International Skin Imaging Collaboration-Designated Diagnoses (ISIC-DX): Consensus terminology for lesion diagnostic labeling.

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.

A European Multicentric Investigation of Atypical Melanocytic Skin Lesions of Palms and Soles: The iDScore-PalmoPlantar Database.

Background: The differential diagnosis of atypical melanocytic palmoplantar skin lesions (aMPLs) represents a diagnostic challenge, including atypical nevi (AN) and early melanomas (MMs) that display overlapping clinical and dermoscopic features. We aimed to set up a multicentric dataset of aMPL dermoscopic cases paired with multiple anamnestic risk factors and demographic and morphologic data. Methods: Each aMPL case was paired with a dermoscopic and clinical picture and a series of lesion-related data (maximum diameter value; location on the palm/sole in 17 areas; histologic diagnosis; and patient-related data (age, sex, family history of melanoma/sunburns, phototype, pheomelanin, eye/hair color, multiple/dysplastic body nevi, and traumatism on palms/soles). Results: A total of 542 aMPL cases-113 MM and 429 AN-were collected from 195 males and 347 females. No sex prevalence was found for melanomas, while women were found to have relatively more nevi. Melanomas were prevalent on the heel, plantar arch, and fingers in patients aged 65.3 on average, with an average diameter of 17 mm. Atypical nevi were prevalent on the plantar arch and palmar area of patients aged 41.33 on average, with an average diameter of 7 mm. Conclusions: Keeping in mind the risk profile of an aMPL patient can help obtain a timely differentiation between malignant/benign cases, thus avoiding delayed and inappropriate excision, respectively, with the latter often causing discomfort/dysfunctional scarring, especially at acral sites.

Dermatoscopic patterns of cutaneous metastases: A multicentre cross-sectional study of the International Dermoscopy Society.

BACKGROUND: The detection of cutaneous metastases (CMs) from various primary tumours represents a diagnostic challenge. OBJECTIVES: Our aim was to evaluate the general characteristics and dermatoscopic features of CMs from different primary tumours. METHODS: Retrospective, multicentre, descriptive, cross-sectional study of biopsy-proven CMs. RESULTS: We included 583 patients (247 females, median age: 64 years, 25%-75% percentiles: 54-74 years) with 632 CMs, of which 52.2% (n = 330) were local, and 26.7% (n = 169) were distant. The most common primary tumours were melanomas (n = 474) and breast cancer (n = 59). Most non-melanoma CMs were non-pigmented (n = 151, 95.6%). Of 169 distant metastases, 54 (32.0%) appeared on the head and neck region. On dermatoscopy, pigmented melanoma metastases were frequently structureless blue (63.6%, n = 201), while amelanotic metastases were typified by linear serpentine vessels and a white structureless pattern. No significant difference was found between amelanotic melanoma metastases and CMs of other primary tumours. CONCLUSIONS: The head and neck area is a common site for distant CMs. Our study confirms that most pigmented melanoma metastasis are structureless blue on dermatoscopy and may mimic blue nevi. Amelanotic metastases are typified by linear serpentine vessels and a white structureless pattern, regardless of the primary tumour.

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma.

Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists' decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists' diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists' confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists' willingness to adopt such XAI systems, promoting future use in the clinic.

Lentigo maligna and lentigo maligna melanoma in patients younger than 50†years: a multicentre international clinical-dermoscopic study.

BACKGROUND: Lentigo maligna/lentigo maligna melanoma (LM/LMM) is usually diagnosed in older patients, when lesions are larger. However, it is important to detect it at an earlier stage to minimize the area for surgical procedure. OBJECTIVES: To determine and define clinical, dermoscopic and reflectance confocal microscopy (RCM) features of LM/LMM in patients < 50 years old. METHODS: This was a multicentre study involving tertiary referral centres for skin cancer management. The study included cases of consecutively excised LM/LMM arising in patients < 50 years of age with a histopathological diagnosis of LM/LMM and a complete set of clinical and dermoscopic images; RCM images were considered when present. RESULTS: In total, 85 LM/LMM of the face from 85 patients < 50 years were included in the study. A regression model showed a direct association with the size of the lesion (R2 = 0.08; P = 0.01) and with the number of dermoscopic features at diagnosis (R2 = 0.12; P < 0.01). In a multivariable analysis, an increasing number of dermoscopic features correlated with increased patient age (P < 0.01), while the presence of grey colour was a predictor of younger age at diagnosis (P = 0.03). RCM revealed the presence of melanoma diagnostic features in all cases (pagetoid cells and atypical nesting). CONCLUSIONS: LM is not a disease limited to older people as previously thought. LM presenting in young adults tends to be smaller and with fewer dermoscopic features, making its diagnosis challenging. Careful evaluation of facial pigmented lesions prior to cosmetic procedures is imperative to avoid incorrectly treating early LM as a benign lesion.

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.

Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check.

The field of skin cancer detection offers a compelling use case for the application of artificial intelligence (AI) within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms have the capacity to classify clinical or dermoscopic images with remarkable accuracy. Although these AI-based applications can operate both autonomously and under human supervision, the best results are achieved through a collaborative approach that leverages the expertise of both AI and human experts. However, it is important to note that most studies focus on assessing the diagnostic accuracy of AI in artificial settings rather than in real-world scenarios. Consequently, the practical utility of AI-assisted diagnosis in a clinical environment is still largely unknown. Furthermore, there exists a knowledge gap concerning the optimal use cases and deployment settings for these AI systems as well as the practical challenges that may arise from widespread implementation. This review explores the advantages and limitations of AI in a variety of real-world contexts, with a specific focus on its value to consumers, general practitioners, and dermatologists.

Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial.

BACKGROUND: Diagnosis of skin cancer requires medical expertise, which is scarce. Mobile phone-powered artificial intelligence (AI) could aid diagnosis, but it is unclear how this technology performs in a clinical scenario. Our primary aim was to test in the clinic whether there was equivalence between AI algorithms and clinicians for the diagnosis and management of pigmented skin lesions. METHODS: In this multicentre, prospective, diagnostic, clinical trial, we included specialist and novice clinicians and patients from two tertiary referral centres in Australia and Austria. Specialists had a specialist medical qualification related to diagnosing and managing pigmented skin lesions, whereas novices were dermatology junior doctors or registrars in trainee positions who had experience in examining and managing these lesions. Eligible patients were aged 18-99 years and had a modified Fitzpatrick I-III skin type; those in the diagnostic trial were undergoing routine excision or biopsy of one or more suspicious pigmented skin lesions bigger than 3 mm in the longest diameter, and those in the management trial had baseline total-body photographs taken within 1-4 years. We used two mobile phone-powered AI instruments incorporating a simple optical attachment: a new 7-class AI algorithm and the International Skin Imaging Collaboration (ISIC) AI algorithm, which was previously tested in a large online reader study. The reference standard for excised lesions in the diagnostic trial was histopathological examination; in the management trial, the reference standard was a descending hierarchy based on histopathological examination, comparison of baseline total-body photographs, digital monitoring, and telediagnosis. The main outcome of this study was to compare the accuracy of expert and novice diagnostic and management decisions with the two AI instruments. Possible decisions in the management trial were dismissal, biopsy, or 3-month monitoring. Decisions to monitor were considered equivalent to dismissal (scenario A) or biopsy of malignant lesions (scenario B). The trial was registered at the Australian New Zealand Clinical Trials Registry ACTRN12620000695909 (Universal trial number U1111-1251-8995). FINDINGS: The diagnostic study included 172 suspicious pigmented lesions (84 malignant) from 124 patients and the management study included 5696 pigmented lesions (18 malignant) from the whole body of 66 high-risk patients. The diagnoses of the 7-class AI algorithm were equivalent to the specialists' diagnoses (absolute accuracy difference 1·2% [95% CI -6·9 to 9·2]) and significantly superior to the novices' ones (21·5% [13·1 to 30·0]). The diagnoses of the ISIC AI algorithm were significantly inferior to the specialists' diagnoses (-11·6% [-20·3 to -3·0]) but significantly superior to the novices' ones (8·7% [-0·5 to 18·0]). The best 7-class management AI was significantly inferior to specialists' management (absolute accuracy difference in correct management decision -0·5% [95% CI -0·7 to -0·2] in scenario A and -0·4% [-0·8 to -0·05] in scenario B). Compared with the novices' management, the 7-class management AI was significantly inferior (-0·4% [-0·6 to -0·2]) in scenario A but significantly superior (0·4% [0·0 to 0·9]) in scenario B. INTERPRETATION: The mobile phone-powered AI technology is simple, practical, and accurate for the diagnosis of suspicious pigmented skin cancer in patients presenting to a specialist setting, although its usage for management decisions requires more careful execution. An AI algorithm that was superior in experimental studies was significantly inferior to specialists in a real-world scenario, suggesting that caution is needed when extrapolating results of experimental studies to clinical practice. FUNDING: MetaOptima Technology.

Dermatoscopy of Cutaneous Lichen Planus - Attempt to Translate Metaphoric Terminology Into Descriptive Terminology.

INTRODUCTION: Dermatoscopy is gaining appreciation in assisting the diagnosis of inflammatory dermatoses (inflammoscopy). Lichen planus (LP) is a common inflammatory skin disease with characteristic dermatoscopic features. Over the last few years, numerous articles were published on the dermatoscopy of LP and a high number of terms have been used to describe the dermatoscopic features of this disease. OBJECTIVES: The objective of this study was to review the literature on the dermatoscopy of LP and to re-evaluate the published descriptions in the light of the 2019 expert consensus on the terminology of dermatoscopy for non-neoplastic skin diseases. METHODS: We searched the PubMed database using the keywords 'lichen planus and dermatoscopy', 'lichen planus and dermoscopy', 'lichen planus and epiluminescence microscopy', and 'lichen planus and inflammoscopy'. RESULTS: Of 408 articles retrieved, we selected 67 articles for full-text review, and finally included 58 articles, mostly case reports or small case series, comprising 572 patients with LP. We identified 118 different terms or short descriptions that were used to characterize the dermatoscopy of LP and redescribed them according to International Dermoscopy Society consensus paper. Frequently, authors applied various terms or descriptions to variants of the same feature. Although reported under different designations, Wickham striae were the most consistent dermatoscopic feature of LP. Other characteristics of LP, such as vascular patterns, pigmented structures and follicular findings were less consistent or depended on skin type, anatomic site, disease stage and applied treatment. CONCLUSIONS: While Wickham striae are the single most important clue for the diagnosis, other dermatoscopic characteristics of LP are less consistent. Based on the descriptions published in the literature we established a dictionary of useful terms for the description of LP that is consistent with the terminology suggested by the recent consensus conference.

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.

Practitioner characteristics, diagnostic accuracy metrics and discovering-individual with respect to 637 melanomas documented by 27 general practitioners on the Skin Cancer Audit Research Database.

BACKGROUND AND OBJECTIVE: Knowledge of accuracy for melanoma diagnosis and melanoma discovering-individual in primary care is limited. We describe general practitioner (GP) characteristics and analyse defined diagnostic accuracy metrics for GPs in the current study comparing this with a previous study for GPs common to both, and we analyse the individual first discovering each melanoma as a lesion of concern. METHODS: The characteristics and diagnostic accuracy of 27 Australasian GPs documenting 637 melanomas on the Skin Cancer Audit Research Database (SCARD) in 2013 were described and analysed. The number needed to treat (NNT) and percentage of melanomas that were in situ (percentage in situ) were analysed as surrogates for specificity and sensitivity, respectively. The discovering-individual was analysed according to patient age and sex and lesion Breslow thickness. RESULTS: The average NNT and percentage in situ were 5.73% and 65.07%, respectively. For 21 GPs in both a 2008-2010 study and the current study, the NNT was 10.78 and 5.56, respectively (p = 0.0037). A consistent trend of decreasing NNT and increasing percentage in situ through increasingly subspecialised GP categories did not reach statistical significance. NNT trended high at ages and sites for which melanoma was rare. While the patient or family member was more likely to discover thick melanomas and melanomas in patients under 40 years, GPs discovered 73.9% of the melanomas as lesions of concern. CONCLUSIONS: GPs were the discovering-individuals for the majority of melanomas in the current study and their accuracy metrics compared favourably with published figures for dermatologists and GPs.

[Dermoscopy of nonneoplastic diseases in dark skin]. / Dermatoskopie nichtneoplastischer Erkrankungen auf dunkler Haut.

BACKGROUND: Dermoscopy is an important tool in general dermatology. OBJECTIVES: To show differences of light and dark skin in nonneoplastic diseases with focus on dermoscopy. MATERIALS AND METHODS: Using previously published studies, dermoscopic differences of various skin types as well as features of inflammatory diseases and pigmentary changes are illustrated. RESULTS: Certain structures are more difficult to assess in dermoscopy of dark skin (e.g., vessels), while other structures (e.g., follicular openings) are more prominent. CONCLUSIONS: The majority of studies on dermoscopy are from studies that predominantly included individuals with fair skin types. Further studies of individuals with skin type IV or higher are needed to improve diagnosis in these patients.

Integrated diagnosis of nevi with severely atypical features and impact of second opinions.

BACKGROUND: A subset of melanocytic proliferations is difficult to classify by dermatopathology alone and their management is challenging. OBJECTIVE: To explore the value of correlation with dermatoscopy and to evaluate the utility of second opinions by additional pathologists. METHODS: For this single center retrospective study we collected 122 lesions that were diagnosed as atypical melanocytic proliferations, we reviewed dermatoscopy and asked two experienced pathologists to reassess the slides independently. RESULTS: For the binary decision of nevus versus melanoma the diagnostic consensus among external pathologists was only moderate (kappa 0.43; 95% CI 0.25-0.61). If ground truth were defined such that both pathologists had to agree on the diagnosis of melanoma, 13.1% of cases would have been diagnosed as melanoma. If one pathologist were sufficient to call it melanoma 29.5% of cases would have been diagnosed as melanoma. In either case, the presence of dermatoscopic white lines was associated with the diagnosis of melanoma. In lesions with peripheral dots and clods, melanoma was not jointly diagnosed by the two pathologists if the patient was younger than 45 years. CONCLUSIONS: A considerable number of atypical melanocytic proliferations may be diagnosed as melanoma if revised by other pathologists. The presence of white lines on dermatoscopy increases the likelihood of revision towards melanoma. Peripheral clods indicate growth but are not a melanoma clue if patients are younger than 45 years.

Indications for Digital Monitoring of Patients With Multiple Nevi: Recommendations from the International Dermoscopy Society.

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.