Validation of a dermatoscopy-based algorithm for the diagnosis of acral melanoma.

INTRODUCTION: Diagnosis of acral melanocytic lesions can be challenging. The BRAAFF checklist was introduced as a tool to help differentiate between acral nevi and melanoma but has not been validated. METHODS: We asked raters with varying expertise in dermatoscopy to diagnose dermatoscopic images of 533 acral nevi and 144 melanomas via an online platform with and without use of the BRAAFF checklist. From the ratings we calculated sensitivity, specificity, and interrater agreement. Additionally, a new simplified version of the checklist was also tested. RESULTS: We collected 6880 ratings from 175 readers. The BRAAFF checklist achieved a sensitivity of 92.5% and a specificity of 65.0%, which was similar to diagnosis from pattern recognition (sensitivity 90.0%, specificity: 72.1%). Interrater agreement for the BRAAFF criteria ranged from fair to moderate, with lowest agreement for parallel ridge and fibrillar pattern (alpha=0.31) and highest for asymmetry of colors and structures (alpha=0.46). Agreement and diagnostic accuracy were higher for more experienced readers. A simplified version with only two criteria achieved similar sensitivity (95.0%) and lower specificity (60.0%) as the original BRAAFF checklist. Conclusion: The BRAAFF checklist is a useful tool for the diagnosis of melanocytic acral lesions with acceptable sensitivity and reasonable specificity but is not superior to pattern recognition. A simplified version of the checklist could be easier to use with equal sensitivity while exhibiting a modest reduction in specificity.

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.

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.

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.

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.

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.

Reproduction of patterns in melanocytic proliferations by agent-based simulation and geometric modeling.

Spatio-temporal patterns of melanocytic proliferations observed in vivo are important for diagnosis but the mechanisms that produce them are poorly understood. Here we present an agent-based model for simulating the emergence of the main biologic patterns found in melanocytic proliferations. Our model portrays the extracellular matrix of the dermo-epidermal junction as a two-dimensional manifold and we simulate cellular migration in terms of geometric translations driven by adhesive, repulsive and random forces. Abstracted cellular functions and melanocyte-matrix interactions are modeled as stochastic events. For identification and validation we use visual renderings of simulated cell populations in a horizontal perspective that reproduce growth patterns observed in vivo by sequential dermatoscopy and corresponding vertical views that reproduce the arrangement of melanocytes observed in histopathologic sections. Our results show that a balanced interplay of proliferation and migration produces the typical reticular pattern of nevi, whereas the globular pattern involves additional cellular mechanisms. We further demonstrate that slight variations in the three basic cellular properties proliferation, migration, and adhesion are sufficient to produce a large variety of morphological appearances of nevi. We anticipate our model to be a starting point for the reproduction of more complex scenarios that will help to establish functional connections between abstracted microscopic behavior and macroscopic patterns in all types of melanocytic proliferations including melanoma.

Characteristics, treatment and outcomes of 589 melanoma patients documented by 27 general practitioners on the Skin Cancer Audit Research Database.

BACKGROUND AND OBJECTIVE: General practitioners manage more melanomas than dermatologists or surgeons in Australia. Previously undescribed, the management and outcomes of melanoma patients treated by multiple Australasian general practitioners are examined. METHODS: The characteristics, management and outcomes of 589 melanoma patients, managed by 27 Australasian general practitioners and documented on the Skin Cancer Audit Research Database (SCARD), were analysed. RESULTS: Most patients (58.9%) were males with mean age at diagnosis of 62.7 years (range 18-96), and most melanomas were in situ or thin-invasive. Patients aged under 40 years had fewer melanomas, but a higher proportion (the majority) were invasive, compared with older patients (P < 0.0001). Most (55.9%) melanomas were diagnosed following elliptical excision biopsy, the rate of unintended involved margins being eightfold higher for shave biopsies. Wide re-excision was performed by the treating general practitioner for most (74.9%) melanomas, with thick melanomas preferentially referred to surgeons. The average Breslow thickness of invasive melanomas re-excised by general practitioners was 0.67 mm compared with 1.99 mm for those referred to other specialists (P < 0.0001). Of 205 patients with invasive melanoma, 14 progressed to metastatic disease, 50% of these being associated with nodular melanoma. Nine patients progressed to melanoma-specific death. The 5-year survival rate for patients with invasive melanoma was 95.2% (95% CI: 91.2-98.5%). CONCLUSIONS: Diagnostic and therapeutic management of a series of melanoma patients by Australasian general practitioners were closely aligned with current guidelines and 5-year survival with respect to invasive melanoma was at least as favourable as national population-based metrics.

Mus musculus papillomavirus 1 is a key driver of skin cancer development upon immunosuppression.

Epidemiological and experimental data implicate cutaneous human papillomavirus infection as co-factor in the development of cutaneous squamous cell carcinomas (cSCCs), particularly in immunocompromised organ transplant recipients (OTRs). Herein, we established and characterized a skin cancer model, in which Mus musculus papillomavirus 1 (MmuPV1) infection caused cSCCs in cyclosporine A (CsA)-treated mice, even in the absence of UV light. Development of cSCCs and their precursors were observed in 70% of MmuPV1-infected, CsA-treated mice on back as well as on tail skin. Immunosuppression by systemic CsA, but not UV-B irradiation, was a prerequisite, as immunocompetent or UV-B-irradiated mice did not develop skin malignancies after infection. In the virus-driven cSCCs the MmuPV1-E6/E7 oncogenes were abundantly expressed, and transcriptional activity and productive infection demonstrated. MmuPV1 infection induced the expression of phosphorylated H2AX, but not degradation of proapoptotic BAK in the cSCCs. Transfer of primary cells, established from a MmuPV1-induced cSCC from back skin, into athymic nude mice gave rise to secondary cSCCs, which lacked viral DNA, demonstrating that maintenance of the malignant phenotype was virus independent. This papillomavirus-induced skin cancer model opens future investigations into viral involvement, pathogenesis, and cancer surveillance, aiming at understanding and controlling the high incidence of skin cancer in OTRs.

The dermoscopic inverse approach significantly improves the accuracy of human readers for lentigo maligna diagnosis.

BACKGROUND: A recently introduced dermoscopic method for the diagnosis of early lentigo maligna (LM) is based on the absence of prevalent patterns of pigmented actinic keratosis and solar lentigo/flat seborrheic keratosis. We term this the inverse approach. OBJECTIVE: To determine whether training on the inverse approach increases the diagnostic accuracy of readers compared to classic pattern analysis. METHODS: We used clinical and dermoscopic images of histopathologically diagnosed LMs, pigmented actinic keratoses, and solar lentigo/flat seborrheic keratoses. Participants in a dermoscopy masterclass classified the lesions at baseline and after training on pattern analysis and the inverse approach. We compared their diagnostic performance among the 3 timepoints and to that of a trained convolutional neural network. RESULTS: The mean sensitivity for LM without training was 51.5%; after training on pattern analysis, it increased to 56.7%; and after learning the inverse approach, it increased to 83.6%. The mean proportions of correct answers at the 3 timepoints were 62.1%, 65.5, and 78.5%. The percentages of readers outperforming the convolutional neural network were 6.4%, 15.4%, and 53.9%, respectively. LIMITATIONS: The experimental setting and the inclusion of histopathologically diagnosed lesions only. CONCLUSIONS: The inverse approach, added to the classic pattern analysis, significantly improves the sensitivity of human readers for early LM diagnosis.

Melanoma diagnosed on digital dermoscopy monitoring: A side-by-side image comparison is needed to improve early detection.

BACKGROUND: Digital dermoscopy monitoring (DDM) helps to recognize melanomas lacking specific dermoscopic features at baseline, but the number of melanomas eventually developing specific features is still unknown. OBJECTIVE: To assess how many melanomas are identified because they develop melanoma-specific criteria over time compared with melanomas recognized by side-by-side image comparison. METHODS: A case-control study was conducted collecting 206 melanomas: 103 melanomas diagnosed during DDM follow-up and 103 melanomas diagnosed at baseline. The control group was composed of 309 benign lesions consisting of 103 nevi excised for diagnostic reasons, 103 not excised nevi, and 103 not excised seborrheic keratoses. Dermoscopic images of all 515 lesions were randomly presented to 2 blinded experts to give a diagnosis and to score the criteria of the 7-point checklist. RESULTS: Of the 103 melanomas diagnosed at baseline, 78.6% (n = 81) were correctly identified compared with only 40.8% (n = 42) of melanomas diagnosed after DDM (P < .001). Of the 103 melanomas excised after DDM, 59.2% (n = 61), did not develop melanoma-specific criteria and were identified only because of the side-by-side image comparison. LIMITATIONS: The type of morphologic changes considered as suspicious on DDM was not assessed. CONCLUSIONS: Most melanomas are diagnosed with DDM by side-by-side image comparison.

Clinical improvement in psoriasis with specific targeting of interleukin-23.

Psoriasis is a chronic inflammatory skin disorder that affects approximately 2-3% of the population worldwide and has severe effects on patients' physical and psychological well-being. The discovery that psoriasis is an immune-mediated disease has led to more targeted, effective therapies; recent advances have focused on the interleukin (IL)-12/23p40 subunit shared by IL-12 and IL-23. Evidence suggests that specific inhibition of IL-23 would result in improvement in psoriasis. Here we evaluate tildrakizumab, a monoclonal antibody that targets the IL-23p19 subunit, in a three-part, randomized, placebo-controlled, sequential, rising multiple-dose phase I study in patients with moderate-to-severe psoriasis to provide clinical proof that specific targeting of IL-23p19 results in symptomatic improvement of disease severity in human subjects. A 75% reduction in the psoriasis area and severity index (PASI) score (PASI75) was achieved by all subjects in parts 1 and 3 (pooled) in the 3 and 10 mg kg(-1) groups by day 196. In part 2, 10 out of 15 subjects in the 3 mg kg(-1) group and 13 out of 14 subjects in the 10 mg kg(-1) group achieved a PASI75 by day 112. Tildrakizumab demonstrated important clinical improvement in moderate-to-severe psoriasis patients as demonstrated by improvements in PASI scores and histological samples.

Characteristics of 637 melanomas documented by 27 general practitioners on the Skin Cancer Audit Research Database.

BACKGROUND AND OBJECTIVE: Most melanomas (including melanomas in situ), in Australasia, are treated by general practitioners (GPs). Previously undescribed, the characteristics of a series of melanomas treated by multiple GPs are examined. PATIENTS AND METHODS: Six hundred and thirty-seven melanomas treated by 27 Australasian GPs during 2013 and documented on the Skin Cancer Audit Research Database (SCARD) were analysed by anatomical site, subtype, Breslow thickness, diameter, associated naevi and linked adverse outcomes. RESULTS: Most melanomas (59.7%) were on males, mean age at diagnosis being 62.7 years (range 18-96). Most (65.0%) were in situ, with a high incidence of lentiginous melanoma (LM) (38.8%) and 32% were naevus associated. Most LM (86.4%) were in situ, compared to 55% of superficial spreading melanoma (SSM) (P < 0.0001). There was male predominance on the head, neck and trunk and female predominance on extremities. There was no significant association between Breslow thickness and diameter, with small melanomas as likely to be thick as large melanomas, and melanomas ≤3 mm diameter, on average, more likely to be invasive than larger melanomas. There was a positive correlation between age and both melanoma diameter and Breslow thickness. Seven cases progressed to melanoma-specific death: Five nodular melanoma (NM) and two SSM, one of which was thin (Breslow thickness 0.5 mm). CONCLUSIONS: A large series of melanomas treated by Australasian GPs were predominantly in situ, with a high proportion of LM subtype. With implications for GP training, NM linked to death was over-represented and there was a novel finding that older patients had larger diameter melanomas.

Number needed to biopsy ratio and diagnostic accuracy for melanoma detection.

BACKGROUND: The number needed to biopsy (NNB) ratio for melanoma diagnosis is calculated by dividing the total number of biopsies by the number of biopsied melanomas. It is the inverse of positive predictive value (PPV), which is calculated by dividing the number of biopsied melanomas by the total number of biopsies. NNB is increasingly used as a metric to compare the diagnostic accuracy of health care practitioners. OBJECTIVE: To investigate the association of NNB with the standard statistical measures of sensitivity and specificity. METHODS: We extracted published diagnostic accuracy data from 5 cross-sectional skin cancer reader studies (median [min-max] readers/study was 29 [8-511]). Because NNB is a ratio, we converted it to PPV. RESULTS: Four studies showed no association and 1 showed a negative association between PPV and sensitivity. All 5 studies showed a positive association between PPV and specificity. LIMITATIONS: Reader study data. CONCLUSIONS: An individual health care practitioner with a lower NNB is likely to have a higher specificity than one with a higher NNB, assuming they practice under similar conditions; no conclusions can be made about their relative sensitivities. We advocate for additional research to define quality metrics for melanoma detection and caution when interpreting NNB.

Clinical and dermoscopic characteristics of congenital and noncongenital nevus-associated melanomas.

BACKGROUND: No specific features of nevus-associated melanoma (NAM) are currently defined. OBJECTIVE: To identify clinical/dermoscopic features of NAM. METHODS: Retrospective evaluation of histopathologically diagnosed NAM. RESULTS: Eighty of 165 NAMs had a clinically recognizable nevus component, often raised or nodular, most frequently characterized by different morphologic clones and/or colors. In 111 of 165 NAMs, dermoscopy showed a nevus component, prevalently characterized by regular dots/clods and structureless brown areas. Clinically, the melanoma component was eccentric/peripheral in 45 of 80 cases and central in 35 of 80; dermoscopically, the figures were 59 of 111 and 52 of 111, respectively. Melanomas associated with congenital nevi (C-NAMs) occur at a younger age and have a thicker Breslow depth than melanomas associated with acquired nevi (NC-NAMs). Dermoscopically, regular dots/globules characterize C-NAMs, and hypopigmented structureless areas characterize NC-NAMs. LIMITATIONS: Retrospective analysis. CONCLUSION: C-NAMs are more often central to a congenital nevus, with a clod/globular or structureless brown pattern, typical of young patients. NC-NAMs are frequently hypopigmented nodules/plaques, eccentric/peripheral, with hypopigmented structureless areas, typical of older patients.

Analysis of Collective Human Intelligence for Diagnosis of Pigmented Skin Lesions Harnessed by Gamification Via a Web-Based Training Platform: Simulation Reader Study.

BACKGROUND: The diagnosis of pigmented skin lesion is error prone and requires domain-specific expertise, which is not readily available in many parts of the world. Collective intelligence could potentially decrease the error rates of nonexperts. OBJECTIVE: The aim of this study was to evaluate the feasibility and impact of collective intelligence for the detection of skin cancer. METHODS: We created a gamified study platform on a stack of established Web technologies and presented 4216 dermatoscopic images of the most common benign and malignant pigmented skin lesions to 1245 human raters with different levels of experience. Raters were recruited via scientific meetings, mailing lists, and social media posts. Education was self-declared, and domain-specific experience was tested by screening tests. In the target test, the readers had to assign 30 dermatoscopic images to 1 of the 7 disease categories. The readers could repeat the test with different lesions at their own discretion. Collective human intelligence was achieved by sampling answers from multiple readers. The disease category with most votes was regarded as the collective vote per image. RESULTS: We collected 111,019 single ratings, with a mean of 25.2 (SD 18.5) ratings per image. As single raters, nonexperts achieved a lower mean accuracy (58.6%) than experts (68.4%; mean difference=-9.4%; 95% CI -10.74% to -8.1%; P<.001). Collectives of nonexperts achieved higher accuracies than single raters, and the improvement increased with the size of the collective. A collective of 4 nonexperts surpassed single nonexperts in accuracy by 6.3% (95% CI 6.1% to 6.6%; P<.001). The accuracy of a collective of 8 nonexperts was 9.7% higher (95% CI 9.5% to 10.29%; P<.001) than that of single nonexperts, an improvement similar to single experts (P=.73). The sensitivity for malignant images increased for nonexperts (66.3% to 77.6%) and experts (64.6% to 79.4%) for answers given faster than the intrarater mean. CONCLUSIONS: A high number of raters can be attracted by elements of gamification and Web-based marketing via mailing lists and social media. Nonexperts increase their accuracy to expert level when acting as a collective, and faster answers correspond to higher accuracy. This information could be useful in a teledermatology setting.

[Inflammoscopy: dermatoscopy for inflammatory, infiltrating and infectious dermatoses : Indication and standardization of dermatoscopic terminology]. / Inflammoskopie: Dermatoskopie bei entzündlichen, infiltrierenden und infektiösen Dermatosen : Indikation und standardisierte dermatoskopische Terminologie.

Dermatoscopy as a noninvasive diagnostic tool is not only useful in the differentiation of malignant and benign skin tumors, but is also effective in the diagnosis of inflammatory, infiltrative and infectious dermatoses. As a result, the need for diagnostic punch biopsies in dermatoses could be reduced. Hereby the selection of affected skin areas is essential. The diagnostic accuracy is independent of the skin type. Helpful dermatoscopic features include vessels morphology and distribution, scales colors and distribution, follicular findings, further structures such as colors and morphology as well as specific clues. The dermatoscopic diagnosis is made based on the descriptive approach in clinical routine, teaching and research. In all clinical and dermatoscopic diagnoses that remain unclear, a punch biopsy with histopathology should be performed. The dermatoscope should be cleaned after every examination according to the guidelines.

Dermatoscopic features of pigmented intraepidermal carcinoma on the head and neck.

BACKGROUND AND OBJECTIVE: Pigmented intraepidermal carcinoma is characterized by dermatoscopic dots and structureless areas, including dots in linear arrangement and by coiled vessels. There are no studies describing the dermatoscopic features of pigmented intraepidermal carcinoma on the head and neck. We aim to characterize the clinical and dermatoscopic appearance of this entity. PATIENTS AND METHODS: We retrospectively analyzed 79 cases of pigmented intraepidermal carcinoma on the head and neck. RESULTS: Pigmented intraepidermal carcinoma on the head and neck was characterized dermatoscopically by multiple colors (98.7 %, n = 78), pigmented circles (48.1 %, n = 38), white circles (17.7 %, n = 14), angulated lines (41.8 %, n = 33) and structureless areas (86.1 %, n = 68). Dots in linear arrangement were present in 13.9 % (n = 11). Coiled vessels were present in 7.6 % (n = 6), the dominant vessel type being prominent serpentine vessels (29.2 %, n = 23), thicker and/or redder in color than surrounding vessels, most being in the angular arrangement of the dermal plexus (24.1 %, n = 19). CONCLUSIONS: Pigmented intraepidermal carcinoma on the head and neck differs from current published descriptions of pigmented intraepidermal carcinoma, reaching statistical significance with a lower incidence of coiled vessels and a higher incidence of pigmented circles, with evident similarities to pigmented actinic keratosis at that location.

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.

BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. METHODS: For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen's disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms. FINDINGS: Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55·4%) of 511 human readers were board-certified dermatologists, 118 (23·1%) were dermatology residents, and 83 (16·2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2·01 (95% CI 1·97 to 2·04; p<0·0001) more correct diagnoses (17·91 [SD 3·42] vs 19·92 [4·27]). 27 human experts with more than 10 years of experience achieved a mean of 18·78 (SD 3·15) correct answers, compared with 25·43 (1·95) correct answers for the top three machine algorithms (mean difference 6·65, 95% CI 6·06-7·25; p<0·0001). The difference between human experts and the top three algorithms was significantly lower for images in the test set that were collected from sources not included in the training set (human underperformance of 11·4%, 95% CI 9·9-12·9 vs 3·6%, 0·8-6·3; p<0·0001). INTERPRETATION: State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice. However, a possible limitation of these algorithms is their decreased performance for out-of-distribution images, which should be addressed in future research. FUNDING: None.