Three roots of melanoma

Segura et al1 describe morphologic features of melanomas with a nodular component using in vivo reflectance-mode confocal microscopy (RCM) and correlate these RCM findings with histopathologic findings. The most striking observation made by the investigators is the remarkable difference in epidermal involvement between nodular melanoma (NM) and superficial spreading melanoma (SSM) with a nodular component. At RCM, SSMs frequently showed epidermal disarrangement and pagetoid infiltration, whereas NMs exhibited a preserved epidermal pattern and few pagetoid cells.1 This new observation provides fertile ground for revisiting the conventional concept of melanoma development. We propose an alternative hypothesis based on recent observations made in stem cell research and demonstrate how this hypothesis can better account for the observed clinical and epidemiologic differences between melanoma subtypes

Metaphoric and descriptive terminology in dermoscopy: Lessons from the cognitive sciences

Metaphoric or analogical terminology is common in clinical dermatology, dermoscopy and dermatopathology. Metaphoric language in dermatology has been criticized for a perceived lack of clear definition and specificity, and non-metaphoric (descriptive) terms and diagnostic algorithms have attempted to be constructed. Metaphors are pervasive in human language and appear to be deeply rooted in our conceptual frameworks. The utility of metaphors in dermoscopy is discussed, with particular reference to research in the cognitive sciences

Assessment of the Safety Risk of Dermatoscope Magnets in Patients With Cardiovascular Implanted Electronic Devices

Importance Cardiovascular implanted electronic devices (CIEDs) are susceptible to electromagnetic interference. Dermatologists regularly use devices containing magnets, including dermatoscopes and their attachments, which could pose a hazard to patients with CIEDs. Objective To investigate the safety risk of magnets in dermatoscopes to patients with CIEDs. Design, Setting, and Participants This cross-sectional observational study was conducted between January 1, 2018, and March 31, 2018, in a controlled laboratory setting. Two experiments were performed. In the first experiment (performed in the Dermatology Service at Memorial Sloan Kettering Cancer Center, New York), dermatoscopes that contain magnets were obtained from 3 manufacturers. Using a magnometer, the magnetic field strength of the dermatoscopes was measured over the magnet; at the faceplate; and at a distance of 0.5 cm, 1 cm and 15 cm away from the faceplate. In the second experiment (performed in the University Heart Center Zurich, Zurich, Switzerland), ex vivo measurements were conducted to determine how the dermatoscopes affected old-generation and new generation CIEDs (pacemakers and implantable defibrillators). Main Outcomes and Measures Magnetic field strength as measured directly over the dermatoscope magnet; at the faceplate; and at distances of 0.5 cm, 1 cm, and 15 cm from the faceplate. Pacemaker and defibrillator operation when exposed to dermatoscopes. Results After conducting 24 measurements, the magnetic field (measured in gauss [G]) strength varied between 24.26 G and 163.04 G over the dermatoscope magnet, between 2.22 G and 9.98 G at the dermatoscope faceplate, between 0.82 G and 2.4 G at a distance of 0.5 cm, and between 0.5 G and 1.04 G at a distance of 1 cm; it was 0 for all devices at a 15 cm distance. The field strength at the faceplate was found to be generally below the CIED industry standard safety threshold. None of the dermatoscopes in the ex vivo experiment exerted any demonstrable disruptions or changes to the CIEDs. Conclusions and Relevance In real life, dermatoscope magnets likely present no measurable safety risk to patients with CIEDs. Using the polarized noncontact mode permits dermoscopy to be performed at least 0.5 cm from the skin surface, where the magnetic field strength was well below the 5-G safety threshold

Standards in Dermatologic Imaging

The current era of ubiquitous digital cameras, digital cameras integrated into smartphones, and virtually limitless data storage affords exciting new opportunities for medicine in general and specifically dermatology. Digital photography has the potential to dramatically enable and facilitate improvements in dermatology teaching, clinical documentation, and diagnosis. One of the barriers to the diffusion of digital imaging into dermatology practice is the lack of standards for digital photography. As noted in the article by Quigley et al,1 there are currently no standards for dermatologic photography designated by Digital Imaging and Communications in Medicine. While some organizations, such as the American Teledermatology Association,2 have offered general guidelines, to our knowledge, no consistent actionable standards exist in medical publications. The absence of standards severely impedes the integration of dermatologic images across systems that support documentation, diagnosis, and clinical practice

Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study

Background Dermoscopy is commonly used for the evaluation of pigmented lesions, but agreement between experts for identification of dermoscopic structures is known to be relatively poor. Expert labeling of medical data is a bottleneck in the development of machine learning (ML) tools, and crowdsourcing has been demonstrated as a cost- and time-efficient method for the annotation of medical images. Objective The aim of this study is to demonstrate that crowdsourcing can be used to label basic dermoscopic structures from images of pigmented lesions with similar reliability to a group of experts. Methods First, we obtained labels of 248 images of melanocytic lesions with 31 dermoscopic “subfeatures” labeled by 20 dermoscopy experts. These were then collapsed into 6 dermoscopic “superfeatures” based on structural similarity, due to low interrater reliability (IRR): dots, globules, lines, network structures, regression structures, and vessels. These images were then used as the gold standard for the crowd study. The commercial platform DiagnosUs was used to obtain annotations from a nonexpert crowd for the presence or absence of the 6 superfeatures in each of the 248 images. We replicated this methodology with a group of 7 dermatologists to allow direct comparison with the nonexpert crowd. The Cohen κ value was used to measure agreement across raters. Results In total, we obtained 139,731 ratings of the 6 dermoscopic superfeatures from the crowd. There was relatively lower agreement for the identification of dots and globules (the median κ values were 0.526 and 0.395, respectively), whereas network structures and vessels showed the highest agreement (the median κ values were 0.581 and 0.798, respectively). This pattern was also seen among the expert raters, who had median κ values of 0.483 and 0.517 for dots and globules, respectively, and 0.758 and 0.790 for network structures and vessels. The median κ values between nonexperts and thresholded average–expert readers were 0.709 for dots, 0.719 for globules, 0.714 for lines, 0.838 for network structures, 0.818 for regression structures, and 0.728 for vessels. Conclusions This study confirmed that IRR for different dermoscopic features varied among a group of experts; a similar pattern was observed in a nonexpert crowd. There was good or excellent agreement for each of the 6 superfeatures between the crowd and the experts, highlighting the similar reliability of the crowd for labeling dermoscopic images. This confirms the feasibility and dependability of using crowdsourcing as a scalable solution to annotate large sets of dermoscopic images, with several potential clinical and educational applications, including the development of novel, explainable ML tools

Fast and Accurate Border Detection in Dermoscopy Images Using Statistical Region Merging

Copyright 2007 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.As a result of advances in skin imaging technology and the development of suitable image processing techniques during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, since the accuracy of the subsequent steps crucially depends on it. In this paper, a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the Statistical Region Merging algorithm is presented. The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which a set of dermatologist-determined borders is used as the ground-truth. The proposed method is compared to six state-of-the-art automated methods (optimized histogram thresholding, orientation-sensitive fuzzy c-means, gradient vector flow snakes, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method) and borders determined by a second dermatologist. The results demonstrate that the presented method achieves both fast and accurate border detection in dermoscopy images.http://dx.doi.org/10.1117/12.70907

Visual Impact of Large and Giant Congenital Naevi: Comparison of Surgical Scars with Naevi Before Surgery

Surgical attempts to remove large/giant congenital melanocytic naevi (LGCMN) are supported mainly by the theoretical improvement in patients’ self-image; however such surgery can result in unaesthetic scarring. We hypothesize that difference in appearance itself has an impact, and hence surgery cannot negate this impact. The aim of this cross-sectional study was to explore how LGCMN and scarring are perceived by non-affected people. We surveyed the visual impact on 1,015 health and non-health professionals working in a university hospital. Participants were assigned to 1 of 3 surveys, which, based on photographs of children: (i) assessed the visual impact of LGCMN; (ii) the visual impact of scarring; (iii) compared the impact of LGCMN and scarring. Feelings and perceptions evoked by images of children, either with LGCMN or with scarring, were remarkably similar. However, when the images of the same child (with LGCMN or scarring) were shown together, respondents showed significantly increased preference for scarring

Theory-Based Approaches to Support Dermoscopic Image Interpretation Education: A Review of the Literature

Introduction: Efficient interpretation of dermoscopic images relies on pattern recognition, and the development of expert-level proficiency typically requires extensive training and years of practice. While traditional methods of transferring knowledge have proven effective, technological advances may significantly improve upon these strategies and better equip dermoscopy learners with the pattern recognition skills required for real-world practice. Objectives: A narrative review of the literature was performed to explore emerging directions in medical image interpretation education that may enhance dermoscopy education. This article represents the first of a two-part review series on this topic. Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collaboration (ISIC)assembled a 12-member Education Working Group that comprises international dermoscopy experts and educational scientists. Based on a preliminary literature review and their experiences as educators, the group developed and refined a list of innovative approaches through multiple rounds of discussion and feedback. For each approach, literature searches were performed for relevant articles. Results: Through a consensus-based approach, the group identified a number of emerging directions in image interpretation education. The following theory-based approaches will be discussed in this first part: whole-task learning, microlearning, perceptual learning, and adaptive learning. Conclusions: Compared to traditional methods, these theory-based approaches may enhance dermoscopy education by making learning more engaging and interactive and reducing the amount of time required to develop expert-level pattern recognition skills. Further exploration is needed to determine how these approaches can be seamlessly and successfully integrated to optimize dermoscopy education