Assessment of melanoma thickness based on dermoscopy images: an open, web-based, international, diagnostic study.

BACKGROUND: Preoperative assessment of whether a melanoma is invasive or in situ (MIS) is a common task that might have important implications for triage, prognosis and the selection of surgical margins. Several dermoscopic features suggestive of melanoma have been described, but only a few of these are useful in differentiating MIS from invasive melanoma. OBJECTIVE: The primary aim of this study was to evaluate how accurately a large number of international readers, individually as well as collectively, were able to discriminate between MIS and invasive melanomas as well as estimate the Breslow thickness of invasive melanomas based on dermoscopy images. The secondary aim was to compare the accuracy of two machine learning convolutional neural networks (CNNs) and the collective reader response. METHODS: We conducted an open, web-based, international, diagnostic reader study using an online platform. The online challenge opened on 10 May 2021 and closed on 19 July 2021 (71 days) and was advertised through several social media channels. The investigation included, 1456 dermoscopy images of melanomas (788 MIS; 474 melanomas ≤1.0 mm and 194 >1.0 mm). A test set comprising 277 MIS and 246 invasive melanomas was used to compare readers and CNNs. RESULTS: We analysed 22 314 readings by 438 international readers. The overall accuracy (95% confidence interval) for melanoma thickness was 56.4% (55.7%-57.0%), 63.4% (62.5%-64.2%) for MIS and 71.0% (70.3%-72.1%) for invasive melanoma. Readers accurately predicted the thickness in 85.9% (85.4%-86.4%) of melanomas ≤1.0 mm (including MIS) and in 70.8% (69.2%-72.5%) of melanomas >1.0 mm. The reader collective outperformed a de novo CNN but not a pretrained CNN in differentiating MIS from invasive melanoma. CONCLUSIONS: Using dermoscopy images, readers and CNNs predict melanoma thickness with fair to moderate accuracy. Readers most accurately discriminated between thin (≤1.0 mm including MIS) and thick melanomas (>1.0 mm).

Position paper on a simplified histopathological classification of basal cell carcinoma: results of the European Consensus Project.

BACKGROUND: Histopathological classification of basal cell carcinoma (BCC) has important prognostic and therapeutic implications, but reproducibility of BCC subtyping among dermatopathologists is poor. OBJECTIVES: To obtain a consensus paper on BCC classification and subtype definitions. METHODS: A panel of 12 recognized dermatopathologists (G12) from nine European countries used a modified Delphi method and evaluated 100 BCC cases uploaded to a website. The strategy involved five steps: (I) agreement on definitions for WHO 2018 BCC subtypes; (II) classification of 100 BCCs using the agreed definitions; (III) discussion on the weak points of the WHO classification and proposal of a new classification with clinical insights; (IV) re-evaluation of the 100 BCCs using the new classification; and (V) external independent evaluation by 10 experienced dermatopathologists (G10). RESULTS: A simplified classification unifying infiltrating, sclerosing, and micronodular BCCs into a single "infiltrative BCC" subtype improved reproducibility and was practical from a clinical standpoint. Fleiss' κ values increased for all subtypes, and the level of agreement improved from fair to moderate for the nodular and the unified infiltrative BCC groups, respectively. The agreement for basosquamous cell carcinoma remained fair, but κ values increased from 0.276 to 0.342. The results were similar for the G10 group. Delphi consensus was not achieved for the concept of trichoblastic carcinoma. In histopathological reports of BCC displaying multiple subtypes, only the most aggressive subtype should be mentioned, except superficial BCC involving margins. CONCLUSIONS: The three BCC subtypes with infiltrative growth pattern, characteristically associated with higher risk of deep involvement (infiltrating, sclerosing, and micronodular), should be unified in a single group. The concise and encompassing term "infiltrative BCCs" can be used for these tumors. A binary classification of BCC into low-risk and high-risk subtypes on histopathological grounds alone is questionable; correlation with clinical factors is necessary to determine BCC risk and therapeutic approach.

Perilesional sun damage as a diagnostic clue for pigmented actinic keratosis and Bowen's disease.

BACKGROUND: Chronic sun damage in the background is common in pigmented actinic keratoses and Bowen's disease (pAK/BD). While explainable artificial intelligence (AI) demonstrated increased background attention for pAK/BD, humans frequently miss this clue in dermatoscopic images because they tend to focus on the lesion. AIM: To analyse whether perilesional sun damage is a robust diagnostic clue for pAK/BD and if teaching this clue to dermatoscopy users improves their diagnostic accuracy. METHODS: We assessed the interrater agreement and the frequency of perilesional sun damage in 220 dermatoscopic images and conducted a reader study with 124 dermatoscopy users. The readers were randomly assigned to one of two online tutorials; one tutorial pointed to perilesional sun damage as a clue to pAK/BD (group A) the other did not (group B). In both groups, we compared the frequencies of correct diagnoses before and after receiving the tutorial. RESULTS: The frequency of perilesional sun damage was higher in pAK/BD than in other types of pigmented skin lesions and interrater agreement was good (kappa = 0.675). The diagnostic accuracy for pAK/BD improved in both groups of readers (group A: +16.1%, 95%-CI: 9.5-22.7; group B: +13.1%; 95%-CI: 7.1-19.0; P for both <0.001), but the overall accuracy improved only in group A from (59.1% (95%-CI: 55.0-63.1) to 63.5% (95%-CI: 59.5-67.6); P = 0.002). CONCLUSION: Perilesional sun damage is a good clue to differentiate pAK/BD from other pigmented skin lesions in dermatoscopic images, which could be useful for teledermatology. Knowledge of this clue improves the accuracy of dermatoscopy users, which demonstrates that insights from explainable AI can be used to train humans.

Dermatoscopy of combined blue nevi: a multicentre study of the International Dermoscopy Society.

BACKGROUND: Combined blue nevi (CBN) may mimic melanoma and are relatively often biopsied for diagnostic reasons. OBJECTIVE: To better characterize CBN and to compare it with melanoma. METHODS: We collected clinical and dermatoscopic images of 111 histologically confirmed CBN and contrasted their dermatoscopic characteristics with 132 partly blue coloured melanomas. Furthermore, we compared the accuracy of human experts using pattern analysis with a computer algorithm based on deep learning. RESULTS: Combined blue nevi are usually flat or slightly elevated and, in comparison with melanoma, more frequent on the head and neck. Dermatoscopically, they are typified by a blue structureless part in combination with either brown clods (n = 52, 46.8%), lines (n = 28, 25.2%) or skin-coloured or brown structureless areas (n = 31, 27.9%). In contrast with melanoma, the blue part of CBN is more often well defined (18.9% vs. 4.5%, P < 0.001) and more often located in the centre (22.5% vs. 5.3%, P < 0.001). Melanomas are more often chaotic (OR: 28.7, 95% CI: 14.8-55.7, P < 0.001), have at least one melanoma clue (OR: 10.8, 95% CI: 5.2-22.2 P < 0.001) in particular white lines (OR: 37.1, 95% CI: 13.4-102.9, P < 0.001). Using simplified pattern analysis (chaos and clues), two raters reached sensitivities of 93.9% (95% CI: 88.4-97.3%) and 92.4% (95% CI: 86.5-96.3%) at corresponding specificities of 59.5% (95% CI: 49.7-68.7%) and 65.8% (95% CI: 56.2-74.5%). The human accuracy with pattern analysis was on par with a state-of-the-art computer algorithm based on deep learning that achieved an area under the curve of (0.92, 95% CI: 0.87-0.96) and a specificity of 85.3% (95% CI: 76.5-91.7%) at a given sensitivity of 83.6% (95% CI: 72.5-91.5%). CONCLUSION: CBN usually lack melanoma clues, in particular white lines. The accuracy of pattern analysis for combined nevi is acceptable, and histopathologic confirmation may not be necessary in exemplary cases.

[Computer-assisted skin cancer diagnosis : Is it time for artificial intelligence in clinical practice?] / Computerassistierte Hautkrebsdiagnose : Wann kommt künstliche Intelligenz in der Praxis an?

BACKGROUND: Artificial intelligence (AI) is increasingly being used in medical practice. Especially in the image-based diagnosis of skin cancer, AI shows great potential. However, there is a significant discrepancy between expectations and true relevance of AI in current dermatological practice. OBJECTIVES: This article summarizes promising study results of skin cancer diagnosis by computer-based diagnostic systems and discusses their significance for daily practice. We hereby focus on the analysis of dermoscopic images of pigmented and unpigmented skin lesions. MATERIALS AND METHODS: A selective literature search for recent relevant trials was conducted. The included studies used machine learning, and in particular "convolutional neural networks", which have been shown to be particularly effective for the classification of image data. RESULTS AND CONCLUSIONS: In numerous studies, computer algorithms were able to detect pigmented and nonpigmented neoplasms of the skin with high precision, comparable to that of dermatologists. The combination of the physician's assessment and AI showed the best results. Computer-based diagnostic systems are widely accepted among patients and physicians. However, they are still not applicable in daily practice, since computer-based diagnostic systems have only been tested in an experimental environment. In addition, many digital diagnostic criteria that help AI to classify skin lesions remain unclear. This lack of transparency still needs to be addressed. Moreover, clinical studies on the use of AI-based assistance systems are needed in order to prove its applicability in daily dermatologic practice.

Human surface anatomy terminology for dermatology: a Delphi consensus from the International Skin Imaging Collaboration.

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

Dermatoscopic features of thin (≤2 mm Breslow thickness) vs. thick (>2 mm Breslow thickness) nodular melanoma and predictors of nodular melanoma versus nodular non-melanoma tumours: a multicentric collaborative study by the International Dermoscopy Society.

BACKGROUND: Thin nodular melanoma (NM) often lacks conspicuous melanoma-specific dermatoscopic criteria and escapes clinical detection until it progresses to a thicker and more advanced tumour. OBJECTIVE: To investigate the dermatoscopic morphology of thin (≤2 mm Breslow thickness) vs. thick (>2 mm) NM and to identify dermatoscopic predictors of its differential diagnosis from other nodular tumours. METHODS: Retrospective, morphological case-control study, conducted on behalf of the International Dermoscopy Society. Dermatoscopic images of NM and other nodular tumours from 19 skin cancer centres worldwide were collected and analysed. RESULTS: Overall, 254 tumours were collected (69 NM of Breslow thickness ≤2 mm, 96 NM >2 mm and 89 non-melanoma nodular lesions). Light brown coloration (50.7%) and irregular brown dots/globules (42.0%) were most frequently observed in ≤2 mm NMs. Multivariate analysis revealed that dotted vessels (3.4-fold), white shiny streaks (2.9-fold) and irregular blue structureless area (2.4-fold) were predictors for thinner NM compared to non-melanoma nodular tumours. Overall, irregular blue structureless area (3.4-fold), dotted vessels (4.6-fold) and serpentine vessels (1.9-fold) were predictors of all NM compared to non-melanoma nodular lesions. LIMITATIONS: Absence of a centralized, consensus pathology review and cases selected form tertiary centres maybe not reflecting the broader community. CONCLUSIONS: Our study sheds light into the dermatoscopic morphology of thin NM in comparison to thicker NM and could provide useful clues for its differential diagnosis from other non-melanoma nodular tumours.

[Artificial intelligence and smartphone program applications (Apps) : Relevance for dermatological practice]. / Künstliche Intelligenz und Smartphone-Programm-Applikationen (Apps) : Bedeutung für die dermatologische Praxis.

ADVANTAGES OF ARTIFICIAL INTELLIGENCE (AI): With responsible, safe and successful use of artificial intelligence (AI), possible advantages in the field of dermato-oncology include the following: (1) medical work can focus on skin cancer patients, (2) patients can be more quickly and effectively treated despite the increasing incidence of skin cancer and the decreasing number of actively working dermatologists and (3) users can learn from the AI results. POTENTIAL DISADVANTAGES AND RISKS OF AI USE: (1) Lack of mutual trust can develop due to the decreased patient-physician contact, (2) additional time effort will be necessary to promptly evaluate the AI-classified benign lesions, (3) lack of adequate medical experience to recognize misclassified AI decisions and (4) recontacting a patient in due time in the case of incorrect AI classifications. Still problematic in the use of AI are the medicolegal situation and remuneration. Apps using AI currently cannot provide sufficient assistance based on clinical images of skin cancer. REQUIREMENTS AND POSSIBLE USE OF SMARTPHONE PROGRAM APPLICATIONS: Smartphone program applications (apps) can be implemented responsibly when the image quality is good, the patient's history can be entered easily, transmission of the image and results are assured and medicolegal aspects as well as remuneration are clarified. Apps can be used for disease-specific information material and can optimize patient care by using teledermatology.

Standardization of dermoscopic terminology and basic dermoscopic parameters to evaluate in general dermatology (non-neoplastic dermatoses): an expert consensus on behalf of the International Dermoscopy Society.

BACKGROUND: Over the last few years, several articles on dermoscopy of non-neoplastic dermatoses have been published, yet there is poor consistency in the terminology among different studies. OBJECTIVES: We aimed to standardize the dermoscopic terminology and identify basic parameters to evaluate in non-neoplastic dermatoses through an expert consensus. METHODS: The modified Delphi method was followed, with two phases: (i) identification of a list of possible items based on a systematic literature review and (ii) selection of parameters by a panel of experts through a three-step iterative procedure (blinded e-mail interaction in rounds 1 and 3 and a face-to-face meeting in round 2). Initial panellists were recruited via e-mail from all over the world based on their expertise on dermoscopy of non-neoplastic dermatoses. RESULTS: Twenty-four international experts took part in all rounds of the consensus and 13 further international participants were also involved in round 2. Five standardized basic parameters were identified: (i) vessels (including morphology and distribution); (ii) scales (including colour and distribution); (iii) follicular findings; (iv) 'other structures' (including colour and morphology); and (v) 'specific clues'. For each of them, possible variables were selected, with a total of 31 different subitems reaching agreement at the end of the consensus (all of the 29 proposed initially plus two more added in the course of the consensus procedure). CONCLUSIONS: This expert consensus provides a set of standardized basic dermoscopic parameters to follow when evaluating inflammatory, infiltrative and infectious dermatoses. This tool, if adopted by clinicians and researchers in this field, is likely to enhance the reproducibility and comparability of existing and future research findings and uniformly expand the universal knowledge on dermoscopy in general dermatology. What's already known about this topic? Over the last few years, several papers have been published attempting to describe the dermoscopic features of non-neoplastic dermatoses, yet there is poor consistency in the terminology among different studies. What does this study add? The present expert consensus provides a set of standardized basic dermoscopic parameters to follow when evaluating inflammatory, infiltrative and infectious dermatoses. This consensus should enhance the reproducibility and comparability of existing and future research findings and uniformly expand the universal knowledge on dermoscopy in general dermatology.

Dermoscopic features of mammary Paget's disease: a retrospective case-control study by the International Dermoscopy Society.

BACKGROUND: Mammary Paget's disease (MPD) is a rare intraepidermal adenocarcinoma of the nipple-areola complex, associated with an underlying breast cancer in approximately 90% of cases. Delayed diagnosis of MPD is common. Its dermoscopic features have been ill defined in the literature. OBJECTIVES: To determine the clinical and dermoscopic features of MPD versus other dermatologic entities that involve nipple and areola. METHODS: Members of the IDS were invited to submit any case of histologically confirmed MPD, as well as other benign and malignant dermatoses that involve the nipple and areola complex. A standardized evaluation of the dermoscopic images was performed and the results were statistically analyzed. RESULTS: Sixty-five lesions were included in the study, 22 (33.8%) of them MPD and 43 (66.2%) controls. The most frequent dermoscopic criteria of MPD were white scales (86.4%) and pink structureless areas (81.8%), followed by dotted vessels (72.7%), erosion/ulceration (68.2%) and white shiny lines (63.6%). The multivariate analysis showed that white scales and pink structureless areas were significant predictors of MPD, posing a 68-fold and a 31-fold probability of MPD, respectively. Split of the population into pigmented and non-pigmented lesions showed that in pigmented MPD, pink structureless areas, white lines and grey granules and dots are positive predictors of the disease. Among non-pigmented lesions, pink structureless areas, white lines, erosion/ulceration and white scales served as predictors of MPD. CONCLUSIONS: The most frequent profile of an individual with MPD is an elderly female with unilateral, asymptomatic, erythematous plaque of the nipple, dermoscopically displaying pink structureless areas, fine white scales, dotted and a few short linear vessels. In case of pigmentation we may also observe brown structureless areas and pigmented granules. LIMITATIONS: Small sample size, retrospective design.

[Dermatoscopic-pathological correlation of melanocytic skin lesions]. / Dermatoskopisch-pathologische Korrelation melanozytärer Hautläsionen.

There is no doubt that dermatopathology is the most important method to decide if a melanocytic lesion is benign or malignant; however, like most morphologic examinations, dermatopathology is subjective. A recent study demonstrated that the pathologic diagnosis of melanocytic skin lesions has a high variability. Reports with false-positive or false-negative diagnoses are relatively common. The pathologic examination of melanocytic lesions also has observer-independent limitations and one has to accept that some melanocytic lesions cannot be classified as benign or malignant with confidence by dermatopathology alone. If a confident diagnosis is not possible a dermatoscopic-pathologic correlation may be helpful. This, however, is only possible if dermatoscopic images are available and if the dermatopathologist knows how to interpret dermatoscopic structures. A dermatoscopic-pathologic correlation is not useful in all difficult melanocytic lesions but it should be considered in difficult flat pigmented lesions. In these cases dermatoscopy may provide even more important additional information than molecular findings.

[Image-based computer diagnosis of melanoma]. / Bildbasierte Computerdiagnose des Melanoms.

The use of automated diagnostic systems for the diagnosis of melanomas is becoming increasingly more established. These are based on the following four steps: 1) preprocessing, to ensure that disturbing factors are eliminated, 2) segmentation, the separation of the image and the background, 3) extraction and selection of features that provide the highest measure of accuracy for the diagnosis and 4) classification, in which the lesion is assigned to a diagnostic class. Recently, the computer-assisted diagnosis of melanoma has focused on algorithms based on transfer learning, which can make steps 2 and 3 obsolete and provides better results. In this article we also review smartphone applications in the field of melanoma screening and recognition. These applications should be considered with caution as they are available to lay persons although the diagnostic accuracy of these applications has not usually been tested in clinical trials.

Dermoscopy vs. reflectance confocal microscopy for the diagnosis of lentigo maligna.

BACKGROUND: Several dermoscopic and in vivo reflectance confocal microscopy (RCM) diagnostic criteria of lentigo maligna (LM)/lentigo maligna melanoma (LMM) have been identified. However, no study compared the diagnostic accuracy of these techniques. OBJECTIVE: We evaluated the diagnostic accuracy of dermoscopy and RCM for LM/LMM using a holistic assessment of the images. METHODS: A total of 223 facial lesions were evaluated by 21 experts. Diagnostic accuracy of the clinical, dermoscopic and RCM examination was compared. Interinvestigator variability and confidence level in the diagnosis were also evaluated. RESULTS: Overall diagnostic accuracy of the two imaging techniques was good (area under the curve of the sROC function: 0.89). RCM was more sensitive (80%, vs. 61%) and less specific (81% vs. 92%) than dermoscopy for LM/LMM. In particular, RCM showed a higher sensitivity for hypomelanotic and recurrent LM/LMM. RCM had a higher interinvestigator agreement and a higher confidence level in the diagnosis than dermoscopy. CONCLUSION: Reflectance confocal microscopy and dermoscopy are both useful techniques for the diagnosis of facial lesions and in particular LM/LMM. RCM is particularly suitable for the identification of hypomelanotic and recurrent LM/LMM.

Molecular classification of tumour cells in a patient with intravascular large B-cell lymphoma.

BACKGROUND: Intravascular large B-cell lymphoma (IVLBCL) is a rare type of extranodal LBCL. It is characterized by the proliferation of tumour cells exclusively intraluminally in small blood vessels of different organs. The clinical manifestation depends on the type of organ affected; additionally, a haemophagocytic syndrome can be observed in some patients. OBJECTIVES: The aim was to further understand the nosology of this lymphoma as, due to its rarity and in spite of detailed immunohistochemical investigations, its exact nosology is only incompletely understood. METHODS: We used microarray-based analysis of gene expression of tumour cells isolated from a patient with primary manifestation of the lymphoma in the skin and compared it with various other diffuse LBCLs (DLBCLs) as well as a previously published DLBCL classifier. RESULTS: In unsupervised analyses, the tumour cells clustered together with non-germinal centre B-cell (non-GCB) DLBCL samples but were clearly distinct from GCB-DLBCL. Analogous to non-GCB DLBCL, molecular cell-of-origin classification revealed similarity to bone-marrow derived plasma cells. CONCLUSIONS: The IVLBCL of this patient showed molecular similarity to non-GCB DLBCL. Due to the prognostic and increasingly also therapeutic relevance of molecular subtyping in DLBCL, this method, in addition to immunohistochemistry, should also be considered for the diagnosis of IVLBCL in the future.