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POT1 is the second most frequently reported gene (after CDKN2A ) in familial melanoma. Pathogenic variants are associated with earlier onset and/or multiple primary melanomas (MPMs). To date, POT1 phenotypical reports have been largely restricted to associated malignancies, and description of the dermatological landscape has been limited. We identified 10 variants in n=18 of 384 (4.7%) unrelated individuals (n=13 MPMs; n=5 single primary melanomas) of European ancestry. Five variants were rare (minor allele frequency <0.001) or novel (two loss-of-function (LOF), one splice acceptor and two missense) and were predicted to be functionally significant, in five unrelated probands with MPMs (≥3 melanomas). We performed three-dimensional total body photography on both individuals with confirmed pathogenic LOF variants to characterise the dermatological phenotype. Total body naevus counts (≥2 mm diameter) were significantly higher (p=7.72 ×10-12 ) in carriers compared with a control population. Majority of naevi were on the probands' back and lower limb regions, where only mild to moderate ultraviolet (UV) damage was observed. Conversely, the head/neck region, where both probands exhibited severe UV damage, had comparably fewer naevi. We hypothesise that carriage of functionally significant POT1 variants is associated with increased naevus counts generally, and naevi >5 mm in diameter specifically and the location of these are independent of UV damage., Competing Interests: Competing interests: PS is a shareholder of MoleMap Limited and e-derm consult and undertakes regular teledermatological reporting for both companies. PS is a medical consultant for Canfield Scientific and Blaze Bioscience MoleMap Australia Limited, and a medical advisor for First Derm. No other authors have conflicts to declare., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

MITF E318K moderates melanoma risk. Only five MITF E318K homozygous cases have been reported to date, one in association with melanoma. This novel report uses 3D total-body-photography (TBP) to describe the dermatological phenotype of a homozygous MITF E318K individual. The case, a 32-year-old male, was diagnosed with his first of six primary melanomas at 26 years of age. Five melanomas were located on the back and one in the groin. Two were superficial spreading. Three arose from pre-existing naevi and one was a rare naevoid melanoma. 3D-TBP revealed a high naevus count (n = 162) with pigmentation varying from light to dark. Most naevi generally (n = 90), and large (>5 mm diameter) and clinically atypical naevi specifically were located on the back where sun damage was mild. In contrast, naevi count was low (n = 25 total) on the head/neck and lower limbs where sun damage was severe. Thus, melanoma location correlated with naevi density, rather than degree of sun damage. In addition to the MITF E318K homozygosity, there was heterozygosity for four other moderate-risk variants, which may contribute to melanoma risk. Further research is warranted to explore whether melanomas in E318K heterozygous and other homozygotes coincide with regions of high naevi density as opposed to sun damage. This could inform future melanoma screening/surveillance., (© 2023 The Authors. Pigment Cell & Melanoma Research published by John Wiley & Sons Ltd.)

Competing Interests: Competing interests: PS is a shareholder of MoleMap Limited and e-derm consult and undertakes regular teledermatological reporting for both companies. PS is a medical consultant for Canfield Scientific, Blaze Bioscience and MoleMap Australia Limited, and a medical advisor for First Derm.

Artificial intelligence algorithms to classify melanoma are dependent on their training data, which limits generalizability. The objective of this study was to compare the performance of an artificial intelligence model trained on a standard adult-predominant dermoscopic dataset before and after the addition of additional pediatric training images. The performances were compared using held-out adult and pediatric test sets of images. We trained two models: one (model A) on an adult-predominant dataset (37,662 images from the International Skin Imaging Collaboration) and the other (model A+P) on an additional 1,536 pediatric images. We compared performance between the two models on adult and pediatric held-out test images separately using the area under the receiver operating characteristic curve. We then used Gradient-weighted Class Activation Maps and background skin masking to understand the contributions of the lesion versus background skin to algorithm decision making. Adding images from a pediatric population with different epidemiological and visual patterns to current reference standard datasets improved algorithm performance on pediatric images without diminishing performance on adult images. This suggests a way that dermatologic artificial intelligence models can be made more generalizable. The presence of background skin was important to the pediatric-specific improvement seen between models. Our study highlights the importance of carefully curated and labeled data from diverse inputs to improve the generalizability of AI models for dermatology, in this case applied to dermoscopic images of adult and pediatric lesions to improve melanoma detection., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Population-wide screening for melanoma is not cost-effective, but genetic characterization could facilitate risk stratification and targeted screening. Common Melanocortin-1 receptor (MC1R) red hair colour (RHC) variants and Microphthalmia-associated transcription factor (MITF) E318K separately confer moderate melanoma susceptibility, but their interactive effects are relatively unexplored., Objectives: To evaluate whether MC1R genotypes differentially affect melanoma risk in MITF E318K+ vs. E318K- individuals., Materials and Methods: Melanoma status (affected or unaffected) and genotype data (MC1R and MITF E318K) were collated from research cohorts (five Australian and two European). In addition, RHC genotypes from E318K+ individuals with and without melanoma were extracted from databases (The Cancer Genome Atlas and Medical Genome Research Bank, respectively). χ2 and logistic regression were used to evaluate RHC allele and genotype frequencies within E318K+/- cohorts depending on melanoma status. Replication analysis was conducted on 200 000 general-population exomes (UK Biobank)., Results: The cohort comprised 1165 MITF E318K- and 322 E318K+ individuals. In E318K- cases MC1R R and r alleles increased melanoma risk relative to wild type (wt), P < 0.001 for both. Similarly, each MC1R RHC genotype (R/R, R/r, R/wt, r/r and r/wt) increased melanoma risk relative to wt/wt (P < 0.001 for all). In E318K+ cases, R alleles increased melanoma risk relative to the wt allele [odds ratio (OR) 2.04 (95% confidence interval 1.67-2.49); P = 0.01], while the r allele risk was comparable with the wt allele [OR 0.78 (0.54-1.14) vs. 1.00, respectively]. E318K+ cases with the r/r genotype had a lower but not significant melanoma risk relative to wt/wt [OR 0.52 (0.20-1.38)]. Within the E318K+ cohort, R genotypes (R/R, R/r and R/wt) conferred a significantly higher risk compared with non-R genotypes (r/r, r/wt and wt/wt) (P < 0.001). UK Biobank data supported our findings that r did not increase melanoma risk in E318K+ individuals., Conclusions: RHC alleles/genotypes modify melanoma risk differently in MITF E318K- and E318K+ individuals. Specifically, although all RHC alleles increase risk relative to wt in E318K- individuals, only MC1R R increases melanoma risk in E318K+ individuals. Importantly, in the E318K+ cohort the MC1R r allele risk is comparable with wt. These findings could inform counselling and management for MITF E318K+ individuals., Competing Interests: Conflicts of interest H.P.S. is a shareholder of MoleMap NZ Limited and e-derm consult GmbH, and undertakes regular teledermatological reporting for both companies; he is also a Medical Consultant for Canfield Scientific Inc., Blaze Bioscience Inc., MoleMap Australia Pty Limited, and a Medical Advisor for First Derm and Revenio Research Oy. The other authors state no conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of British Association of Dermatologists.)

Background: While skin cancers are less prevalent in people with skin of color, they are more often diagnosed at later stages and have a poorer prognosis. The use of artificial intelligence (AI) models can potentially improve early detection of skin cancers; however, the lack of skin color diversity in training datasets may only widen the pre-existing racial discrepancies in dermatology., Objective: The aim of this study was to systematically review the technique, quality, accuracy, and implications of studies using AI models trained or tested in populations with skin of color for classification of pigmented skin lesions., Methods: PubMed was used to identify any studies describing AI models for classification of pigmented skin lesions. Only studies that used training datasets with at least 10% of images from people with skin of color were eligible. Outcomes on study population, design of AI model, accuracy, and quality of the studies were reviewed., Results: Twenty-two eligible articles were identified. The majority of studies were trained on datasets obtained from Chinese (7/22), Korean (5/22), and Japanese populations (3/22). Seven studies used diverse datasets containing Fitzpatrick skin type I-III in combination with at least 10% from black Americans, Native Americans, Pacific Islanders, or Fitzpatrick IV-VI. AI models producing binary outcomes (e.g., benign vs. malignant) reported an accuracy ranging from 70% to 99.7%. Accuracy of AI models reporting multiclass outcomes (e.g., specific lesion diagnosis) was lower, ranging from 43% to 93%. Reader studies, where dermatologists' classification is compared with AI model outcomes, reported similar accuracy in one study, higher AI accuracy in three studies, and higher clinician accuracy in two studies. A quality review revealed that dataset description and variety, benchmarking, public evaluation, and healthcare application were frequently not addressed., Conclusions: While this review provides promising evidence of accurate AI models in populations with skin of color, the majority of the studies reviewed were obtained from East Asian populations and therefore provide insufficient evidence to comment on the overall accuracy of AI models for darker skin types. Large discrepancies remain in the number of AI models developed in populations with skin of color (particularly Fitzpatrick type IV-VI) compared with those of largely European ancestry. A lack of publicly available datasets from diverse populations is likely a contributing factor, as is the inadequate reporting of patient-level metadata relating to skin color in training datasets., (© 2023 The Author(s). Published by S. Karger AG, Basel.)

Systematic literature searches on POT1/POLE/BAP1 found that limited skin phenotypic characteristics have been documented in mutation carriers; 248 variants were annotated, and high-cluster variant regions associated with cutaneous melanoma were found in all three genes. Genotype-phenotype correlations can be used to identify patient disease predisposition based on mutation position and cluster regions., Competing Interests: Declaration of interests H.P.S. is a shareholder of MoleMap NZ Limited and e-derm consult GmbH, and undertakes regular teledermatological reporting for both companies. H.P.S. is also a Medical Consultant for Canfield Scientific Inc, MoleMap Australia Pty Ltd, Blaze Bioscience Inc, Revenio Research Oy and a Medical Advisor for First Derm. The other authors have no conflicts of interest to declare., (Crown Copyright © 2022. Published by Elsevier Ltd. All rights reserved.)

Background: Timely diagnosis is the cornerstone of melanoma morbidity and mortality reduction. 2D total body photography and dermoscopy are routinely used to assist with early detection of skin malignancies. Polarized 3D total body photography is a novel technique that enables fast image acquisition of almost the entire skin surface. We aimed to determine the added value of 3D total body photography alongside dermoscopy for monitoring cutaneous lesions., Methods: Lesion images from high-risk individuals were assessed for long-term substantial changes via dermoscopy and 3D total body photography. Three case studies are presented demonstrating how 3D total body photography may enhance lesion analysis alongside traditional dermoscopy., Results: 3D total body photography can assist clinicians by presenting cutaneous lesions in their skin ecosystem, thereby providing additional clinical context and enabling a more holistic assessment to aid dermoscopy interpretation. For lesion cases where previous dermoscopy is unavailable, corresponding 3D images can substitute for baseline dermoscopy. Additionally, 3D total body photography is not susceptible to artificial stretch artefacts., Conclusion: 3D total body photography is valuable alongside dermoscopy for monitoring cutaneous lesions. Furthermore, it is capable of surveilling almost the entire skin surface, including areas not traditionally monitored by sequential imaging., (© 2021 The Author(s). Published by S. Karger AG, Basel.)

Background: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice., Objective: The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians., Methods: PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included., Results: A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images., Conclusions: All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: J.S.U. is on the advisory board or has received honoraria and travel support from Amgen, Bristol Myers Squibb, GSK, LEO Pharma, Merck Sharp and Dohme, Novartis, Pierre Fabre and Roche, outside the submitted work. M.G. has received speaker's honoraria and/or has served as a consultant and/or member of advisory boards for Almirall, Argenx, Biotest, Eli Lilly, Janssen Cilag, LEO Pharma, Novartis and UCB, outside the submitted work. H.A.H. worked as a consultant or received honoraria and travel support from Heine Optotechnik GmbH, JenLab GmbH, FotoFinder Systems GmbH, Magnosco GmbH, SciBase AB, Beiersdorf AG, Almirall Hermal GmbH and Galderma Laboratorium GmbH. V.M.R. is on the advisory board or has received honoraria or ownership in Inhabit Brands, Inc. unrelated to this work. Sondermann W. reports grants from medi GmbH Bayreuth, personal fees from Janssen, grants and personal fees from Novartis, personal fees from Lilly, personal fees from UCB, personal fees from Almirall, personal fees from LEO Pharma and personal fees from Sanofi Genzyme, outside the submitted work. H.P.S. is a shareholder of MoleMap NZ Limited and e-derm consult GmbH and undertakes regular tele-dermatological reporting for both companies. H.P.S. is a medical consultant for Canfield Scientific, Inc., MoleMap Australia Pty Ltd and Revenio Research Oy and a medical advisor for First Derm. M.L-V. has received speaker's honoraria and/or received grants and/or participated in clinical trials of AbbVie, Almirall, Amgen, Celgene, Eli Lilly, Janssen Cilag, LEO Pharma, Novartis and UCB, outside the submitted work. A.Z. has been an advisor and/or received speaker's honoraria and/or received grants and/or participated in clinical trials of AbbVie, Almirall, Amgen, Beiersdorf Dermo Medical, Bencard Allergy, Celgene, Eli Lilly, Janssen Cilag, LEO Pharma, Novartis, Sanofi-Aventis and UCB Pharma, outside the submitted work. Kittler H. received speaker's honoraria from FotoFinder Systems GmbH and received non-financial support from Heine Optotechnik GmbH, Derma Medical and 3Gen. T.J.B. reports owning a company that develops mobile apps, including the teledermatology services AppDoc (https://online-hautarzt.de) and Intimarzt (https://Intimarzt.de); Smart Health Heidelberg GmbH, Handschuhsheimer Landstr. 9/1, 69120 Heidelberg, https://smarthealth.de. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.

Introduction: Melanoma is Australia's fourth most common cancer. Early detection is fundamental in maximising health outcomes and minimising treatment costs. To date, population-based screening programmes have not been justified in health economic studies. However, a skin surveillance approach targeting high-risk individuals could improve the cost-benefit ratio., Methods and Analysis: This paper describes a 2-year longitudinal randomised controlled trial (RCT) to compare routine clinical care (control) with an intensive skin surveillance programme (intervention) consisting of novel three-dimensional (3D) total-body photography (TBP), sequential digital dermoscopy and melanoma-risk stratification, in a high-risk melanoma cohort. Primary outcomes will evaluate clinical, economic and consumer impact of the intervention. Clinical outcomes will evaluate differences in the rate of lesion excisions/biopsies per person, benign to malignant ratio for excisions and thickness of melanomas diagnosed. A health economic analysis using government data repositories will capture healthcare utilisation and costs relating to skin surveillance. Consumer questionnaires will examine intervention acceptability, the psychological impact, and attitudes towards melanoma risk and sun protective behaviour. Secondary outcomes include the development of a holistic risk algorithm incorporating clinical, phenotypic and genetic factors to facilitate the identification of those most likely to benefit from this surveillance approach. Furthermore, the feasibility of integrating the intervention with teledermatology to enhance specialist care in remote locations will be evaluated. This will be the first RCT to compare a targeted surveillance programme utilising new 3D TBP technology against current routine clinical care for individuals at high risk of melanoma., Ethics and Dissemination: This study has received Human Research Ethics Committee (HREC) approval from both Metro South Health HREC (HREC/17/QPAH/816) and The University of Queensland HREC (2018000074)., Trial Registration Number: ANZCTR12618000267257; Pre-results., Competing Interests: Competing interests: HPS is a shareholder of MoleMap NZ Limited and e-derm consult GmbH, and undertakes regular teledermatological reporting for both companies. HPS is a Medical Consultant for Canfield Scientific Inc., a Medical Advisor for First Derm, and has a Medical Advisory Board Appointment with MoleMap NZ Limited., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)