POT1 and multiple primary melanomas: the dermatological phenotype.

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.

Examining labelling guidelines for AI-based software as a medical device: A review and analysis of dermatology mobile applications in Australia.

In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI-based SaMD mobile applications to determine usage of these labels. Of the 21 AI-based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification.

Introduction: Artificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with 'untrained' or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process. Methods: This protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data. Conclusion: The anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer.

Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD): A consensus statement.

BACKGROUND/OBJECTIVES: Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. METHODS: Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. RESULTS: There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. CONCLUSIONS: This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.

A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography.

Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clinical approach where skin phenotype and clinical background information are considered. We review the current state of AI for skin lesion classification and present opportunities and challenges when applied to total body photography (TBP). AI in TBP analysis presents opportunities for intrapatient assessment of skin phenotype and holistic risk assessment by incorporating patient-level metadata, although challenges exist for protecting patient privacy in algorithm development and improving explainable AI methods.

Genetic testing for familial melanoma.

While the average lifetime risk of melanoma worldwide is approximately 3%, those with inherited high-penetrance mutations face an increased lifetime risk of 52-84%. In countries of low melanoma incidence, such as in Southern Europe, familial melanoma genetic testing may be warranted when there are two first degree relatives with a melanoma diagnosis. Testing criteria for high incidence countries such as USA, or with very-high incidence, such as Australia and New Zealand, would require a threshold of 3 to 4 affected family members. A mutation in the most common gene associated with familial melanoma, CDKN2A, is identified in approximately 10-40% of those meeting testing criteria. However, the use of multi-gene panels covering additional less common risk genes can significantly increase the diagnostic yield. Currently, genetic testing for familial melanoma is typically conducted by qualified genetic counsellors, however with increasing demand on testing services and high incidence rate in certain countries, a mainstream model should be considered. With appropriate training, dermatologists are well placed to identify high risk individuals and offer melanoma genetic test in dermatology clinics. Genetic testing should be given in conjunction with pre- and post-test consultation. Informed patient consent should cover possible results, the limitations and implications of testing including inconclusive results, and potential for genetic discrimination. Previous studies reporting on participant outcomes of genetic testing for familial melanoma have found significant improvements in both sun protective behavior and screening frequency in mutation carriers.

Assessing the genetic risk of nodular melanoma using a candidate gene approach.

BACKGROUND: Nodular melanoma (NM) is a challenge to diagnose early due to its rapid growth and more atypical clinical presentation, making it the largest contributor to melanoma mortality. OBJECTIVES: Our study aim was to perform a rare-variant allele (RVA) analysis of whole-exome sequencing of patients with NM and non-NM (minor allele frequency ≤ 1% non-Finnish European) for a set of 500 candidate genes potentially implicated in melanoma. METHODS: This study recruited 131 participants with NM and 194 with non-NM from South-east Queensland and patients with NM from Victoria to perform a comparative analysis of possible genetic differences or similarities between the two melanoma cohorts. RESULTS: Phenotypic analysis revealed that a majority of patients diagnosed with NM were older males with a higher frequency of fair skin and red hair than is seen in the general population. The distribution of common melanoma polygenic risk scores was similar in patients with NM and non-NM, with over 28% in the highest quantile of scores. There was also a similar frequency of carriage of familial/high-penetrant melanoma gene and loss-of-function variants. We identified 39 genes by filtering 500 candidate genes based on the greatest frequency in NM compared with non-NM cases. The genes with RVAs of greatest frequency in NM included PTCH1, ARID2 and GHR. Rare variants in the SMO gene, which interacts with PTCH1 as ligand and receptor, were also identified, providing evidence that the Hedgehog pathway may contribute to NM risk. There was a cumulative effect in carrying multiple rare variants in the NM-associated genes. A 14.8-fold increased ratio for NM compared with non-NM was seen when two RVAs of the 39 genes were carried by a patient. CONCLUSIONS: This study highlights the importance of considering frequency of RVA to identify those at risk of NM in addition to known high penetrance genes.

Position statement of the EADV Artificial Intelligence (AI) Task Force on AI-assisted smartphone apps and web-based services for skin disease.

BACKGROUND: As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer. OBJECTIVE: This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection. METHODS: An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance. RESULTS: Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users. CONCLUSIONS: The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.

Expert Agreement on the Presence and Spatial Localization of Melanocytic Features in Dermoscopy.

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.

MITF E318K: A rare homozygous case with multiple primary melanoma.

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.

The Skin Molecular Ecosystem Holds the Key to Nevogenesis and Melanomagenesis.

Early detection of melanoma is critical to good patient outcomes, but we still know little about the mechanisms of early melanoma development. Normal epidermis has many of the sequence variants and genetic architecture disruptions found in both benign nevi, melanomas, and other skin cancers, yet continues to behave more or less normally. One hypothesis is that many melanocytes in this context are "tumor competent" but are regulated by the microenvironment provided by the surrounding keratinocytes to inhibit progress to nevi or melanoma. There is evidence of accumulating disorder in several measures of the genomic and epigenomic landscape from normal skin through nevi to melanoma that may be key to promoting nevogenesis and melanomagenesis.

Local blockade of tacrolimus promotes T-cell-mediated tumor regression in systemically immunosuppressed hosts.

BACKGROUND: Immunosuppressive drugs such as tacrolimus have revolutionized our ability to transplant organs between individuals. Tacrolimus acts systemically to suppress the activity of T-cells within and around transplanted organs. However, tacrolimus also suppresses T-cell function in the skin, contributing to a high incidence of skin cancer and associated mortality and morbidity in solid organ transplant recipients. Here, we aimed to identify a compound capable of re-establishing antitumor T-cell control in the skin despite the presence of tacrolimus. METHODS: In this study, we performed time-resolved fluorescence resonance energy transfer to identify molecules capable of antagonizing the interaction between tacrolimus and FKBP12. The capacity of these molecules to rescue mouse and human T-cell function in the presence of tacrolimus was determined in vitro, and the antitumor effect of the lead compound, Q-2361, was assessed in "regressor" models of skin cancer in immunosuppressed mice. Systemic CD8 T-cell depletion and analyses of intratumoral T-cell activation markers and effector molecule production were performed to determine the mechanism of tumor rejection. Pharmacokinetic studies of topically applied Q-2361 were performed to assess skin and systemic drug exposure. RESULTS: Q-2361 potently blocked the interaction between tacrolimus and FKBP12 and reversed the inhibition of the nuclear factor of activated T cells activation by tacrolimus following T-cell receptor engagement in human Jurkat cells. Q-2361 rescued T-cell function in the presence of tacrolimus, rapamycin, and everolimus. Intratumoral injection of Q-2361-induced tumor regression in mice systemically immune suppressed with tacrolimus. Mechanistically, Q-2361 treatment permitted T-cell activation, proliferation, and effector function within tumors. When CD8 T cells were depleted, Q-2361 could not induce tumor regression. A simple solution-based Q-2361 topical formulation achieved high and sustained residence in the skin with negligible drug in the blood. CONCLUSIONS: Our findings demonstrate that the local application of Q-2361 permits T-cells to become activated driving tumor rejection in the presence of tacrolimus. The data presented here suggests that topically applied Q-2361 has great potential for the reactivation of T-cells in the skin but not systemically, and therefore represents a promising strategy to prevent or treat skin malignancies in immunosuppressed organ transplant recipients.

Delphi Consensus Among International Experts on the Diagnosis, Management, and Surveillance for Lentigo Maligna.

INTRODUCTION: Melanoma of the lentigo maligna (LM) type is challenging. There is lack of consensus on the optimal diagnosis, treatment, and follow-up. OBJECTIVES: To obtain general consensus on the diagnosis, treatment, and follow-up for LM. METHODS: A modified Delphi method was used. The invited participants were either members of the International Dermoscopy Society, academic experts, or authors of published articles relating to skin cancer and melanoma. Participants were required to respond across three rounds using a 4-point Likert scale). Consensus was defined as >75% of participants agreeing/strongly agreeing or disagreeing/strongly disagreeing. RESULTS: Of the 31 experts invited to participate in this Delphi study, 29 participants completed Round 1 (89.9% response rate), 25/31 completed Round 2 (77.5% response rate), and 25/31 completed Round 3 (77.5% response rate). Experts agreed that LM diagnosis should be based on a clinical and dermatoscopic approach (92%) followed by a biopsy. The most appropriate primary treatment of LM was deemed to be margin-controlled surgery (83.3%), although non-surgical modalities, especially imiquimod, were commonly used either as alternative off-label primary treatment in selected patients or as adjuvant therapy following surgery; 62% participants responded life-long clinical follow-up was needed for LM. CONCLUSIONS: Clinical and histological diagnosis of LM is challenging and should be based on macroscopic, dermatoscopic, and RCM examination followed by a biopsy. Different treatment modalities and follow-up should be carefully discussed with the patient.

A reinforcement learning model for AI-based decision support in skin cancer.

We investigated whether human preferences hold the potential to improve diagnostic artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case. We utilized nonuniform rewards and penalties based on expert-generated tables, balancing the benefits and harms of various diagnostic errors, which were applied using reinforcement learning. Compared with supervised learning, the reinforcement learning model improved the sensitivity for melanoma from 61.4% to 79.5% (95% confidence interval (CI): 73.5-85.6%) and for basal cell carcinoma from 79.4% to 87.1% (95% CI: 80.3-93.9%). AI overconfidence was also reduced while simultaneously maintaining accuracy. Reinforcement learning increased the rate of correct diagnoses made by dermatologists by 12.0% (95% CI: 8.8-15.1%) and improved the rate of optimal management decisions from 57.4% to 65.3% (95% CI: 61.7-68.9%). We further demonstrated that the reward-adjusted reinforcement learning model and a threshold-based model outperformed naïve supervised learning in various clinical scenarios. Our findings suggest the potential for incorporating human preferences into image-based diagnostic algorithms.

High-risk Prognostic Tumor Features of Squamous Cell Carcinomas in Organ Transplant Recipients Compared With the General Population.

Importance: The extent to which major high-risk features of squamous cell carcinomas (SCCs) in organ transplant recipients (OTRs) differ from SCCs in the general population is not known. Objective: To quantify the relative frequency of perineural invasion, invasion below the dermis, lack of cellular differentiation, and tumor diameter greater than 20 mm in SCCs in OTRs and the general population, by anatomic site. Design, Setting, and Participants: This dual-cohort study in Queensland, Australia, included a cohort of OTRs at high risk of skin cancer ascertained from 2012 to 2015 (Skin Tumours in Allograft Recipients [STAR] study) and a population-based cohort ascertained from 2011 (QSkin Sun and Health Study). The STAR study comprised population-based lung transplant recipients and kidney and liver transplant recipients at high risk of skin cancer recruited from tertiary centers and diagnosed with histopathologically confirmed SCC from 2012 to 2015. The QSkin participants were recruited from Queensland's general adult population, and primary SCCs diagnosed from 2012 to 2015 were ascertained through Medicare (national health insurance scheme) and linked with histopathology records. Data analysis was performed from July 2022 to April 2023. Main Outcomes and Measures: Prevalence ratio (PR) of head/neck location, perineural invasion, tumor invasion to/beyond subcutaneous fat, poor cellular differentiation, and tumor diameter greater than 20 mm among SCCs in OTRs vs the general population. Results: There were 741 SCCs excised from 191 OTRs (median [IQR] age, 62.7 [56.7-67.1] years; 149 [78.0%] male) and 2558 SCCs from 1507 persons in the general population (median [IQR] age, 63.7 [58.0-68.8] years; 955 [63.4%] male). The SCCs developed most frequently on the head/neck in OTRs (285, 38.6%), but on arms/hands in the general population (896, 35.2%) (P < .001). After adjusting for age and sex, perineural invasion was more than twice as common in OTRs as in population cases (PR, 2.37; 95% CI, 1.70-3.30), as was invasion to/beyond subcutaneous fat (PR, 2.37; 95% CI, 1.78-3.14). Poorly vs well-differentiated SCCs were more than 3-fold more common in OTRs (PR, 3.45; 95% CI, 2.53-4.71), and prevalence of tumors greater than 20 mm vs 20 mm or smaller was moderately higher in OTRs (PR, 1.52; 95% CI, 1.08-2.12). Conclusions and Relevance: In this dual-cohort study, SCCs in OTRs had significantly worse prognostic features than SCCs in the general population, reinforcing the necessity of early diagnosis and definitive management of SCCs in OTRs.

Epidermal mutation accumulation in photodamaged skin is associated with skin cancer burden and can be targeted through ablative therapy.

The main carcinogen for keratinocyte skin cancers (KCs) such as basal and squamous cell carcinomas is ultraviolet (UV) radiation. There is growing evidence that accumulation of mutations and clonal expansion play a key role in KC development. The relationship between UV exposure, epidermal mutation load, and KCs remains unclear. Here, we examined the mutation load in both murine (n = 23) and human (n = 37) epidermal samples. Epidermal mutations accumulated in a UV dose-dependent manner, and this mutation load correlated with the KC burden. Epidermal ablation (either mechanical or laser induced), followed by spontaneous healing from underlying epithelial adnexae reduced the mutation load markedly in both mouse (n = 8) and human (n = 6) clinical trials. In a model of UV-induced basal cell carcinoma, epidermal ablation reduced incident lesions by >80% (n = 5). Overall, our findings suggest that mutation burden is strongly associated with KC burden and represents a target to prevent subsequent KCs.