitro & in vivo. This study provides a proof-of-concept design of pH-switchable cationic micelles as transdermal gene delivery nanoplatforms with targeting effect for melanoma therapy, which may be adapted widely in the treatment of various superficial tumors and skin genetic diseases.
Schematic illustration of the design and therapeutic strategy of SHP. Part I: Synthesis of PAE and preparation of SHP micelle from the PAE, HA and SCP. Part II: Topical application of SHP/SiRNA inducessurvivin slicing in skin melanoma. (1) SHP/SiRNA nanocomplexes penetrate through the skin stratum corneum and targetto melanoma locates at the interface of epidermis and dermis. (2) SHP/SiRNA-survivin nanocomplexes areuptaken by melanoma cells. (3) SHP/SiRNA-survivin nanocomplexes escaped from the lysosome, release the siRNA that bind to the targeting RNA, followed by slicing survivin, which possesses the great potential to induce the significant apoptosis to melanoma cells in vitro and retard the melanoma progression in vivo. Display omitted
•We propose a deep learning model called MFSNet (Multi-Focus Segmentation Network).•MFSNet uses differently scaled feature maps for skin lesion segmentation.•It has Boundary Attention, Reverse ...Attention and Parallel Partial Decoder modules.•It uses Res2Net as backbone which is a recently proposed CNN model.•It outperforms past methods when evaluated on PH2, ISIC2017 and HAM10000 datasets.
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Segmentation is essential for medical image analysis to identify and localize diseases, monitor morphological changes, and extract discriminative features for further diagnosis. Skin cancer is one of the most common types of cancer globally, and its early diagnosis is pivotal for the complete elimination of malignant tumors from the body. This research develops an Artificial Intelligence (AI) framework for supervised skin lesion segmentation employing the deep learning approach. The proposed framework, called MFSNet (Multi-Focus Segmentation Network), uses differently scaled feature maps for computing the final segmentation mask using raw input RGB images of skin lesions. In doing so, initially, the images are preprocessed to remove unwanted artifacts and noises. The MFSNet employs the Res2Net backbone, a recently proposed convolutional neural network (CNN), for obtaining deep features used in a Parallel Partial Decoder (PPD) module to get a global map of the segmentation mask. In different stages of the network, convolution features and multi-scale maps are used in two boundary attention (BA) modules and two reverse attention (RA) modules to generate the final segmentation output. MFSNet, when evaluated on three publicly available datasets: PH2, ISIC 2017, and HAM10000, outperforms state-of-the-art methods, justifying the reliability of the framework. The relevant codes for the proposed approach are accessible at https://github.com/Rohit-Kundu/MFSNet.
Background
Melanoma, a frequently encountered cutaneous malignancy characterized by a poor prognosis, persists in presenting formidable challenges despite the advancement in molecularly targeted ...drugs designed to improve survival rates significantly. Unfortunately, as more therapeutic choices have developed over time, the gradual emergence of drug resistance has become a notable impediment to the effectiveness of these therapeutic interventions. The hepatocyte growth factor (HGF)/c‐met signaling pathway has attracted considerable attention, associated with drug resistance stemming from multiple potential mutations within the c‐met gene. The activation of the HGF/c‐met pathway operates in an autocrine manner in melanoma. Notably, a key player in the regulatory orchestration of HGF/c‐met activation is the long non‐coding RNA MEG3.
Methods
Melanoma tissues were collected to measure MEG3 expression. In vitro validation was performed on MEG3 to prove its oncogenic roles. Bioinformatic analyses were conducted on the TCGA database to build the MEG3‐related score. The immune characteristics and mutation features of the MEG3‐related score were explored.
Results
We revealed a negative correlation between HGF and MEG3. In melanoma cells, HGF inhibited MEG3 expression by augmenting the methylation of the MEG3 promoter. Significantly, MEG3 exhibits a suppressive impact on the proliferation and migration of melanoma cells, concurrently inhibiting c‐met expression. Moreover, a predictive model centered around MEG3 demonstrates notable efficacy in forecasting critical prognostic indicators, immunological profiles, and mutation statuses among melanoma patients.
Conclusions
The present study highlights the potential of MEG3 as a pivotal regulator of c‐met, establishing it as a promising candidate for targeted drug development in the ongoing pursuit of effective therapeutic interventions.
The Cancer Genome Atlas database and the special data were searched, presuming a relationship between hepatocyte growth factor (HGF) and MEG3. We confirmed the assumption by several experiments and further uncovered the molecular mechanism among HGF, MEG3 and mesenchymal–epithelial transition factor. MEG3 has been identified as a crucial biological marker in melanoma. Using a MEG3‐related model, the prognosis, immunological traits and mutation status of melanoma patients were accurately predicted.
Abstract Background In Europe skin melanoma (SM) survival has increased over time. The aims were to evaluate recent trends and differences between countries and regions of Europe. Methods Relative ...survival (RS) estimates and geographical comparisons were based on 241,485 patients aged 15 years and over with a diagnosis of invasive SM in Europe (2000–2007). Survival time trends during 1999–2007 were estimated using the period approach, for 213,101 patients. Age, gender, sub-sites and morphology subgroups were considered. Results In European patients, estimated 5-year RS was 83% (95% confidence interval, CI 83–84%). The highest values were found for patients resident in Northern (88%; 87–88%) and Central (88%; 87–88%) Europe, followed by Ireland and United Kingdom (UK) (86%; 85–86%) and Southern Europe (83%; 82–83%). The lowest survival was in Eastern Europe (74%; 74–75%). Within regions the intercountry absolute difference in percentage points of RS varied from 4% (North) to 34% (East). RS decreased markedly with patients’ age and was higher in women than men. Differences according to SM morphology and skin sub-sites also emerged. Survival has slightly increased from 1999 to 2007, with a small improvement in Northern and the most pronounced improvement in Eastern Europe. Discussion SM survival is high and still increasing in European patients. The gap between Northern and Southern and especially Eastern European countries, although still present, diminished over time. Differences in stage distribution at diagnosis may explain most of the geographical differences. However, part of the improvement in survival may be attributed to overdiagnosis from early diagnosis practices.
Objective
To create a high‐quality electronic health record (EHR)–derived mortality dataset for retrospective and prospective real‐world evidence generation.
Data Sources/Study Setting
Oncology EHR ...data, supplemented with external commercial and US Social Security Death Index data, benchmarked to the National Death Index (NDI).
Study Design
We developed a recent, linkable, high‐quality mortality variable amalgamated from multiple data sources to supplement EHR data, benchmarked against the highest completeness U.S. mortality data, the NDI. Data quality of the mortality variable version 2.0 is reported here.
Principal Findings
For advanced non‐small‐cell lung cancer, sensitivity of mortality information improved from 66 percent in EHR structured data to 91 percent in the composite dataset, with high date agreement compared to the NDI. For advanced melanoma, metastatic colorectal cancer, and metastatic breast cancer, sensitivity of the final variable was 85 to 88 percent. Kaplan–Meier survival analyses showed that improving mortality data completeness minimized overestimation of survival relative to NDI‐based estimates.
Conclusions
For EHR‐derived data to yield reliable real‐world evidence, it needs to be of known and sufficiently high quality. Considering the impact of mortality data completeness on survival endpoints, we highlight the importance of data quality assessment and advocate benchmarking to the NDI.
Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the ...riskiest is melanoma, although it starts in a few different ways. The patient is extremely unaware of recognizing skin malignant growth at the initial stage. Literature is evident that various handcrafted and automatic deep learning features are employed to diagnose skin cancer using the traditional machine and deep learning techniques. The current research presents a comparison of skin cancer diagnosis techniques using handcrafted and non‐handcrafted features. Additionally, clinical features such as Menzies method, seven‐point detection, asymmetry, border color and diameter, visual textures (GRC), local binary patterns, Gabor filters, random fields of Markov, fractal dimension, and an oriental histography are also explored in the process of skin cancer detection. Several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity, are compared on benchmark data sets to assess reported techniques. Finally, publicly available skin cancer data sets are described and the remaining issues are highlighted.
Computer vision‐based traditional and deep learning techniques for skin cancer diagnosis are explored with handcrafted and non‐handcrafted features, respectively. Results are compared on several benchmark data sets.
Melanoma develops from malignant transformations of the pigment-producing melanocytes. If located in the basal layer of the skin epidermis, melanoma is referred to as cutaneous, which is more ...frequent. However, as melanocytes are be found in the eyes, ears, gastrointestinal tract, genitalia, urinary system, and meninges, cases of mucosal melanoma or other types (e.g., ocular) may occur. The incidence and morbidity of cutaneous melanoma (cM) are constantly increasing worldwide. Australia and New Zealand are world leaders in this regard with a morbidity rate of 54/100,000 and a mortality rate of 5.6/100,000 for 2015. The aim of this review is to consolidate and present the data related to the aetiology and pathogenesis of cutaneous melanoma, thus rendering them easier to understand. In this article we will discuss these problems and the possible impacts on treatment for this disease.
Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional ...Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu’s thresholding, (iii) Kapur’s thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database.
The goal of this study was to evaluate the global burden of malignant skin melanoma (MSM) from 1990 to 2019 using MSM-related data from the Global Burden of Disease study.
The incidences' ...relationships with the social-demographic index (SDI) and human developmental index (HDI) were investigated. To determine significant changes in incidence trends, the joinpoint regression model was used. To demonstrate trends in MSM mortality rates, an Age-Period-Cohort framework was conducted. For the projection of new cases and the age-standardized incidence rate (ASR) of MSM incidence to 2034, the Nordpred method was used.
In 2019, the ASR incidence per 100, 000 people for MSM was 3.6 (95% UI, 2.6-4.2). MSM prevalence increased in most countries between 1990 and 2019 (average annual percentage change >0). HDI and annual percentage change (APC) (ρ = .63,
< .001), as well as SDI and ASR, had a positive correlation. The total MSM mortality rate declined globally, with an APC of -.61%. Likewise, the mortality rate for the age group of people with ages <77.5 years declined. Predictive analysis demonstrated a declining trend in ASR incidence and a growing number of MSM.
There are significant differences in ASR incidence among regions and countries. Despite decreases in ASR incidence and fatality, MSM remains one of the leading sources of cancer mortality and morbidity globally. MSM necessitates more primary prevention measures and screening in high-risk areas.
The early diagnosis of melanoma is associated with decreased mortality. The smartphone, with its apps and the possibility of sending photographs to a dermatologist, could improve the early diagnosis ...of melanoma.
The aim of our review was to report the evidence on (1) the diagnostic performance of automated smartphone apps and store-and-forward teledermatology via a smartphone in the early detection of melanoma, (2) the impact on the patient's medical-care course, and (3) the feasibility criteria (focusing on the modalities of picture taking, transfer of data, and time to get a reply).
We conducted a systematic search of PubMed for the period from January 1, 2007 (launch of the first smartphone) to November 1, 2017.
The results of the 25 studies included 13 concentrated on store-and-forward teledermatology, and 12 analyzed automated smartphone apps. Store-and-forward teledermatology opens several new perspectives, such as it accelerates the care course (less than 10 days vs 80 days), and the related procedures were assessed in primary care populations. However, the concordance between the conclusion of a teledermatologist and the conclusion of a dermatologist who conducts a face-to-face examination depended on the study (the kappa coefficient range was .20 to .84, median κ=.60). The use of a dermoscope may improve the concordance (the kappa coefficient range was .29 to .87, median κ=.74). Regarding automated smartphone apps, the major concerns are the lack of assessment in clinical practice conditions, the lack of assessment in primary care populations, and their low sensitivity, ranging from 7% to 87% (median 69%). In this literature review, up to 20% of the photographs transmitted were of insufficient quality. The modalities of picture taking and encryption of the data were only partially reported.
The use of store-and-forward teledermatology could improve access to a dermatology consultation by optimizing the care course. Our review confirmed the absence of evidence of the safety and efficacy of automated smartphone medical apps. Further research is required to determine quality criteria, as there was major variability among the studies.
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