Technological advances for the detection of melanoma

DermSynth3D: Synthesis of in-the-wild annotated dermatology images

In recent years, deep learning (DL) has shown great potential in the field of dermatological image analysis. However, existing datasets in this domain have significant limitations, including a small number of image samples, limited disease conditions, insufficient annotations, and non-standardized image acquisitions. To address these shortcomings, we propose a novel framework called DermSynth3D. DermSynth3D blends skin disease patterns onto 3D textured meshes of human subjects using a differentiable renderer and generates 2D images from various camera viewpoints under chosen lighting conditions in diverse background scenes. Our method adheres to top-down rules that constrain the blending and rendering process to create 2D images with skin conditions that mimic in-the-wild acquisitions, ensuring more meaningful results. The framework generates photo-realistic 2D dermatological images and the corresponding dense annotations for semantic segmentation of the skin, skin conditions, body parts, bounding boxes around lesions, depth maps, and other 3D scene parameters, such as camera position and lighting conditions. DermSynth3D allows for the creation of custom datasets for various dermatology tasks. We demonstrate the effectiveness of data generated using DermSynth3D by training DL models on synthetic data and evaluating them on various dermatology tasks using real 2D dermatological images. We make our code publicly available at https://github.com/sfu-mial/DermSynth3D.

The Role of Dermoscopy in Provider-to-Provider Store-and-Forward Dermatology eConsults: A Scoping Review of the Recent Literature

Purpose of Review

This scoping review maps recent literature on dermatology provider-to-provider asynchronous store-and-forward (SAF) electronic consult (eConsult) platforms with dermoscopy. It offers a descriptive overview, highlighting benefits and challenges.

Recent Findings

Incorporating dermoscopy into SAF eConsults improves diagnostic accuracy for benign and malignant skin neoplasms. Diagnostic and treatment concordance with traditional face-to-face (FTF) visits is high. SAF eConsults with dermoscopy enhance access to dermatological care by improving triage and reducing wait times for FTF visits. Pediatric patients benefit with improved evaluation of melanocytic and vascular growths. eConsult platforms with dermoscopy serve as a telementoring opportunity for clinicians interested in improving their dermoscopy skills.

Summary

Adding dermoscopy to SAF eConsults is valuable and results in improved diagnostic accuracy and reduced need for FTF visits. Implementation barriers can be overcome through collaboration between primary care and dermatology. Dermoscopy in SAF eConsults has significant potential for managing skin conditions and reducing the burden caused by unnecessary FTF visit and biopsies.

Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection

Trypanosomiasis, a neglected tropical disease (NTD), challenges communities in sub-Saharan Africa and Latin America. The World Health Organization underscores the need for practical, field-adaptable diagnostics and rapid screening tools to address the negative impact of NTDs. While artificial intelligence has shown promising results in disease screening, the lack of curated datasets impedes progress. In response to this challenge, we developed the Tryp dataset, comprising microscopy images of unstained thick blood smears containing the Trypanosoma brucei brucei parasite. The Tryp dataset provides bounding box annotations for tightly enclosed regions containing the parasite for 3,085 positive images, and 93 images collected from negative blood samples. The Tryp dataset represents the largest of its kind. Furthermore, we provide a benchmark on three leading deep learning-based object detection techniques that demonstrate the feasibility of AI for this task. Overall, the availability of the Tryp dataset is expected to facilitate research advancements in diagnostic screening for this disease, which may lead to improved healthcare outcomes for the communities impacted.

Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study)

The use of artificial intelligence (AI) has the potential to improve the assessment of lesions suspicious of melanoma, but few clinical studies have been conducted. We validated the accuracy of an open-source, non-commercial AI algorithm for melanoma diagnosis and assessed its potential impact on dermatologist decision-making. We conducted a prospective, observational clinical study to assess the diagnostic accuracy of the AI algorithm (ADAE) in predicting melanoma from dermoscopy skin lesion images. The primary aim was to assess the reliability of ADAE's sensitivity at a predefined threshold of 95%. Patients who had consented for a skin biopsy to exclude melanoma were eligible. Dermatologists also estimated the probability of melanoma and indicated management choices before and after real-time exposure to ADAE scores. All lesions underwent biopsy. Four hundred thirty-five participants were enrolled and contributed 603 lesions (95 melanomas). Participants had a mean age of 59 years, 54% were female, and 96% were White individuals. At the predetermined 95% sensitivity threshold, ADAE had a sensitivity of 96.8% (95% CI: 91.1-98.9%) and specificity of 37.4% (95% CI: 33.3-41.7%). The dermatologists' ability to assess melanoma risk significantly improved after ADAE exposure (AUC 0.7798 vs. 0.8161, p = 0.042). Post-ADAE dermatologist decisions also had equivalent or higher net benefit compared to biopsying all lesions. We validated the accuracy of an open-source melanoma AI algorithm and showed its theoretical potential for improving dermatology experts' ability to evaluate lesions suspicious of melanoma. Larger randomized trials are needed to fully evaluate the potential of adopting this AI algorithm into clinical workflows.

Experiences Regarding Use and Implementation of Artificial Intelligence-Supported Follow-Up of Atypical Moles at a Dermatological Outpatient Clinic: Qualitative Study

BACKGROUND

Artificial intelligence (AI) is increasingly used in numerous medical fields. In dermatology, AI can be used in the form of computer-assisted diagnosis (CAD) systems when assessing and diagnosing skin lesions suspicious of melanoma, a potentially lethal skin cancer with rising incidence all over the world. In particular, CAD may be a valuable tool in the follow-up of patients with high risk of developing melanoma, such as patients with multiple atypical moles. One such CAD system, ATBM Master (FotoFinder), can execute total body dermoscopy (TBD). This process comprises automatically photographing a patient´s entire body and then neatly displaying moles on a computer screen, grouped according to their clinical relevance. Proprietary FotoFinder algorithms underlie this organized presentation of moles. In addition, ATBM Master's optional convoluted neural network (CNN)-based Moleanalyzer Pro software can be used to further assess moles and estimate their probability of malignancy.

OBJECTIVE

Few qualitative studies have been conducted on the implementation of AI-supported procedures in dermatology. Therefore, the purpose of this study was to investigate how health care providers experience the use and implementation of a CAD system like ATBM Master, in particular its TBD module. In this way, the study aimed to elucidate potential barriers to the application of such new technology.

METHODS

We conducted a thematic analysis based on 2 focus group interviews with 14 doctors and nurses regularly working in an outpatient pigmented lesions clinic.

RESULTS

Surprisingly, the study revealed that only 3 participants had actual experience using the TBD module. Even so, all participants were able to provide many notions and anticipations about its use, resulting in 3 major themes emerging from the interviews. First, several organizational matters were revealed to be a barrier to consistent use of the ATBM Master's TBD module, namely lack of guidance, time pressure, and insufficient training. Second, the study found that the perceived benefits of TBD were the ability to objectively detect and monitor subtle lesion changes and unbiasedness of the procedure. Imprecise identification of moles, inability to photograph certain areas, and substandard technical aspects were the perceived weaknesses. Lastly, the study found that clinicians were open to use AI-powered technology and that the TBD module was considered a supplementary tool to aid the medical staff, rather than a replacement of the clinician.

CONCLUSIONS

Demonstrated by how few of the participants had actual experience with the TBD module, this study showed that implementation of new technology does not occur automatically. It highlights the importance of having a strategy for implementation to ensure the optimized application of CAD tools. The study identified areas that could be improved when implementing AI-powered technology, as well as providing insight on how medical staff anticipated and experienced the use of a CAD device in dermatology.

Melanin Stacking Differences in Pigmented and Non-Pigmented Melanomas: Quantitative Differentiation between Pigmented and Non-Pigmented Melanomas Based on Light-Scattering Properties

Cutaneous melanoma is a cancer with metastatic potential characterized by varying amounts of pigment-producing melanocytes, and it is one of the most aggressive and fatal forms of skin malignancy, with several hundreds of thousands of cases each year. Early detection and therapy can lead to decreased morbidity and decreased cost of therapy. In the clinic, this often translates to annual skin screenings, especially for high-risk patients, and generous use of the ABCDE (asymmetry, border irregularity, color, diameter, evolving) criteria. We have used a new technique termed vibrational optical coherence tomography (VOCT) to non-invasively differentiate between pigmented and non-pigmented melanomas in a pilot study. The VOCT results reported in this study indicate that both pigmented and non-pigmented melanomas have similar characteristics, including new 80, 130, and 250 Hz peaks. Pigmented melanomas have larger 80 Hz peaks and smaller 250 Hz peaks than non-pigmented cancers. The 80 and 250 Hz peaks can be used to quantitative characterize differences between different melanomas. In addition, infrared light penetration depths indicated that melanin in pigmented melanomas has higher packing densities than in non-pigmented lesions. Using machine learning techniques, the sensitivity and specificity of differentiating skin cancers from normal skin are shown to range from about 78% to over 90% in this pilot study. It is proposed that using AI on both lesion histopathology and mechanovibrational peak heights may provide even higher specificity and sensitivity for differentiating the metastatic potential of different melanocytic lesions.

Melanoma Detection by Non-Specialists: An Untapped Potential for Triage?

INTRODUCTION

The incidence of melanoma increased considerably in recent decades, representing a significant public health problem. We aimed to evaluate the ability of non-specialists for the preliminary screening of skin lesions to identify melanoma-suspect lesions.

MATERIALS AND METHODS

A medical student and a dermatologist specialist examined the total body scans of 50 patients.

RESULTS

The agreement between the expert and the non-specialist was 87.75% (κ = 0.65) regarding the assessment of clinical significance. The four parameters of the ABCD rule were evaluated on the 129 lesions rated as clinically significant by both observers. Asymmetry was evaluated similarly in 79.9% (κ = 0.59), irregular borders in 74.4% (κ = 0.50), color in 81.4% (κ = 0.57), and diameter in 89.9% (κ = 0.77) of the cases. The concordance of the two groups was 96.9% (κ = 0.83) in the case of the detection of the Ugly Duckling Sign.

CONCLUSIONS

Although the involvement of GPs is part of routine care worldwide, emphasizing the importance of educating medical students and general practitioners is crucial, as many European countries lack structured melanoma screening training programs targeting non-dermatologists.

Clinically Inspired Skin Lesion Classification through the Detection of Dermoscopic Criteria for Basal Cell Carcinoma

Background and Objective. Skin cancer is the most common cancer worldwide. One of the most common non-melanoma tumors is basal cell carcinoma (BCC), which accounts for 75% of all skin cancers. There are many benign lesions that can be confused with these types of cancers, leading to unnecessary biopsies. In this paper, a new method to identify the different BCC dermoscopic patterns present in a skin lesion is presented. In addition, this information is applied to classify skin lesions into BCC and non-BCC. Methods. The proposed method combines the information provided by the original dermoscopic image, introduced in a convolutional neural network (CNN), with deep and handcrafted features extracted from color and texture analysis of the image. This color analysis is performed by transforming the image into a uniform color space and into a color appearance model. To demonstrate the validity of the method, a comparison between the classification obtained employing exclusively a CNN with the original image as input and the classification with additional color and texture features is presented. Furthermore, an exhaustive comparison of classification employing different color and texture measures derived from different color spaces is presented. Results. Results show that the classifier with additional color and texture features outperforms a CNN whose input is only the original image. Another important achievement is that a new color cooccurrence matrix, proposed in this paper, improves the results obtained with other texture measures. Finally, sensitivity of 0.99, specificity of 0.94 and accuracy of 0.97 are achieved when lesions are classified into BCC or non-BCC. Conclusions. To the best of our knowledge, this is the first time that a methodology to detect all the possible patterns that can be present in a BCC lesion is proposed. This detection leads to a clinically explainable classification into BCC and non-BCC lesions. In this sense, the classification of the proposed tool is based on the detection of the dermoscopic features that dermatologists employ for their diagnosis.

An Epidemiologic Analysis of Melanoma Overdiagnosis in the United States, 1975-2017

The primary cause of the increase in melanoma incidence in the United States has been suggested to be overdiagnosis. We used Surveillance, Epidemiology, and End Result Program data from 1975 to 2017 to examine epidemiologic trends of melanoma incidence and mortality and better characterize overdiagnosis in white Americans. Over the 43-year period, incidence and mortality showed discordant temporal changes across population subgroups; trends most suggestive of overdiagnosis alone were present in females aged 55-74. Other groups showed mixed changes suggestive of overdiagnosis plus changes in underlying disease risk (decreasing risk in younger individuals and increasing risk in older males). Cohort effects were identified for male and female mortality and male incidence but were not as apparent for female incidence, suggesting that period effects have had a greater influence on changes in incidence over time in females. Encouraging trends included long-term declines in mortality in younger individuals and recent stabilization of invasive incidence in individuals aged 15-44 years and males aged 45-54 years. Melanoma in situ incidence, however, has continued to increase throughout the population. Overdiagnosis appears to be relatively greater in American females and for melanoma in situ.

The treatment of advanced melanoma: a review of systemic and local therapies in combination with immune checkpoint inhibitors in phase 1 and 2 clinical trials

INTRODUCTION

While the incidence of melanoma continues to rise, the mortality of the disease appears to have stabilized. This may, in part, be due to the development and application of immune checkpoint inhibitors as standard of care in advanced melanoma. However, many patients do not respond to these therapies alone. Combining immune checkpoint inhibitors with other classes of therapeutics appears to be a promising direction to improve response and survival in advanced melanoma.

AREAS COVERED

This review article aims to discuss phase 1 and 2 clinical trials examining immune checkpoint inhibitors in combination therapy for the treatment of advanced, unresectable melanoma. In particular, these regimens include various kinase inhibitors, tumor-infiltrating lymphocytes, toll-like receptor agonists, cytokines, and oncolytic viral therapies. The combinations under discussion include both systemic and combination systemic/local therapies.

EXPERT OPINION

Drug combinations discussed here appear to be promising therapeutic regimens for advanced melanoma. Improved understanding of the mechanisms of primary, adaptive, and acquired resistance to immune checkpoint inhibitors may guide the development of future combination regimens.

Melanoma: How and When to Consider Clinical Diagnostic Technologies

In response to rising rates of melanoma worldwide, novel non-invasive melanoma detection techniques are emerging to facilitate the early detection of melanoma and decrease unnecessary biopsies of benign pigmented lesions. Because they often report similar study findings, it may be difficult to determine how best to incorporate these technologies into clinical practice based on their supporting studies alone. As an expansion of the recent article by Fried et al.¹, which reviewed the clinical data supporting these non-invasive melanoma detection techniques, the first article in this continuing medical education series provides practical advice on how and when to use various non-invasive melanoma detection techniques into clinical practice.

Emerging Developments in Management of Melanoma During the COVID-19 Era

In March 2020, the designation of the COVID-19 outbreak as a worldwide pandemic marked the beginning of an unprecedented era in modern medicine. Facing the possibility of resource precincts and healthcare rationing, leading dermatological and cancer societies acted expeditiously to adapt their guidelines to these contingencies. Melanoma is a lethal and aggressive skin cancer necessitating a multidisciplinary approach to management and is associated with significant healthcare and economic cost in later stages of disease. In revisiting how the pandemic transformed guidelines from diagnosis and surveillance to surgical and systemic management of melanoma, we appraise the evidence behind these decisions and their enduring implications.

Association Between Melanoma Detected During Routine Skin Checks and Mortality

Importance

Early melanoma diagnosis is associated with better health outcomes, but there is insufficient evidence that screening, such as having routine skin checks, reduces mortality.

Objective

To assess melanoma-specific and all-cause mortality associated with melanomas detected through routine skin checks, incidentally or patient detected. A secondary aim was to examine patient, sociodemographic, and clinicopathologic factors associated with different modes of melanoma detection.

Design, Setting, and Participants

This prospective, population-based, cohort study included patients in New South Wales, Australia, who were diagnosed with melanoma over 1 year from October 23, 2006, to October 22, 2007, in the Melanoma Patterns of Care Study and followed up until 2018 (mean [SD] length of follow-up, 11.9 [0.3] years) by using linked mortality and cancer registry data. All patients who had invasive melanomas recorded at the cancer registry were eligible for the study, but the number of in situ melanomas was capped. The treating doctors recorded details of melanoma detection and patient and clinical characteristics in a baseline questionnaire. Histopathologic variables were obtained from pathology reports. Of 3932 recorded melanomas, data were available and analyzed for 2452 (62%; 1 per patient) with primary in situ (n = 291) or invasive (n = 2161) cutaneous melanoma. Data were analyzed from March 2020 to January 2021.

Main Outcomes and Measures

Melanoma-specific mortality and all-cause mortality.

Results

A total of 2452 patients were included in the analyses. The median age at diagnosis was 65 years (range, 16-98 years), and 1502 patients (61%) were men. A total of 858 patients (35%) had their melanoma detected during a routine skin check, 1148 (47%) self-detected their melanoma, 293 (12%) had their melanoma discovered incidentally when checking another skin lesion, and 153 (6%) reported "other" presentation. Routine skin-check detection of invasive melanomas was associated with 59% lower melanoma-specific mortality (subhazard ratio, 0.41; 95% CI, 0.28-0.60; P < .001) and 36% lower all-cause mortality (hazard ratio, 0.64; 95% CI, 0.54-0.76; P < .001), adjusted for age and sex, compared with patient-detected melanomas. After adjusting for prognostic factors including ulceration and mitotic rate, the associations were 0.68 (95% CI, 0.44-1.03; P = .13), and 0.75 (95% CI, 0.63-0.90; P = .006), respectively. Factors associated with higher odds of routine skin-check melanoma detection included being male (female vs male, odds ratio [OR], 0.73; 95% CI, 0.60-0.89; P = .003), having previous melanoma (vs none, OR, 2.36; 95% CI, 1.77-3.15; P < .001), having many moles (vs not, OR, 1.39; 95% CI, 1.10-1.77; P = .02), being 50 years or older (eg, 50-59 years vs <40 years, OR, 2.89; 95% CI, 1.92-4.34; P < .001), and living in nonremote areas (eg, remote or very remote vs major cities, OR, 0.23; 95% CI, 0.05-1.04; P = .003).

Conclusions and Relevance

In this cohort study, melanomas diagnosed through routine skin checks were associated with significantly lower all-cause mortality, but not melanoma-specific mortality, after adjustment for patient, sociodemographic, and clinicopathologic factors.

Etiologies of Melanoma Development and Prevention Measures: A Review of the Current Evidence

Simple Summary

Melanoma constitutes a major public health risk, with the rates of diagnosis increasing on a yearly basis. Monitoring for risk factors and preventing dangerous behaviors that increase melanoma risk, such as tanning, are important measures for melanoma prevention. Additionally, assessing the effectiveness of various methods to prevent sun exposure and sunburns—which can lead to melanoma—is important to help identify ways to reduce the development of melanoma. We summarize the recent evidence regarding the heritable and behavioral risks underlying melanoma, as well as the current methods used to reduce the risk of developing melanoma and to improve the diagnosis of this disease.

Abstract

(1) Melanoma is the most aggressive dermatologic malignancy, with an estimated 106,110 new cases to be diagnosed in 2021. The annual incidence rates continue to climb, which underscores the critical importance of improving the methods to prevent this disease. The interventions to assist with melanoma prevention vary and typically include measures such as UV avoidance and the use of protective clothing, sunscreen, and other chemopreventive agents. However, the evidence is mixed surrounding the use of these and other interventions. This review discusses the heritable etiologies underlying melanoma development before delving into the data surrounding the preventive methods highlighted above. (2) A comprehensive literature review was performed to identify the clinical trials, observational studies, and meta-analyses pertinent to melanoma prevention and incidence. Online resources were queried to identify epidemiologic and clinical trial information. (3) Evidence exists to support population-wide screening programs, the proper use of sunscreen, and community-targeted measures in the prevention of melanoma. Clinical evidence for the majority of the proposed preventive chemotherapeutics is presently minimal but continues to evolve. (4) Further study of these chemotherapeutics, as well as improvement of techniques in artificial intelligence and imaging techniques for melanoma screening, is warranted for continued improvement of melanoma prevention.

On Naevi and Melanomas: Two Sides of the Same Coin?

Benign naevi are closely linked to melanoma, as risk factors, simulators, or sites of melanoma formation. There is a heavy genetic overlap between the two lesions, a shared environmental influence of ultraviolet radiation, and many similar cellular features, yet naevi remain locally situated while melanomas spread from their primary site and may progress systemically to distal organs. Untangling the overlapping contributors and predictors of naevi and melanoma is an ongoing area of research and should eventually lead to more personalized prevention and treatment strategies, through the development of melanoma risk stratification tools and early detection of evolving melanomas. This will be achieved through a range of complementary strategies: risk-adjusted primary prevention counseling; the use of lesion imaging technologies such as sequential 3D total body photography and consumer-performed lesion imaging; artificial intelligence deep phenotyping and clinical assistance; a better understanding of genetic drivers of malignancy, risk variants, clinical genetics, and polygenic effects; and the interplay between genetics, phenotype and the environment.

A Clinical Perspective on the Automated Analysis of Reflectance Confocal Microscopy in Dermatology

BACKGROUND AND OBJECTIVES

Non-invasive optical imaging has the potential to provide a diagnosis without the need for biopsy. One such technology is reflectance confocal microscopy (RCM), which uses low power, near-infrared laser light to enable real-time in vivo visualization of superficial human skin from the epidermis down to the papillary dermis. Although RCM has great potential as a diagnostic tool, there is a need for the development of reliable image analysis programs, as acquired grayscale images can be difficult and time-consuming to visually assess. The purpose of this review is to provide a clinical perspective on the current state of artificial intelligence (AI) for the analysis and diagnostic utility of RCM imaging.

STUDY DESIGN/MATERIALS AND METHODS

A systematic PubMed search was conducted with additional relevant literature obtained from reference lists.

RESULTS

Algorithms used for skin stratification, classification of pigmented lesions, and the quantification of photoaging were reviewed. Image segmentation, statistical methods, and machine learning techniques are among the most common methods used to analyze RCM image stacks. The poor visual contrast within RCM images and difficulty navigating image stacks were mediated by machine learning algorithms, which allowed the identification of specific skin layers.

CONCLUSIONS

AI analysis of RCM images has the potential to increase the clinical utility of this emerging technology. A number of different techniques have been utilized but further refinements are necessary to allow consistent accurate assessments for diagnosis. The automated detection of skin cancers requires more development, but future applications are truly boundless, and it is compelling to envision the role that AI will have in the practice of dermatology. Lasers Surg. Med. © 2020 Wiley Periodicals LLC.