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Background: Intraepidermal Langerhans cells (ILC) are difficult to differentiate from melanocytes under reflectance confocal microscopy (RCM) and their presence may simulate pagetoid spread of melanocytes on RCM images.Objective: We sought to correlate bright round and dendritic cells in a pagetoid pattern identified on RCM with findings of conventional histopathology and immunohistochemistry for lesions that were falsely diagnosed as melanoma by RCM.Methods: This retrospective study included histopathologically proven nevi, imaged by RCM, which displayed bright cells in a pagetoid pattern (BCPP) under RCM, resulting in the incorrect RCM diagnosis of melanoma. Morphological comparisons were made between RCM images of nevi showing BCPP, histopathologically proven melanomas displaying BCPP, and biopsy-proven nevi without BCPP.Results: We identified 24 nevi that were falsely diagnosed as melanoma by RCM because of the presence of BCPP. These pagetoid cells on RCM corresponded on histopathology to ILC with a high density in 23 of the 24 nevi (95%) and to melanocytes in 7 of the 24 nevi (29%). Among 6 melanomas displaying BCPP on RCM, ILC with high density were observed histopathologically in 5 of the 6 cases (83%) and pagetoid melanocytes were seen in all 6 cases (100%).Limitations: The results cannot be generalized to clinically banal-appearing nevi.Conclusions: Although the finding of BCPP is a useful RCM feature for the diagnosis of melanoma, it does not always imply the presence of pagetoid melanocytes but may at times represent ILC. [ABSTRACT FROM AUTHOR]

The incidence of primary cutaneous melanoma has been increasing dramatically for several decades. Melanoma accounts for the majority of skin cancer-related deaths, but treatment is nearly always curative with early detection of disease. In this update of the guidelines of care, we will discuss the treatment of patients with primary cutaneous melanoma. We will discuss biopsy techniques of a lesion clinically suspicious for melanoma and offer recommendations for the histopathologic interpretation of cutaneous melanoma. We will offer recommendations for the use of laboratory and imaging tests in the initial workup of patients with newly diagnosed melanoma and for follow-up of asymptomatic patients. With regard to treatment of primary cutaneous melanoma, we will provide recommendations for surgical margins and briefly discuss nonsurgical treatments. Finally, we will discuss the value and limitations of sentinel lymph node biopsy and offer recommendations for its use in patients with primary cutaneous melanoma. [ABSTRACT FROM AUTHOR]

The authors report baseline findings and predictors of nevus count (log total nevi) at the completion of year 1 (2004) of the first known population-based, prospective study of nevi in a US cohort of children. Overall, 64% (n = 443/691) of grade 5 students and their parents in Framingham, Massachusetts, completed surveys and underwent digital photography. Total nevus count was associated with skin and hair color and tendency to burn, as measured by a sun sensitivity index. In multivariate analyses, male gender (rate ratio (RR) = 1.38, 95% confidence interval (CI): 1.22, 1.55; P < 0.0001), spending 5-6 weekly hours outdoors between 10 AM and 4 PM (RR = 1.13, 95% CI: 1.00, 1.28; P = 0.051), getting a painful sunburn once (RR = 1.24, 95% CI: 0.98, 1.57; P = 0.073) and at least twice (RR = 1.34, 95% CI: 0.99, 1.82; P = 0.061), and wearing a shirt at the beach or pool rarely (RR = 1.29, 95% CI: 1.08, 1.54; P = 0.005), sometimes (RR = 1.26, 95% CI: 1.01, 1.57; P = 0.041), and often and always (RR = 1.32, 95% CI: 1.13, 1.54; P = 0.001) were associated with increased number of nevi. Identifying factors that predict the development of nevi will improve primary prevention efforts during early life. [ABSTRACT FROM AUTHOR]

Background: We describe the planning, recruitment, key challenges, and lessons learned in the development of a study of the evolution of nevi (moles) among children in a school setting. Methods: This population-based study of digital photography and dermoscopy of the child's back (overview, close-up, and dermoscopic images) and genetic specimens took place among fifth graders in the Framingham, Massachusetts School System. Schoolchildren and their parents completed baseline surveys on sun protection practices, sunburns, and past ultraviolet exposures, including summer and vacation experiences. Results: Prestudy outreach was conducted with children, parents, nurses, administrators, and pediatricians. Of the 691 Framingham families with a fifth grader (aged 10-11), 443 consented to complete surveys and undergo digital photography and dermoscopy during the school's routine scoliosis testing. Of the 443 families providing consent, 369 agreed to genetic testing. We identified key factors to consider when implementing school-based studies: (a) pilot studies to demonstrate feasibility, (b) inclusion of school administration and parents, (c) grassroots approach with multiple contacts, and (d) embedding research studies within preexisting school health services. Conclusions: Launching an observational study within the school environment required an academic/school collaboration across numerous disciplines including dermatology, epidemiology, genetics, medical photography, school health, community health education, and most notably, the need for the presence of a full-time study nurse in the school. A large school system proved to be an excellent resource to conduct this first prospective study on the evolution of moles in US schoolchildren. The key challenges and lessons learned may be applicable to other investigators launching school-based initiatives. [ABSTRACT FROM AUTHOR]

Patients with multiple nevi are at greater risk for developing melanoma but may be unable to assess their skin accurately without an effective tool. At Memorial Sloan-Kettering Cancer Center, the standard of practice has been to use a patient educational brochure teaching high-risk patients mole mapping a procedure of recording moles by drawing them. The photo print book offers patients an improved way of performing skin self-examination (SSE) in comparison to drawing. The purpose of this study was to determine patient experiences through patient interview and to learn ways to increase patient compliance and understanding of monthly SSE performance using a photo book. [ABSTRACT FROM AUTHOR]

AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world. De-identified images were systematically extracted from sensitive 3D Total Body Photographs and are comparable in optical resolution to smartphone images. Algorithms trained on lower quality images could improve clinical workflows and detect skin cancers earlier if deployed in primary care or non-clinical settings, where photos are captured by non-expert physicians or patients. Such a tool could prompt individuals to visit a specialized dermatologist. This dataset circumvents many inherent limitations of prior datasets and may be used to build upon previous applications of skin imaging for cancer detection., (© 2024. The Author(s).)

Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Clínic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation., (© 2024. The Author(s).)

Background: A common terminology for diagnosis is critically important for clinical communication, education, research and artificial intelligence. Prevailing lexicons are limited in fully representing skin neoplasms., Objectives: To achieve expert consensus on diagnostic terms for skin neoplasms and their hierarchical mapping., Methods: Diagnostic terms were extracted from textbooks, publications and extant diagnostic codes. Terms were hierarchically mapped to super-categories (e.g. 'benign') and cellular/tissue-differentiation categories (e.g. 'melanocytic'), and appended with pertinent-modifiers and synonyms. These terms were evaluated using a modified-Delphi consensus approach. Experts from the International-Skin-Imaging-Collaboration (ISIC) were surveyed on agreement with terms and their hierarchical mapping; they could suggest modifying, deleting or adding terms. Consensus threshold was >75% for the initial rounds and >50% for the final round., Results: Eighteen experts completed all Delphi rounds. Of 379 terms, 356 (94%) reached consensus in round one. Eleven of 226 (5%) benign-category terms, 6/140 (4%) malignant-category terms and 6/13 (46%) indeterminate-category terms did not reach initial agreement. Following three rounds, final consensus consisted of 362 terms mapped to 3 super-categories and 41 cellular/tissue-differentiation categories., Conclusions: We have created, agreed upon, and made public a taxonomy for skin neoplasms and their hierarchical mapping. Further study will be needed to evaluate the utility and completeness of the lexicon., (© 2024 European Academy of Dermatology and Venereology.)

Competing Interests: Conflicts of interest The authors declare no conflicts of interest.

Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. In this study, we attempted to evaluate agreement among experts on exemplar images not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least 1 of 31 melanocytic-specific features were submitted by 25 world experts as exemplars. Using a web-based platform that allows for image markup of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with eight achieving excellent agreement (Gwet's AC >0.75) and seven of them being melanoma-specific features. These features were peppering/granularity (0.91), shiny white streaks (0.89), typical pigment network (0.83), blotch irregular (0.82), negative network (0.81), irregular globules (0.78), dotted vessels (0.77), and blue-whitish veil (0.76). By utilizing an exemplar dataset, a good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication, and machine learning experiments., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Competing Interests: Conflicts of interest Collin Costello, MD, has grant support from the Melanoma Research Alliance to support melanoma educational programs and research support from DeepX Health for melanoma imaging technology. Both were paid to the institution. Allan Halpern, MD, is an advisor to Canfield Scientific Inc and Scibase Inc.

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., (© 2023. The Author(s).)

Background: Existing artificial intelligence for melanoma detection has relied on analysing images of lesions of clinical interest, which may lead to missed melanomas. Tools analysing the entire skin surface are lacking., Objectives: To determine if melanoma can be distinguished from other skin lesions using data from automated analysis of 3D-images., Methods: Single-centre, retrospective, observational convenience sample of patients diagnosed with melanoma at a tertiary care cancer hospital. Eligible participants were those with a whole-body 3D-image captured within 90 days prior to the diagnostic skin biopsy. 3D-images were obtained as standard of care using VECTRA WB360 Whole Body 3-dimensional Imaging System (Canfield Scientific). Automated data from image processing (i.e. lesion size, colour, border) for all eligible participants were exported from VECTRA DermaGraphix research software for analysis. The main outcome was the area under the receiver operating characteristic curve (AUC)., Results: A total of 35 patients contributed 23,538 automatically identified skin lesions >2 mm in largest diameter (102-3021 lesions per participant). All were White patients and 23 (66%) were males. The median (range) age was 64 years (26-89). There were 49 lesions of melanoma and 22,489 lesions that were not melanoma. The AUC for the prediction model was 0.94 (95% CI: 0.92-0.96). Considering all lesions in a patient-level analysis, 14 (28%) melanoma lesions had the highest predicted score or were in the 99th percentile among all lesions for an individual patient., Conclusions: In this proof-of-concept pilot study, we demonstrated that automated analysis of whole-body 3D-images using simple image processing techniques can discriminate melanoma from other skin lesions with high accuracy. Further studies with larger, higher quality, and more representative 3D-imaging datasets would be needed to improve and validate these results., (© 2023 European Academy of Dermatology and Venereology.)

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., (©Jonathan Kentley, Jochen Weber, Konstantinos Liopyris, Ralph P Braun, Ashfaq A Marghoob, Elizabeth A Quigley, Kelly Nelson, Kira Prentice, Erik Duhaime, Allan C Halpern, Veronica Rotemberg. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 18.01.2023.)

Background: There have been no studies of the American Academy of Dermatology's SpotMe skin cancer screening program to collectively analyze and determine the factors associated with suspected basal cell carcinoma (BCC), squamous cell carcinoma (SCC), dysplastic nevus (DN), and cutaneous melanoma (CM) diagnoses., Objective: Describe the demographics, risk factors, and access to care profiles associated with suspected diagnoses of BCC, SCC, DN, and CM among first-time SpotMe screenees during 2009-2010., Methods: We conducted a cross-sectional analysis of data from the SpotMe skin cancer screenings conducted in 2009 and 2010. We performed multivariable logistic regression analysis for each diagnosis, incorporating standard demographic, access to care, and risk factor variables in the models., Results: Men, those without a regular dermatologist, persons reporting recently changing moles, and those with a personal history of melanoma were at increased risk for each of the suspected diagnoses analyzed. Uninsured persons were at increased risk for suspected malignancies (BCC, SCC, and CM)., Limitations: Lack of histologic confirmation for diagnoses and cross-sectional design., Conclusion: Among first-time SpotMe participants, suspected diagnoses of BCC, SCC, DN, and CM shared several associated factors, which may be considered when planning outreach and screening for populations at risk for skin cancer., (Copyright © 2019. Published by Elsevier Inc.)

Background: There is little understanding regarding the long-term natural history of melanocytic nevi among adults., Objective: The objective of the study was to describe the long-term natural history of individual nevi located on the torso of high-risk patients., Methods: All patients attending Memorial Sloan Kettering Cancer Center (MSKCC) who underwent two total body photography (TBP) sessions 15+ years apart were included ('retrospective' group). To account for a potential selection bias, we also included consecutive patients who had TBP 15+ years ago and consented to undergo follow-up TBP ('prospective' group). We compared baseline and follow-up torso images on the TBPs and evaluated the number of total, new and disappearing nevi; number of seborrheic keratoses and actinic keratoses; each nevus' diameter at both time points; each nevus' colour change; the presence of clinical atypia; and when dermoscopy was available, the dermoscopic features at each time point., Results: One hundred six patients were included in the study. Although the average age of the patients was 40 at baseline TBP, most patients developed new nevi between imaging sessions (median 16.4 years) with an average of 2.6 (SD = 4.8) nevi per participant. The average number of disappearing nevi was 0.3 (SD = 0.6). In addition, 62/106 (58%) patients had an absolute increase, and 9/106 (8%) patients had an absolute decrease in their total nevus count. Roughly half (49%: 1416/2890) of the nevi that could be evaluated at both time points increased in diameter by at least 25%. Only 6% (159/2890) of nevi shrunk in diameter by at least 25%. Patients with a history of melanoma had a higher rate of disappearing nevi, and their nevi were more likely to grow. Most nevi demonstrated no significant dermoscopic changes., Conclusions: High-risk patients acquire new nevi throughout life with very few nevi disappearing over time. Contrary to prior reports, most nevi in adults increase in diameter, while few nevi shrink., (© 2022 The Authors. Journal of the European Academy of Dermatology and Venereology published by John Wiley & Sons Ltd on behalf of European Academy of Dermatology and Venereology.)

On the basis of the clinical impression and current knowledge, acquired melanocytic nevi and melanomas may not occur in random localizations. The goal of this study was to identify whether their distribution on the back is random and whether the location of melanoma correlates with its adjacent lesions. Therefore, patient-level and lesion-level spatial analyses were performed using the Clark‒Evans test for complete spatial randomness. A total of 311 patients with three-dimensional total body photography (average age of 40.08 [30‒49] years; male/female ratio: 128/183) with 5,108 eligible lesions in total were included in the study (mean sum of eligible lesions per patient of 16.42 [3‒199]). The patient-level analysis revealed that the distributions of acquired melanocytic neoplasms were more likely to deviate toward clustering than dispersion (average z-score of ‒0.55 [95% confidence interval = ‒0.69 to ‒0.41; P < 0.001]). The lesion-level analysis indicated a higher portion of melanomas (n = 57 of 72, 79.2% [95% confidence interval = 69.4‒88.9%]) appearing in proximity to neighboring melanocytic neoplasms than to nevi (n = 2,281 of 5,036, 45.3% [95% confidence interval = 43.9‒46.7%]). In conclusion, the nevi and melanomas' distribution on the back tends toward clustering as opposed to dispersion. Furthermore, melanomas are more likely to appear proximally to their neighboring neoplasms than to nevi. These findings may justify various oncogenic theories and improve diagnostic methodology., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Despite the increasing ubiquity and accessibility of teledermatology applications, few studies have comprehensively surveyed their features and technical standards. Importantly, features implemented after the point of capture are often intended to augment image utilization, while technical standards affect interoperability with existing healthcare systems. We aim to comprehensively survey image utilization features and technical characteristics found within publicly discoverable digital skin imaging applications., Materials and Methods: Applications were identified and categorized as described in Part I. Included applications were then further assessed by three independent reviewers for post-imaging content, tools, and functionality. Publicly available information was used to determine the presence or absence of relevant technology standards and/or data characteristics., Results: A total of 20 post-image acquisition features were identified across three general categories: (1) metadata attachment, (2) functional tools (i.e., those that utilized images or in-app content to perform a user-directed function), and (3) image processing. Over 80% of all applications implemented metadata features, with nearly half having metadata features only. Individual feature occurred and feature richness varied significantly by primary audience (p < 0.0001) and function (p < 0.0001). On average, each application included under three features. Less than half of all applications requested consent for user-uploaded photos and fewer than 10% provided clear data use and privacy policies., Conclusion: Post-imaging functionality in skin imaging applications varies significantly by primary audience and intended function, though nearly all applications implemented metadata labeling. Technical standards are often not implemented or reported consistently. Gaps in the provision of clear consent, data privacy, and data use policies should be urgently addressed., (© 2022 The Authors. Skin Research and Technology published by John Wiley & Sons Ltd.)

Complementary and alternative medicine or therapies (CAM) are frequently used by skin cancers patients. Patient's self-administration of CAM in melanoma can reach up to 40%-50%. CAMs such as botanical agents, phytochemicals, herbal formulas ("black salve") and cannabinoids, among others, have been described in skin cancer patients. The objective of this review article was to acknowledge the different CAM for skin cancers through the current evidence, focusing on biologically active CAM rather than mind-body approaches. We searched MEDLINE database for articles published through July 2022, regardless of study design. Of all CAMs, phytochemicals have the best in vitro evidence-supporting efficacy against skin cancer including melanoma; however, to date, none have proved efficacy on human patients. Of the phytochemicals, Curcumin is the most widely studied. Several findings support Curcumin efficacy in vitro through various molecular pathways, although most studies are in the preliminary phase. In addition, the use of alternative therapies is not exempt of risks physicians should be aware of their adverse effects, interactions with standard treatments, and possible complications arising from CAM usage. There is emerging evidence for CAM use in skin cancer, but no human clinical trials support the effectiveness of any CAM in the treatment of skin cancer to date. Nevertheless, patients worldwide frequently use CAM, and physicians should educate themselves on currently available CAMs., (© 2022 Wiley Periodicals LLC.)

Introduction: In patients with multiple nevi, sequential imaging using total body skin photography (TBSP) coupled with digital dermoscopy (DD) documentation reduces unnecessary excisions and improves the early detection of melanoma. Correct patient selection is essential for optimizing the efficacy of this diagnostic approach., Objectives: The purpose of the study was to identify, via expert consensus, the best indications for TBSP and DD follow-up., Methods: This study was performed on behalf of the International Dermoscopy Society (IDS). We attained consensus by using an e-Delphi methodology. The panel of participants included international experts in dermoscopy. In each Delphi round, experts were asked to select from a list of indications for TBSP and DD., Results: Expert consensus was attained after 3 rounds of Delphi. Participants considered a total nevus count of 60 or more nevi or the presence of a CDKN2A mutation sufficient to refer the patient for digital monitoring. Patients with more than 40 nevi were only considered an indication in case of personal history of melanoma or red hair and/or a MC1R mutation or history of organ transplantation., Conclusions: Our recommendations support clinicians in choosing appropriate follow-up regimens for patients with multiple nevi and in applying the time-consuming procedure of sequential imaging more efficiently. Further studies and real-life data are needed to confirm the usefulness of this list of indications in clinical practice., Competing Interests: Competing interests: None., (©2022 Russo et al.)

Competing Interests: Conflicts of interest Drs Yamada and Prow have a conflict of interest with Trajan Scientific & Medical Pty Ltd as they are currently commercializing microbiopsy. Drs Jain, Busam, Marchetti, and Halpern and authors Autuori, Everett, Harris, and Orlow do not have any conflicts of interest to disclose.

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., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Testicular germ-cell tumours (TGCT) are the most common cancer among young adult men. Previous studies suggested TGCT survivors have an increased risk for skin cancer. The goal of this study was to systematically review the literature and evidence regarding skin cancer risk among TGCT survivors compared with the general population. PubMed, EMBASE, Web of Science, Cochrane Databases and reference lists were included in the search. A systematic review of all comparative studies with more than 10 TGCT survivors reporting on skin cancer incidence was performed. A meta-analysis of the Standardized Incidence Rate (SIR) was calculated by pooling study-specific log-transformed estimates using the random-effects model. Risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale. Nineteen studies that reported on 147 935 TGCT survivors were included. Pooled SIR for skin cancer and for melanoma incidence among TGCT survivors were 1.93 (95% CI 1.62-2.29, P < 0.0001) and 1.81 (95% CI 1.57-2.08, P < 0.0001), respectively. In conclusion, compared to the general population, TGCT survivors have an increased risk for developing skin cancer and melanoma. Additional long-term studies that include TGCT survivors, additional risk factors and all subtypes of skin cancer are required., (© 2022 European Academy of Dermatology and Venereology.)

Background: The rapid adoption of digital skin imaging applications has increased the utilization of smartphone-acquired images in dermatology. While this has enormous potential for scaling the assessment of concerning skin lesions, the insufficient quality of many consumer/patient-taken images can undermine clinical accuracy and potentially harm patients due to lack of diagnostic interpretability. We aim to characterize the current state of digital skin imaging applications and comprehensively assess how image acquisition features address image quality., Materials and Methods: Publicly discoverable mobile, web, and desktop-based skin imaging applications, identified through keyword searches in mobile app stores, Google Search queries, previous teledermatology studies, and expert recommendations were independently assessed by three reviewers. Applications were categorized by primary audience (consumer-facing, nonhospital-based practice, or enterprise/health system), function (education, store-and-forward teledermatology, live-interactive teledermatology, electronic medical record adjunct/clinical imaging storage, or clinical triage), in-app connection to a healthcare provider (yes or no), and user type (patient, provider, or both)., Results: Just over half (57%) of 191 included skin imaging applications had at least one of 14 image acquisition technique features. Those that were consumer-facing, intended for educational use, and designed for both patient and physician users had significantly greater feature richness (p < 0.05). The most common feature was the inclusion of text-based imaging tips, followed by the requirement to submit multiple images and body area matching., Conclusion: Very few skin imaging applications included more than one image acquisition technique feature. Feature richness varied significantly by audience, function, and user categories. Users of digital dermatology tools should consider which applications have standardized features that improve image quality., (© 2022 The Authors. Skin Research and Technology published by John Wiley & Sons Ltd.)

Seborrheic keratoses (SKs) are ubiquitous, generally benign skin tumors that exhibit high clinical variability. While age is a known risk factor, the precise roles of UV exposure and immune abnormalities are currently unclear. The underlying mechanisms of this benign disorder are paradoxically driven by oncogenic mutations and may have profound implications for our understanding of the malignant state. Advances in molecular pathogenesis suggest that inhibition of Akt and APP, as well as existing treatments for skin cancer, may have therapeutic potential in SK. Dermoscopic criteria have also become increasingly important to the accurate detection of SK, and other noninvasive diagnostic methods, such as reflectance confocal microscopy and optical coherence tomography, are rapidly developing. Given their ability to mimic malignant tumors, SK cases are often used to train artificial intelligence-based algorithms in the computerized detection of skin disease. These technologies are becoming increasingly accurate and have the potential to significantly augment clinical practice. Current treatment options for SK cause discomfort and can lead to adverse post-treatment effects, especially in skin of color. In light of the discontinuation of ESKATA in late 2019, promising alternatives, such as nitric-zinc and trichloroacetic acid topicals, should be further developed. There is also a need for larger, head-to-head trials of emerging laser therapies to ensure that future treatment standards address diverse patient needs., (© 2021 S. Karger AG, Basel.)

Background: Melanoma screening includes the assessment of changes in melanocytic lesions using images. However, previous studies of normal nevus temporal changes showed variable results and the optimal method for evaluating these changes remains unclear. Our aim was to evaluate the reproducibility of (a) nevus count done at a single time point (method I) versus two time points (method II); and (b) manual and automated nevus diameter measurements., Materials and Methods: In a first experiment, participants used either a single time point or a two time point annotation method to evaluate the total number and size of nevi on the back of an atypical mole syndrome patient. A Monte Carlo simulation was used to calculate the variance observed. In a second experiment, manual measurements of nevi on 2D images were compared to an automated measurement on 3D images. Percent difference in the paired manual and automated measurements was calculated., Results: Mean nevus count was 137 in method I and 115.5 in method II. The standard deviation was greater in method I (38.80) than in method II (4.65) (p = 0.0025). Manual diameter measurements had intraclass correlation coefficient of 0.88. The observed mean percent difference between manual and automated diameter measurements was 1.5%. Lightly pigmented and laterally located nevi had a higher percent difference., Conclusions: Comparison of nevi from two different time points is more consistent than nevus count performed separately at each time point. In addition, except for selected cases, automated measurements of nevus diameter on 3D images can be used as a time-saving reproducible substitute for manual measurement on 2D images., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Basal cell carcinoma (BCC) is the most common skin cancer, with over 2 million cases diagnosed annually in the United States. Conventionally, BCC is diagnosed by naked eye examination and dermoscopy. Suspicious lesions are either removed or biopsied for histopathological confirmation, thus lowering the specificity of noninvasive BCC diagnosis. Recently, reflectance confocal microscopy, a noninvasive diagnostic technique that can image skin lesions at cellular level resolution, has shown to improve specificity in BCC diagnosis and reduced the number needed to biopsy by 2-3 times. In this study, we developed and evaluated a deep learning-based artificial intelligence model to automatically detect BCC in reflectance confocal microscopy images. The proposed model achieved an area under the curve for the receiver operator characteristic curve of 89.7% (stack level) and 88.3% (lesion level), a performance on par with that of reflectance confocal microscopy experts. Furthermore, the model achieved an area under the curve of 86.1% on a held-out test set from international collaborators, demonstrating the reproducibility and generalizability of the proposed automated diagnostic approach. These results provide a clear indication that the clinical deployment of decision support systems for the detection of BCC in reflectance confocal microscopy images has the potential for optimizing the evaluation and diagnosis of patients with skin cancer., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Multiple studies have reported on dermoscopic structures in basal cell carcinoma (BCC) and its subtypes, with varying results., Objective: To systematically review the prevalence of dermoscopic structures in BCC and its subtypes., Methods: Databases and reference lists were searched for relevant trials according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were assessed for the relative proportion of BCC dermoscopic features. Random-effects models were used to estimate summary effect sizes., Results: Included were 31 studies consisting of 5950 BCCs. The most common dermoscopic features seen in BCC were arborizing vessels (59%), shiny white structures (49%), and large blue-grey ovoid nests (34%). Arborizing vessels, ulceration, and blue-grey ovoid nests and globules were most common in nodular BCC; short-fine telangiectasia, multiple small erosions, and leaf-like, spoke wheel and concentric structures in superficial BCC; porcelain white areas and arborizing vessels in morpheaform BCC; and arborizing vessels and ulceration in infiltrative BCC., Limitations: Studies had significant heterogeneity. Studies reporting BCC histopathologic subtypes did not provide clinical data on pigmentation of lesions., Conclusion: In addition to arborizing vessels, shiny white structures are a common feature of BCC. A constellation of dermoscopic features may aid in differentiating between BCC histopathologic subtypes., (Copyright © 2019 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.)

Background: There is lack of uniformity in the reflectance confocal microscopy (RCM) terminology for melanocytic lesions., Objective: To review published RCM terms for melanocytic lesions and identify redundant, synonymous terms., Methods: A systematic review of original research articles adhering to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines was conducted until August 15, 2018. Two investigators gathered all published RCM terms used to describe melanoma and melanocytic nevi. Synonymous terms were grouped based on similarity in definition and in histopathologic correlation., Results: Out of 156 full-text screened articles, 59 studies met the inclusion criteria. We identified 209 terms; 191 (91.4%) corresponding to high-magnification/cellular-level terms and 18 (8.6%) corresponding to low-magnification/architectural patterns terms. The overall average use frequency of RCM terms was 3.1 times (range, 1-31). By grouping of individual RCM terms based on likely synonymous definitions and by eliminating terms lacking clear definition, the total number of RCM terms could be potentially reduced from 209 to 40 terms (80.8% reduction)., Limitations: Non-English and non-peer-reviewed articles were excluded., Conclusions: This systematic review of published RCM terms identified significant terminology redundancy. It provides the basis for subsequent terminology consensus on melanocytic neoplasms., (Copyright © 2020 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.)

Background: There is no internationally vetted set of anatomic terms to describe human surface anatomy., Objective: To establish expert consensus on a standardized set of terms that describe clinically relevant human surface anatomy., Methods: We conducted a Delphi consensus on surface anatomy terminology between July 2017 and July 2019. The initial survey included 385 anatomic terms, organized in seven levels of hierarchy. If agreement exceeded the 75% established threshold, the term was considered 'accepted' and included in the final list. Terms added by the participants were passed on to the next round of consensus. Terms with <75% agreement were included in subsequent surveys along with alternative terms proposed by participants until agreement was reached on all terms., Results: The Delphi included 21 participants. We found consensus (≥75% agreement) on 361/385 (93.8%) terms and eliminated one term in the first round. Of 49 new terms suggested by participants, 45 were added via consensus. To adjust for a recently published International Classification of Diseases-Surface Topography list of terms, a third survey including 111 discrepant terms was sent to participants. Finally, a total of 513 terms reached agreement via the Delphi method., Conclusions: We have established a set of 513 clinically relevant terms for denoting human surface anatomy, towards the use of standardized terminology in dermatologic documentation., (© 2020 European Academy of Dermatology and Venereology.)