Early detection of melanoma: reviewing the ABCDEs

Assessing the Impact of Simulated Color Vision Deficiency on Ophthalmologists' Ability to Differentiate between Choroidal Melanoma and Choroidal Nevus

Background: Color vision deficiency (CVD) is an often-overlooked issue within the medical community, and its consequences remain insufficiently explored. We aim to evaluate how CVD affects diagnostic accuracy and distinguish between malignant choroidal melanoma and benign choroidal nevus among ophthalmologists. Methods: In this cross-sectional study, we engaged ophthalmologists through a web-based survey distributed via the professional ophthalmology society's social media channels. The survey encompassed a series of three fundus images representing normal fundus, choroidal nevus, and choroidal melanoma. Each image underwent simulation for the three primary types of CVD-protanopia, deuteranopia, and tritanopia-alongside a non-simulated version. Results: The study included 41 participants, averaging 40 years of age (±9.2), comprising 28 (68%) men and 13 (32%) women. Significantly lower rates of identifying orange pigments were observed in simulated protanopia images compared to non-simulated ones (p = 0.038). In simulated deutranopia images, the recognition of melanotic lesions was notably reduced compared to non-simulated images (p = 0.048). No such limitation was observed for tritanopia. However, participants retained their ability to identify subretinal fluid and estimate tumor thickness in simulated and non-simulated images. Concerning simulated images of choroidal nevi, participants misdiagnosed nevi as choroidal melanoma in 37% of cases in simulated protanopia nevi images and 41% in simulated deutranopia nevi images. This resulted in unnecessary referrals of benign lesions as malignant, emphasizing the potential for mistaken diagnoses. Nevertheless, almost all simulated images of malignant melanoma were correctly referred for specialized oncological treatment. Conclusions: The simulated CVD conditions of protanopia and deuteranopia affected the accuracy of identifying the melanotic nature of the choroidal tumor and the presence of orange pigments. This limitation led to challenges in correctly diagnosing choroidal melanoma and choroidal nevus, resulting in extra referrals for nevus cases. However, participants were safe and could still determine the possible risk of eyes with choroidal melanoma, so most referred melanoma cases to specialized oncologists as needed.

Artificial intelligence in the detection of skin cancer: State of the art

The incidence of melanoma is increasing rapidly. This cancer has a good prognosis if detected early. For this reason, various systems of skin lesion image analysis, which support imaging diagnostics of this neoplasm, are developing very dynamically. To detect and recognize neoplastic lesions, such systems use various artificial intelligence (AI) algorithms. This area of computer science applications has recently undergone dynamic development, abounding in several solutions that are effective tools supporting diagnosticians in many medical specialties. In this contribution, a number of applications of different classes of AI algorithms for the detection of this skin melanoma are presented and evaluated. Both classic systems based on the analysis of dermatoscopic images as well as total body systems, enabling the analysis of the patient's whole body to detect moles and pathologic changes, are discussed. These increasingly popular applications that allow the analysis of lesion images using smartphones are also described. The quantitative evaluation of the discussed systems with particular emphasis on the method of validation of the implemented algorithms is presented. The advantages and limitations of AI in the analysis of lesion images are also discussed, and problems requiring a solution for more effective use of AI in dermatology are identified.

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases' diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer's diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

A Retrospective Study of Diameter and Breslow Thickness in Invasive Melanomas

INTRODUCTION

A diameter larger than 6 mm is included in the criteria used in public health messages to detect a cutaneous melanoma. We aimed to investigate the independent association of Breslow thickness with the melanoma diameter.

METHODS

A retrospective study was performed in patients with invasive melanomas of the nodular melanoma (NM) or superficial spreading melanoma (SSM) subtype. The quartiles of the diameter (lower, median, upper) were studied in non-parametric quantile regression model.

RESULTS

In total, 537 cases of invasive melanomas were included and 60% had Breslow thickness ≤1.0 mm. There were 429 SSM (79.9%) and 108 NM (20.1%). Although NMs were significantly thicker (median Breslow thickness: 2.7 mm vs. 0.7 mm, respectively, p < 0.0001), they were not associated with larger diameter compared to SSMs (p = 0.71). After adjustment for age and sex, melanoma location and subtype, having Breslow thickness ≤1.0 mm was not significantly associated with the lower quartile, median and upper quartile of the diameter (p values: 0.063, 0.083, and 0.791, respectively).

CONCLUSION

In our study including melanomas of the NM or SSM subtype, Breslow thickness was not associated with the diameter, adding evidence to support the limitations of using diameter larger than 6 mm for the detection of invasive melanomas and indicating the potential of smaller melanomas to be thicker tumors.

The effects of procedural and conceptual knowledge on visual learning

Even though past research suggests that visual learning may benefit from conceptual knowledge, current interventions for medical image evaluation often focus on procedural knowledge, mainly by teaching classification algorithms. We compared the efficacy of pure procedural knowledge (three-point checklist for evaluating skin lesions) versus combined procedural plus conceptual knowledge (histological explanations for each of the three points). All students then trained their classification skills with a visual learning resource that included images of two types of pigmented skin lesions: benign nevi and malignant melanomas. Both treatments produced significant and long-lasting effects on diagnostic accuracy in transfer tasks. However, only students in the combined procedural plus conceptual knowledge condition significantly improved their diagnostic performance in classifying lesions they had seen before in the pre- and post-tests. Findings suggest that the provision of additional conceptual knowledge supported error correction mechanisms.

Insights into Melanoma Clinical Practice: A Perspective for Future Research

BACKGROUND

Early diagnosis is the key to improving outcomes for patients with melanoma, and this requires a standardized histological assessment approach. The objective of this survey was to understand the challenges faced by clinicians when assessing melanoma cases, and to provide a perspective for future studies.

METHODS

Between April 2022 and February 2023, national and international dermatologists, pathologists, general practitioners, and laboratory managers were invited to participate in a six-question online survey. The data from the survey were assessed using descriptive statistics and qualitative responses.

RESULTS

A total of 54 responses were received, with a 51.4% (n = 28) full completion rate. Of the respondents, 96.4% reported ambiguity in their monthly melanoma diagnosis, and 82.1% routinely requested immunohistochemistry (IHC) testing to confirm diagnosis. SOX10 was the most frequently requested marker, and most respondents preferred multiple markers over a single marker. Diagnostic and prognostic tests, as well as therapeutic options and patient management, were all identified as important areas for future research.

CONCLUSIONS

The respondents indicated that the use of multiple IHC markers is essential to facilitate diagnostic accuracy in melanoma assessment. Survey responses indicate there is an urgent need to develop new biomarkers for clinical decision making at multiple critical intervention points.

Clinical and Dermoscopic Findings of Nevi after Photoepilation: A Review

Atypical clinical and dermoscopic findings, or changes in pigmented melanocytic lesions located on body areas treated with lasers or intense pulsed light (IPL) for hair removal (photoepilation), have been described in the literature. There are three prospective studies in a total of 79 individuals with 287 melanocytic nevi and several case reports reporting the dermoscopic findings and changes after photoepilation. Clinical changes have been reported in 20-100% of individuals, while dermoscopic changes have been observed in 48% to 93% of nevi. More frequent dermoscopic changes included bleaching, the development of pigmented globules, and irregular hyperpigmented areas and regression structures, including gray areas, gray dots/globules, and whitish structureless areas. The diagnostic approach for pigmented lesions with atypical dermoscopic findings and changes after photo-epilation included reflectance confocal microscopy, sequential digital dermoscopy follow-up, and/or excision and histopathology. Challenges pertaining to these diagnostic steps in the context of photoepilation include the detection of findings that may warrant a biopsy to exclude melanoma (ugly duckling, irregular hyperpigmented areas, blue-gray or white areas, and loss of pigment network), the potential persistence of changes at follow-up, and that a histopathologic diagnosis may not be possible due to the distortion of melanocytes or complete regression of the lesion. Furthermore, these diagnostic approaches can be time-consuming, require familiarization of the physician with dermoscopic features, may cause anxiety to the individual, and highlight that avoiding passes of the laser or IPL devices over pigmented lesions is key.

Prosopis juliflora (Sw.) DC phytochemicals induce apoptosis and inhibit cell proliferation signaling pathways, EMT, migration, invasion, angiogenesis and stem cell markers in melanoma cell lines

ETHNOPHARMACOLOGICAL RELEVANCE

Prosopis juliflora (Sw.), DC is a xerophytic plant species that extensively grow in Asia, Africa, Australia, and Brazil. From ancient time P. juliflora is being utilized in various folk remedies for example in wound healing, fever, inflammation, measles, excrescences, diarrhea and dysentery. Traditionally, gum, paste, and smoke obtained from the leaves and pods are applied for anticancer, antidiabetic, anti-inflammatory, and antimicrobial purposes.

AIM OF THE STUDY

Our previous studies have demonstrated the promising potential of Prosopis Juliflora leaves methanol extract (PJLME) against breast cancer, and suggested its possible integration as a complementary medicine for the effective management of breast cancer. However, evidence against how PJLME mechanistically target the cancer proliferative pathways and other targets is poorly understood. The basic aim of the present study was to understand the anti-melanoma potential of PJLME against B16f10 cells with possible mechanisms of action.

MATERIALS AND METHODS

MTT assay was used to determine cell viability. Wound and transwell migration assay was performed to check migration potential of cells after PJLME treatment, while clonogenic assay was carried out to understand its colony inhibition actvity. Flow cytometry was used to perform annexin V/PI assay (apoptosis assay), ROS assay, cell cycle analysis. In-vitro angiogenesis assay was performed to check formation of capillary like vascular structure after PJLME treatment. Apoptotic genes, signaling pathways markers, EMT markers and stem cell markers were determined by western blotting. In-vivo BALB/C mice xenograft model study was performed to check the effect of PJLME on in-vivo melanoma tumor growth.

RESULTS

The experimental outcome of the present study has clearly demonstrated the inhibition of growth, migration, invasion, colony formation and apoptosis inducing potential of PJLME against mouse melanoma cancer cells. Treatment of B16F10 melanoma cells with PJLME resulted in arrest of cell cycle at G0/G1 phase. Annexin V-FITC/PI assay confirmed the apoptosis inducing potential of PJLME in B16F10 and A375 melanoma cells. Furthermore, Western blot experiments confirmed that the treatment of PJLME downregulates the expression of anti-apoptotic gene like Bcl2 and increase the expression profile of pro-apoptotic genes like Bax, Bad, and Bak in B16F10 melanoma cells. HUVEC (Human umbilical vein endothelial cells) tube formation assay clearly demonstrated the anti-angiogenic potential of PJLME. The study also revealed that PJLME has potential to inhibit the Akt and Erk signaling pathways which are participating in cancer cell proliferation, migration, invasion etc. The outcome of qRT-PCR and immunoblotting analysis clearly unveiled that PJLME treatment leads to downregulation of epithelial-mesenchymal transition (EMT) as well as stem cell markers. Finally, the in-vivo animal xenograft model study also revealed the anti-melanoma potential of PJLME by significantly inhibiting the B16F10 melanoma tumor growth in BALB/c mice model. The LC-ESI-MS/MS analysis of PJLME showed the presence of variety of bioactive molecules associated with anticancer effects.

CONCLUSION

The outcome of the present investigation clearly demonstrated the anti-melanoma potential of PJLME against B16f10 melanoma cells. PJLME can be explored as an adjuvant or complementary therapy against melanoma cancer, however further studies are required to understand the clinical efficacy of PJLME. Nevertheless, it can be further explored as a promising resource for identification of novel anticancer candidate drug.

Anticancer Potential of Natural Chalcones: In Vitro and In Vivo Evidence

There is no doubt that significant progress has been made in tumor therapy in the past decades. However, the discovery of new molecules with potential antitumor properties still remains one of the most significant challenges in the field of anticancer therapy. Nature, especially plants, is a rich source of phytochemicals with pleiotropic biological activities. Among a plethora of phytochemicals, chalcones, the bioprecursors of flavonoid and isoflavonoids synthesis in higher plants, have attracted attention due to the broad spectrum of biological activities with potential clinical applications. Regarding the antiproliferative and anticancer effects of chalcones, multiple mechanisms of action including cell cycle arrest, induction of different forms of cell death and modulation of various signaling pathways have been documented. This review summarizes current knowledge related to mechanisms of antiproliferative and anticancer effects of natural chalcones in different types of malignancies including breast cancers, cancers of the gastrointestinal tract, lung cancers, renal and bladder cancers, and melanoma.

Photography as the Sole Means of Proof: Medical Liability in Dermatology

Malignant melanoma is a cutaneous malignancy resulting from the uncontrolled proliferation of melanocytes and poses a challenge diagnostically because neoplastic lesions can mimic benign lesions, which are much more common in the population. Doctors, when they suspect the presence of melanoma, arrange for its removal and the performance of a histological examination to ascertain its diagnosis; in cases where the dermatoscopic examination is indicative of benignity, however, after the lesion is removed, histological examination is not always performed, a very dangerous occurrence and a harbinger of further medico-legal problems. The authors present a court litigation case of an “alleged” failure to diagnose malignant melanoma in a patient who died of brain metastases from melanoma in the absence of a certain location of the primary tumor: the physician who had removed a benign lesion a few months earlier was sued, and only thanks to the presence of photographic documentation was the health care provider able to prove his extraneousness. The aim of this paper is to formulate a proposal for a dermatological protocol to be followed in cases of excisions of benign skin lesions with a twofold purpose: on the one hand, to be able to prove, in a judicial context, the right action on the part of the sanitarians; on the other hand, to avoid the rise of so-called “defensive medicine”.

Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma

Melanoma is a lethal skin disease that develops from moles. This study aimed to integrate multimodal data to predict metastatic melanoma, which is highly aggressive and difficult to treat. The proposed EnsembleSKCM method evaluated the prediction performances of long noncoding RNAs (lncRNAs), protein-coding messenger genes (mRNAs) and pathology images (images) for metastatic melanoma. Feature selection was used to screen for metastatic biomarkers in the lncRNA and mRNA datasets. The integrated EnsembleSKCM model was built based on the weighted results of the lncRNA-, mRNA- and image-based models. EnsembleSKCM achieved 0.9444 in the prediction accuracy of metastatic melanoma and outperformed the single-modal prediction models based on the lncRNA, mRNA and image data. The experimental data suggest the importance of integrating the complementary information from the three data modalities. WGCNA was used to analyze the relationship of molecular-level features and image features, and the results show connections between them. Another cohort was used to validate our prediction.

Evaluating a game-based randomized experiment to increase melanoma identification among adults living in the U.S

BACKGROUND

Although cutaneous melanoma diagnoses are rising, morbidity and mortality can be reduced through early detection. This investigation seeks to improve melanoma identification accuracy, attitudes, and intentions among a lay population by comparing the effectiveness of different melanoma identification training strategies and the effect of real-time decisional feedback on a melanoma identification task. We developed an innovative, game-based approach and hypothesize differences among frequently used melanoma identification training modalities (i.e, the Asymmetry/Border/Color/Diameter [ABCD] rule, the Ugly Duckling Rule [UDS], and a modality that combines them both, ABCDF (where the F stands for 'funny looking"), and investigate differences in types of immediate feedback on a melanoma identification task.

METHODS

We conducted a national online randomized experiment to test a 4 (melanoma training strategies: ABCD, UDS, ABCD-F, control) × 3 (feedback: Dermatological, Dermatological + Motivational, control) factorial design on melanoma identification, skin cancer beliefs (perceived susceptibility, severity, response efficacy, self-efficacy), attitudes, and prevention intentions.

RESULTS

ABCD training (p < .001) and UDS training (p = .05) resulted in significantly higher melanoma identification than the control. All training types resulted in significantly higher self-efficacy than the control (p = .02). Both Dermatological (p = .02) and Dermatological + Motivational feedback (p = .01) elicited significantly lower melanoma identification than the control condition, although this effect may be due to differences observed among participants who received UDS training. There was a significant main effect of feedback on self-efficacy (p = .002), where both Dermatological and Dermatological + Motivational feedback elicited higher levels of self-efficacy than the control.

CONCLUSIONS

Our results suggest that game-based ABCD and UDS training strategies could increase melanoma identification accuracy. Real-time feedback reduced accuracy, but was associated with increased self-efficacy related to melanoma detection outcomes.

Assessing the impact of color blindness on the ability of identifying benign and malignant skin lesions by naked-eye examination

Background

Color vision deficiency describes the inability to distinguish certain shades of color. The aim of this study was to assess the impact of having color vision deficiency on the accuracy of distinguishing benign and malignant skin lesions by naked-eye examination.

Methods

This was a cross-sectional study conducted during the period August 2020 to February 2021. We randomly selected a total of 20 nevi and 20 melanoma images from an open access image database. The 40 images were divided into four sets of images, each set contained 5 benign and 5 malignant skin lesion images simulated as if they were seen by a protanope physician, deuteranope physician, tritanope physician, and a set of images presented without simulation. In an online survey, students who were in their final year of medical school or had newly graduated were asked to diagnose each image as benign or malignant.

Results

A total of 140 participants were included with a mean (SD) age of 24.88 (1.51). We found a significantly higher mean accuracy for non-simulated images compared to deuteranope simulated images (p< 0.001, mean difference = 11.07, 95% CI 8.40 to 13.74). We did not find a significant difference in accuracy classification for protanope simulated images (p = 0.066), nor for tritanope simulated images (p = 0.315). Classification accuracy for malignant lesions was higher than classification accuracy for benign lesions, with the highest difference belonging to deuteranope simulated images, with a difference in mean accuracy of classifying malignant lesions by 32.2 (95% CI 27.0 to 37.6).

Conclusion

Deuteranope participants (i.e., green color deficiency) had a significantly lower accuracy of distinguishing pigmented skin lesions as benign or malignant, an impact not found for other color vision deficiencies, which was mainly for misdiagnosing benign lesions as malignant.

A Novel Approach for the Shape Characterisation of Non-Melanoma Skin Lesions Using Elliptic Fourier Analyses and Clinical Images

The early detection of Non-Melanoma Skin Cancer (NMSC) is crucial to achieve the best treatment outcomes. Shape is considered one of the main parameters taken for the detection of some types of skin cancer such as melanoma. For NMSC, the importance of shape as a visual detection parameter is not well-studied. A dataset of 993 standard camera images containing different types of NMSC and benign skin lesions was analysed. For each image, the lesion boundaries were extracted. After an alignment and scaling, Elliptic Fourier Analysis (EFA) coefficients were calculated for the boundary of each lesion. The asymmetry of lesions was also calculated. Then, multivariate statistics were employed for dimensionality reduction and finally computational learning classification was employed to evaluate the separability of the classes. The separation between malignant and benign samples was successful in most cases. The best-performing approach was the combination of EFA coefficients and asymmetry. The combination of EFA and asymmetry resulted in a balanced accuracy of 0.786 and an Area Under Curve of 0.735. The combination of EFA and asymmetry for lesion classification resulted in notable success rates when distinguishing between benign and malignant lesions. In light of these results, skin lesions’ shape should be integrated as a fundamental part of future detection techniques in clinical screening.

Skin Conditions and Movement Disorders: Hiding in Plain Sight

Skin manifestations are well-recognized non-motor symptoms of Parkinson's disease (PD) and other hypokinetic and hyperkinetic movement disorders. Skin conditions are usually well visible during routine clinical examination and their recognition may play a major role in diagnostic work-up. In this educational review we: (1) briefly outline skin conditions related to Parkinson's disease, including therapy-related skin complications and their management; (2) discuss the role of skin biopsies in early diagnosis of PD and differential diagnosis of parkinsonian syndromes; and focus more on areas which have not been reviewed in the literature before, including (3) skin conditions related to atypical parkinsonism, and (4) skin conditions related to hyperkinetic movement disorders. In case of rare hyperkinetic movement disorders, specific dermatological manifestations, like presence of angiokeratomas, telangiectasias, Mongolian spots, lipomas, ichthyosis, progeroid skin changes and others may point to a very specific group of disorders and help guide further investigations.

LncRNA LNCOC1 is Upregulated in Melanoma and Serves as a Potential Regulatory Target of miR-124 to Suppress Cancer Cell Invasion and Migration

Background

A cascade of genes and pathways have been reported in the precise regulation of malignant melanoma (MM). Previous study has demonstrated that lncRNA LNCOC1 is an oncogenic factor in the pathogenesis and development of various cancers. The present study explored the functionalities of LNCOC1 and its interactions with miR-124 in MM.

Methods

A total of 65 melanoma patients were enrolled in this study. The expression of LNCOC1 and miR-124 after cell transfection were detected by RT-qPCR. The migration rates of SK-MEL-3 and A375 cells after transient transfection with LNCOC1 expression vector and miR-124 mimic was detected by trans-well assay.

Results

LNCOC1 was accumulated to high levels in melanoma, and it was significantly correlated with the low survival rate of melanoma patients. Our bioinformatics data showed that miR-124 could target LNCOC1. Overexpression of miR-124 could downregulate LNCOC1. However, up-regulated the expression of LNCOC1 did not affect the expression of miR-124. Our correlation analysis also revealed that the expression of LNCOC1 and miR-124 were inversely correlated in both melanoma tissues and non-tumor tissues. The trans-well invasion and migration assays indicated that overexpression of miR-124 inhibited the melanoma cell invasion and migration. However, overexpression of LNCOC1 promoted melanoma cell invasion and migration.

Conclusion

LNCOC1 is upregulated in melanoma, which can be considered as a potential target of miR-124 in modulating melanoma cell invasion and migration. LNCOC1 may also be an interfering target of MM therapy.

Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population

(1) Background: The purpose of this study was to evaluate the efficacy in terms of sensitivity, specificity, and accuracy of the quantusSKIN system, a new clinical tool based on deep learning, to distinguish between benign skin lesions and melanoma in a hospital population. (2) Methods: A retrospective study was performed using 232 dermoscopic images from the clinical database of the Ramón y Cajal University Hospital (Madrid, Spain). The skin lesions images, previously diagnosed as nevus (n = 177) or melanoma (n = 55), were analyzed by the quantusSKIN system, which offers a probabilistic percentage (diagnostic threshold) for melanoma diagnosis. The optimum diagnostic threshold, sensitivity, specificity, and accuracy of the quantusSKIN system to diagnose melanoma were quantified. (3) Results: The mean diagnostic threshold was statistically lower (p < 0.001) in the nevus group (27.12 ± 35.44%) compared with the melanoma group (72.50 ± 34.03%). The area under the ROC curve was 0.813. For a diagnostic threshold of 67.33%, a sensitivity of 0.691, a specificity of 0.802, and an accuracy of 0.776 were obtained. (4) Conclusions: The quantusSKIN system is proposed as a useful screening tool for melanoma detection to be incorporated in primary health care systems.

Skin Biopsy Techniques

Because many skin lesions and disorders can appear similar, primary care clinicians often struggle to diagnose them definitively without histopathologic information obtained from a biopsy. This review article explains how to decide whether a lesion should be biopsied and what type of biopsy technique to use and then outlines the stepwise approach to each of the most common skin biopsy techniques: shave, saucerization, punch, fusiform, and subcutaneous nodule biopsies. Finally, potential pitfalls and complications are discussed so the clinician can avoid those and can provide a cosmetically acceptable result from these common outpatient procedures.

Association of Medicaid Expansion With Cancer Stage and Disparities in Newly Diagnosed Young Adults

BACKGROUND

Young adults (YAs) experience higher uninsurance rates and more advanced stage at cancer diagnosis than older counterparts. We examined the association of the Affordable Care Act Medicaid expansion with insurance coverage and stage at diagnosis among YAs newly diagnosed with cancer.

METHODS

Using the National Cancer Database, we identified 309 413 YAs aged 18-39 years who received a first cancer diagnosis in 2011-2016. Outcomes included percentages of YAs without health insurance at diagnosis, with stage I (early-stage) diagnoses, and with stage IV (advanced-stage) diagnoses. We conducted difference-in-difference (DD) analyses to examine outcomes before and after states implemented Medicaid expansion compared with nonexpansion states. All statistical tests were 2-sided.

RESULTS

The percentage of uninsured YAs decreased more in expansion than nonexpansion states (adjusted DD = -1.0 percentage points [ppt], 95% confidence interval [CI] = -1.4 to -0.7 ppt, P < .001). The overall percentage of stage I diagnoses increased (adjusted DD = 1.4 ppt, 95% CI = 0.6 to 2.2 ppt, P < .001) in expansion compared with nonexpansion states, with greater improvement among YAs in rural areas (adjusted DD = 7.2 ppt, 95% CI = 0.2 to 14.3 ppt, P = .045) than metropolitan areas (adjusted DD = 1.3 ppt, 95% CI = 0.4 to 2.2 ppt, P = .004) and among non-Hispanic Black patients (adjusted DD = 2.2 ppt, 95% CI = -0.03 to 4.4 ppt, P = .05) than non-Hispanic White patients (adjusted DD = 1.4 ppt, 95% CI = 0.4 to 2.3 ppt, P = .008). Despite the non-statistically significant change in stage IV diagnoses overall, the percentage declined more (adjusted DD = -1.2 ppt, 95% CI = -2.2 to -0.2 ppt, P = .02) among melanoma patients in expansion relative to nonexpansion states.

CONCLUSIONS

We provide the first evidence, to our knowledge, on the association of Medicaid expansion with shifts to early-stage cancer at diagnosis and a narrowing of rural-urban and Black-White disparities in YA cancer patients.

The Challenging Melanoma Landscape: From Early Drug Discovery to Clinical Approval

Melanoma is recognized as the most dangerous type of skin cancer, with high mortality and resistance to currently used treatments. To overcome the limitations of the available therapeutic options, the discovery and development of new, more effective, and safer therapies is required. In this review, the different research steps involved in the process of antimelanoma drug evaluation and selection are explored, including information regarding in silico, in vitro, and in vivo experiments, as well as clinical trial phases. Details are given about the most used cell lines and assays to perform both two- and three-dimensional in vitro screening of drug candidates towards melanoma. For in vivo studies, murine models are, undoubtedly, the most widely used for assessing the therapeutic potential of new compounds and to study the underlying mechanisms of action. Here, the main melanoma murine models are described as well as other animal species. A section is dedicated to ongoing clinical studies, demonstrating the wide interest and successful efforts devoted to melanoma therapy, in particular at advanced stages of the disease, and a final section includes some considerations regarding approval for marketing by regulatory agencies. Overall, considerable commitment is being directed to the continuous development of optimized experimental models, important for the understanding of melanoma biology and for the evaluation and validation of novel therapeutic strategies.

Seborrheic keratosis-like melanoma: a diagnostic challenge

This article summarizes the main epidemiologic and diagnostic features of seborrheic keratosis-like (SK-like) melanoma. We performed a review of the current literature. The real and current incidence and prevalence of SK-like melanoma are unknown. Many lesions are misdiagnosed and not excised with histopathologic confirmation, or excised without an appropriate surgical approach due to a benign clinical and dermoscopic appearance. SK-like melanoma presents both melanoma and SK features. SK-like melanoma and SK are often clinically indistinguishable even for experienced dermatologists. Clinically, it develops as a black-dark brown or light slightly elevated, papule, nodule or plaque with rapid growth and a regular or irregular shape. SK-like melanoma presents melanocytic and nonmelanocytic dermoscopic features. Irregular dark-brown dots/globules, a blue-grayish veil, streaks, irregular pigmentation or network and brown lines point to a melanoma diagnosis. Among the nonmelanocytic features, milia-like cysts and comedo-like openings have been highlighted. The association of polarized and nonpolarized dermoscopic techniques is more accurate for studying the dermoscopic features of SK-like melanoma. If the dermoscopic features are unclear, further investigations with reflectance confocal microscopy (RCM) or biopsy with histologic examination are essential. SK-like melanoma is a dermatologic challenge. Careful clinical and dermoscopic evaluation favor a correct diagnosis. In unclear cases, it is important to perform a biopsy with histologic examination to confirm the correct diagnosis.

A Clinicopathological Analysis of Melanocytic Nevi: A Retrospective Series

Purpose: Melanocytic nevi are common cutaneous lesions. This study aimed to demonstrate the concordance and discordance between clinical and histopathological diagnoses of melanocytic nevi and the importance of histological evaluation in differentiating malignant lesions from diseases with similar clinical manifestations.

Patients and Methods: We studied 4,561 consecutive patients with a clinical diagnosis of melanocytic nevi from 2014 to 2019. We compared the clinical diagnosis with the histopathological diagnosis to establish a histopathological concordance rate and then investigated the effects of clinical characteristics and the reasons for removal on misclassification.

Results: Among 4,561 patients who were clinically diagnosed with melanocytic nevi, the overall histopathological concordance rate was 82.11% (3,745 of 4,561 patients), while the histopathological discordance rate was 17.89% (816 of 4,561 patients). The histopathological concordance included 90.25% common acquired melanocytic nevi (3,380 of 3,745 patients) and 9.75% other benign melanocytic neoplasms (365 of 3,745 patients). The most common diagnostic change was to seborrheic keratosis (n = 470, 10.30%), followed by basal cell carcinoma (n = 64, 1.40%), vascular tumor (n = 53, 1.16%), fibroma (n = 43, 0.94%), epidermoid cyst (n = 34, 0.75%), wart (n = 30, 0.66%), melanoma (n = 24, 0.53%), Bowen's disease (n = 16, 0.35%), squamous cell carcinoma (n = 4, 0.09%), keratoacanthoma (n = 2, 0.04%), and other neoplasms (n = 76, 1.67%). Male sex, old age, location of the lesion, and the reasons for removal have a potential effect on misclassification. The percentages of misclassified lesions on the trunk and limbs and the perineum and buttocks were higher than those in lesions without a change in diagnosis. Importantly, locations of lesions on the head and neck were significantly related to a change in diagnosis to non-melanoma skin cancer, while locations on the hands and feet were significantly related to a change in diagnosis to melanoma. In addition to a typical clinical features, removal due to lesion changes or repeated stimulation was significantly associated with a change in diagnosis to melanoma.

Conclusions: Our study emphasizes the clinical differential diagnosis of melanocytic nevi, especially the possibility of malignant tumors. The occurrence of clinical features associated with clinicopathological discordance should raise the clinical suspect and be carefully differentiated from malignant tumors.

Skin Lesion Segmentation with Improved Convolutional Neural Network

Recently, the incidence of skin cancer has increased considerably and is seriously threatening human health. Automatic detection of this disease, where early detection is critical to human life, is quite challenging. Factors such as undesirable residues (hair, ruler markers), indistinct boundaries, variable contrast, shape differences, and color differences in the skin lesion images make automatic analysis quite difficult. To overcome these challenges, a highly effective segmentation method based on a fully convolutional network (FCN) is presented in this paper. The proposed improved FCN (iFCN) architecture is used for the segmentation of full-resolution skin lesion images without any pre- or post-processing. It is to support the residual structure of the FCN architecture with spatial information. This situation, which creates a more advanced residual system, enables more precise detection of details on the edges of the lesion, and an analysis independent of skin color can be performed. It offers two contributions: determining the center of the lesion and clarifying the edge details despite the undesirable effects. Two publicly available datasets, the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Challenge and PH2 datasets, are used to evaluate the performance of the iFCN method. The mean Jaccard index is 78.34%, the mean Dice score is 88.64%, and the mean accuracy value is 95.30% for the proposed method for the ISBI 2017 test dataset. Furthermore, the mean Jaccard index is 87.1%, the mean Dice score is 93.02%, and the mean accuracy value is 96.92% for the proposed method for the PH2 test dataset.

Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application

The early detection of melanoma is the most efficient way to reduce its mortality rate. Dermatologists achieve this task with the help of dermoscopy, a non-invasive tool allowing the visualization of patterns of skin lesions. Computer-aided diagnosis (CAD) systems developed on dermoscopic images are needed to assist dermatologists. These systems rely mainly on multiclass classification approaches. However, the multiclass classification of skin lesions by an automated system remains a challenging task. Decomposing a multiclass problem into a binary problem can reduce the complexity of the initial problem and increase the overall performance. This paper proposes a CAD system to classify dermoscopic images into three diagnosis classes: melanoma, nevi, and seborrheic keratosis. We introduce a novel ensemble scheme of convolutional neural networks (CNNs), inspired by decomposition and ensemble methods, to improve the performance of the CAD system. Unlike conventional ensemble methods, we use a directed acyclic graph to aggregate binary CNNs for the melanoma detection task. On the ISIC 2018 public dataset, our method achieves the best balanced accuracy (76.6%) among multiclass CNNs, an ensemble of multiclass CNNs with classical aggregation methods, and other related works. Our results reveal that the directed acyclic graph is a meaningful approach to develop a reliable and robust automated diagnosis system for the multiclass classification of dermoscopic images.

Rapid Noninvasive Skin Monitoring by Surface Mass Recording and Data Learning

Skin problems are often overlooked due to a lack of robust and patient-friendly monitoring tools. Herein, we report a rapid, noninvasive, and high-throughput analytical chemical methodology, aiming at real-time monitoring of skin conditions and early detection of skin disorders. Within this methodology, adhesive sampling and laser desorption ionization mass spectrometry are coordinated to record skin surface molecular mass in minutes. Automated result interpretation is achieved by data learning, using similarity scoring and machine learning algorithms. Feasibility of the methodology has been demonstrated after testing a total of 117 healthy, benign-disordered, or malignant-disordered skins. Remarkably, skin malignancy, using melanoma as a proof of concept, was detected with 100% accuracy already at early stages when the lesions were submillimeter-sized, far beyond the detection limit of most existing noninvasive diagnosis tools. Moreover, the malignancy development over time has also been monitored successfully, showing the potential to predict skin disorder progression. Capable of detecting skin alterations at the molecular level in a nonsurgical and time-saving manner, this analytical chemistry platform is promising to build personalized skin care.

Sonography as a Prognostic Indicator of Breslow Depth: Primary Malignant Melanoma of the Auricular Helix

Malignant melanoma is a cellular cancer that produces pigmentation of the skin. The tendency toward melanoma may be inherited, and risk factors are increased with overexposure to the sun and ultraviolet radiation. Melanomas commonly present as a dark, asymmetrical, mole-like spot that spreads, with an irregular border. It is uncommon to find a melanoma in the auricular regions and even rarer for it to be a primary malignancy of the auricles. Utilizing sonography to evaluate melanoma lesions could serve as a prognostic indicator, regarding Breslow’s depth, an aide in staging, as well as surgical planning. However, utilizing multiple diagnostic imaging modalities is essential in the proper evaluation and staging of a melanoma. Currently there are revolutionary, effective systemic therapies available for patients with a metastatic melanoma. Current therapies are focused on immunotherapy and checkpoint inhibitors. These treatment options provide an opportunity for selected metastatic melanoma patients to achieve healthy long-term success.

Chimeric antigen receptor T-cell therapy for melanoma

INTRODUCTION

In recent years, chimeric antigen receptor (CAR) T cell therapy has emerged as a cancer treatment. After initial therapeutic success for hematologic malignancies, this approach has been extended for the treatment of solid tumors including melanoma.

AREAS COVERED

T cells need to be reprogramed to recognize specific antigens expressed only in tumor cells, a difficult problem since cancer cells are simply transformed normal cells. Tumor antigens, namely, CSPG4, CD70, and GD2 have been targeted by CAR-T cells for melanoma. Moreover, different co-stimulatory signaling domains need to be selected to direct T cell fate. In this review, various approaches for the treatment of melanoma and their effectiveness are comprehensively reviewed and the current status, challenges, and future perspective of CAR-T cell therapy for melanoma are discussed. Literature search was accomplished in three databases (PubMed, Google scholar, and Clinicaltrials.gov). Published papers and clinical trials were screened and relevant documents were included by checking pre-defined eligibility criteria.

EXPERT OPINION

Despite obstacles and the risk of adverse events, CAR T cell therapy could be used for patients with treatment-resistant cancer. Clinical trials are underway to determine the efficacy of this approach for the treatment of melanoma.

Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing

This paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel in the hyperspectral image (HSI) and then taking the mean and standard deviation of these sub-images, we were able to make fits of the resulting curves. These fitted curves had certain characteristics, which then served as a basis of classification. The most distinct fit was for the melanoma pigmented skin lesions (PSLs), which is also the most aggressive malignant cancer. Furthermore, we were able to classify the other PSLs in malignant and benign classes. This gives us a rather complete classification method for PSLs with a novel perspective of the classification procedure by exploiting the variability of each channel in the HSI.

Melanoma diagnosis using deep learning techniques on dermatoscopic images

BACKGROUND

Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that is able to detect the presence of melanoma via a dermatoscopic image of lesions and/or skin pigmentation can be a very useful tool in the area of medical diagnosis.

METHODS

Among state-of-the-art methods used for automated or computer assisted medical diagnosis, attention should be drawn to Deep Learning based on Convolutional Neural Networks, wherewith segmentation, classification and detection systems for several diseases have been implemented. The method proposed in this paper involves an initial stage that automatically crops the region of interest within a dermatoscopic image using the Mask and Region-based Convolutional Neural Network technique, and a second stage based on a ResNet152 structure, which classifies lesions as either "benign" or "malignant".

RESULTS

Training, validation and testing of the proposed model was carried out using the database associated to the challenge set out at the 2017 International Symposium on Biomedical Imaging. On the test data set, the proposed model achieves an increase in accuracy and balanced accuracy of 3.66% and 9.96%, respectively, with respect to the best accuracy and the best sensitivity/specificity ratio reported to date for melanoma detection in this challenge. Additionally, unlike previous models, the specificity and sensitivity achieve a high score (greater than 0.8) simultaneously, which indicates that the model is good for accurate discrimination between benign and malignant lesion, not biased towards any of those classes.

CONCLUSIONS

The results achieved with the proposed model suggest a significant improvement over the results obtained in the state of the art as far as performance of skin lesion classifiers (malignant/benign) is concerned.

Melanoma Biomarkers and Their Potential Application for In Vivo Diagnostic Imaging Modalities

Melanoma is the deadliest form of skin cancer and remains a diagnostic challenge in the dermatology clinic. Several non-invasive imaging techniques have been developed to identify melanoma. The signal source in each of these modalities is based on the alteration of physical characteristics of the tissue from healthy/benign to melanoma. However, as these characteristics are not always sufficiently specific, the current imaging techniques are not adequate for use in the clinical setting. A more robust way of melanoma diagnosis is to "stain" or selectively target the suspect tissue with a melanoma biomarker attached to a contrast enhancer of one imaging modality. Here, we categorize and review known melanoma diagnostic biomarkers with the goal of guiding skin imaging experts to design an appropriate diagnostic tool for differentiating between melanoma and benign lesions with a high specificity and sensitivity.

Geometric border as a marker for melanoma diagnosis: Study of 200 consecutive melanocytic lesions

The ABCD rule has long been proposed as a guidance for malignant melanoma (MM) diagnosis. We aimed to define a new simple, straightforward tool that could be useful in early melanoma detection and must be validated in further studies. We conducted a prospective historic cohort study of 200 melanocytic lesions classifying them according to the presence of geometric borders. Sixty-four percent of the MM and 31% of the melanocytic nevi presented with geometric borders. Lesions with two straight borders that formed a noncurvilinear angle presented a 2.1-fold higher risk of being malignant after excision. When comparing melanomas with geometric and nongeometric border, we found a tendency toward better prognostic markers in the geometric lesions. Lesions located in the extremities and melanoma subtype SSM were more common in the geometric group. Regarding pathologic features, a deeper Breslow (mean, 3.8 vs 1.4 mm), presence of ulceration (25% vs 5%) and a higher number of mitosis was found in the nongeometric group. On the other hand, more regression was found in the geometric group while both groups showed similar degree of lymphovascular infiltration. We propose geometric border as another clinical criterion to take into account when suspecting MM, which must be validated in further studies. The ABCDE rule could be completed with a G for geometry.

Partnering With Massage Therapists to Communicate Information on Reducing the Risk of Skin Cancer Among Clients: Longitudinal Study

BACKGROUND

Skin cancer affects millions of Americans and is an important focus of disease prevention efforts. Partnering with non-health care practitioners such as massage therapists (MTs) can reduce the risk of skin cancer. MTs see clients' skin on a regular basis, which can allow MTs to initiate "helping conversations" (ie, brief behavioral interventions aimed at reducing the risk of skin cancer).

OBJECTIVE

The purpose of this study was to evaluate (1) the feasibility of recruiting, enrolling, and retaining Arizona MTs in an online electronic training (e-training) and (2) the preliminary efficacy of e-training on knowledge, attitudes/beliefs, and practice of risk reduction for skin cancer. We explored MTs' ability to assess suspicious skin lesions.

METHODS

We adapted the existing educational content on skin cancer for applicability to MTs and strategies from previous research on helping conversations. We assessed the feasibility of providing such e-training, using Research Electronic Data Capture (REDCap) tools for data capture. We assessed the preliminary efficacy using established self-report surveys at baseline, immediately post training, and at 3 and 6 months post training.

RESULTS

A total of 95 participants enrolled in the study, of which 77% (73/95) completed the assessments at 6 months (overall attrition=23%). Project satisfaction and e-training acceptability were high. Knowledge, personal behaviors (skin self-examination, clinical skin examination, sun protection frequency), and practice attitudes (appropriateness and comfort with client-focused communication) of risk reduction for skin cancer improved significantly and were sustained throughout the study.

CONCLUSIONS

The e-training was feasible and could be delivered online successfully to MTs. Participants were highly satisfied with and accepting of the e-training. As such, e-training has potential as an intervention in larger trials with MTs for reducing the risk of skin cancer.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/13480.

User satisfaction with a smartphone-compatible, artificial intelligence-based cutaneous pigmented lesion evaluator

INTRODUCTION

Melanoma is the most aggressive type of skin cancer, and it may arise from a cutaneous pigmented lesion. As artificial intelligence (AI)-based teledermatology services hold promise in redefining the melanoma screening paradigm, a study that evaluates user satisfaction with a smartphone-compatible, AI-based cutaneous pigmented lesion evaluator is lacking.

METHODS

Data was collected between April and May 2019 in Taiwan. To assess user satisfaction with MoleMe, an AI-based cutaneous pigmented lesion evaluator on a smartphone, users were asked to complete a questionnaire designed to evaluate four aspects, including interaction, impact on daily life, usability, and overall performance, after completing a MoleMe evaluation session. For each question, users could rank their satisfaction level from 1 to 5, with five showing strongly satisfied and one showing strongly unsatisfied. The Kruskal-Wallis and Wilcoxon rank-sum tests were used to compare user satisfaction among different age groups, genders, and risk predictions received.

RESULT

A total of 1231 questionnaires were collected for analysis. Over 90% of the participants were satisfied (score = 4 or 5) and over 75% of the participants were strongly satisfied (score 5) with MoleMe, in terms of usability, interaction, and impact on daily life. The user satisfaction did not show a significant difference between genders, age groups, and risk predictions received. (all P > 0.05) CONCLUSION: With high user satisfaction regardless of age group, gender, and risk prediction received, AI-based teledermatology services on a smartphone such as MoleMe may potentially achieve widespread usage and be beneficial to both patients and physicians.

Callico G. Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

Operationalization of skin self-examination in randomized controlled trials with individuals at increased risk for melanoma: A systematic review

OBJECTIVE

To investigate how skin self-examination was operationalized and the psychometric properties of the scales used to assess this behavior in randomized controlled trials (RCTs) testing interventions that promote SSE among individuals at increased risk for melanoma.

METHOD

Eight scientific databases (e.g., Medline, EMBASE, CINAHL, PsycINFO) and four trial registries (e.g., Clinicaltrials.gov, UK Clinical Trails Gateway) were searched from inception through April 2, 2019. Three reviewers carried out the selection of relevant trials and conducted data extraction.

RESULTS

The review identified 13 unique RCT's. The definition of skin self-exams, extrapolated from instructions provided to participants during the trials and reported in only 6/13 trials, included periodically checking the skin of the entire body, individually or with partners/mirrors, with or without tracking or monitoring tools, and using the ABCDE criteria to identify early signs of melanoma. There was variability in how skin self-examination behavior was measured with respect to item content, number of items, response format, and type of outcome variable used: continuous or binary). No validity evidence and minimal reliability evidence for the measures were identified.

CONCLUSIONS AND PRACTICE IMPLICATIONS

Future studies are needed to establish the psychometric properties of measures assessing skin self-examination.

Skin cancer-related knowledge, attitudes, beliefs, and practices among the population in Gulf Cooperation Council countries: a systematic search and literature review

Skin cancers are the most common malignancies diagnosed worldwide. In Gulf Cooperation Council (GCC) countries, skin cancer remains a significant health burden. Multiple studies have attempted to elucidate patient knowledge and attitudes regarding skin cancer risks and behavioral interventions to reduce risks. A systematic literature search of relevant articles was conducted in PubMed, ScienceDirect, and the Saudi Digital Library databases. A narrative analysis of relevant study results was conducted. A total of 12 studies were reviewed across GCC. These studies revealed common themes among GCC populations. Many study participants were aware that excess sun exposure represents a threat to health and increases cancer risk. Several studies reported sun exposure, with a mean of 19.13 h per week. More studies reported patients engaging in lower or no sunscreen use rather than regular sunscreen use. There are discrepancies in the understanding of sun exposure risk and risk mitigation practices among the study populations. Skin cancer poses a significant burden to patients in GCC countries and improved patient education will enhance population health.

Economic Analysis of a Noninvasive Molecular Pathologic Assay for Pigmented Skin Lesions

Importance

A recently described noninvasive gene expression test (the pigmented lesion assay [PLA]) with adhesive patch-based sampling has the potential to rule out melanoma and the need for surgical biopsy of pigmented lesions suggestive of melanoma with a negative predictive value of 99% compared with 83% for the histopathologic standard of care. The cost implications of using this molecular test vs visual assessment followed by biopsy and histopathologic assessment (VAH) have not been evaluated.

Objective

To determine potential cost savings of PLA use vs the VAH pathway.

Design, Setting, and Participants

This health economic analysis performed from a US payer perspective was based on consensus treatment guidelines and fee schedules from the Centers for Medicare & Medicaid Services. Data for model input were derived from routine use of the test in US dermatology practices and literature. Participants included patients with primary cutaneous pigmented lesions suggestive of melanoma. Data were analyzed from February 8 to December 1, 2017.

Main Outcomes and Measures

The primary analysis consisted of the relative reduction in costs of diagnostic surgical procedures for PLA vs VAH management. Additional analyses included stage-related treatment costs associated with delays in diagnosis.

Results

In the cost analysis for this economic model, the relative reduction in surgical procedure costs (biopsy and subsequent excision), assuming $0 for the PLA to facilitate multiple comparison scenarios, was -$395 compared with VAH. The relative reduction in stage-related treatment costs associated with the PLA was -$433 compared with VAH, primarily associated with avoidance of delays due to false-negative diagnoses. Surveillance costs were reduced by -$119 with the PLA. The total cost of fully adjudicating a lesion suggestive of melanoma by VAH was $947. At a mean selling price reference point for PLA of $500, cost savings of $447 (47%) per lesion tested could be realized.

Conclusions and Relevance

The results of this analysis suggest that the PLA reduces cost and may improve the care of patients with primary pigmented skin lesions suggestive of melanoma.

Skin Cancer Diagnostics with an All-Inclusive Smartphone Application

Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.

LINC0638 lncRNA is involved in the local recurrence of melanoma following surgical resection

Melanoma is a rare malignancy in China and the treatment outcomes are generally satisfactory. However, postoperative recurrence can be life-threatening. The current study aimed to investigate the involvement of long intergenic non-protein coding RNA 1638 (LINC01638) long non-coding RNA (lncRNA) in the recurrence of melanoma. Expression of LINC01638 lncRNA in skin biopsies and plasma of patients with melanoma, patients with benign skin lesions and healthy controls was detected by reverse transcription-quantitative polymerase chain reaction. Diagnostic values of LINC01638 lncRNA for melanoma were analyzed by receiver operating characteristic curve analysis. The association between LINC01638 lncRNA and clinicopathological data of patients with melanoma was analyzed by χ² test. All patients were followed up for five years to record recurrence. LINC01638 lncRNA expression vectors and shRNAs were transfected into human melanoma cell lines and the effects of LINC01638 lncRNA overexpression and knockdown on cell proliferation were analyzed by cell counting kit-8 assay. LINC01638 lncRNA was significantly upregulated in patients with melanoma compared with the other two groups of patients, and upregulation of LINC01638 lncRNA distinguished patients with melanoma from patients with benign skin lesions and healthy controls. LINC01638 lncRNA expression was significantly associated with tumor size but not with other patient clinical data. Plasma levels of LINC01638 lncRNA were further increased during follow-up in patients with local recurrence but not in patients without recurrence. LINC01638 lncRNA overexpression promoted, while knockdown inhibited proliferation of cells of melanoma cell lines, C32 and SK-MEL-28, in vitro. The upregulation of LINC01638 lncRNA was likely associated with the local recurrence of melanoma following surgical resection.