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.

[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.

Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions.

BACKGROUND: Convolutional neural networks (CNNs) efficiently differentiate skin lesions by image analysis. Studies comparing a market-approved CNN in a broad range of diagnoses to dermatologists working under less artificial conditions are lacking. MATERIALS AND METHODS: One hundred cases of pigmented/non-pigmented skin cancers and benign lesions were used for a two-level reader study in 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information). Additionally, dermoscopic images were classified by a CNN approved for the European market as a medical device (Moleanalyzer Pro, FotoFinder Systems, Bad Birnbach, Germany). Primary endpoints were the sensitivity and specificity of the CNN's dichotomous classification in comparison with the dermatologists' management decisions. Secondary endpoints included the dermatologists' diagnostic decisions, their performance according to their level of experience, and the CNN's area under the curve (AUC) of receiver operating characteristics (ROC). RESULTS: The CNN revealed a sensitivity, specificity, and ROC AUC with corresponding 95% confidence intervals (CI) of 95.0% (95% CI 83.5% to 98.6%), 76.7% (95% CI 64.6% to 85.6%), and 0.918 (95% CI 0.866-0.970), respectively. In level I, the dermatologists' management decisions showed a mean sensitivity and specificity of 89.0% (95% CI 87.4% to 90.6%) and 80.7% (95% CI 78.8% to 82.6%). With level II information, the sensitivity significantly improved to 94.1% (95% CI 93.1% to 95.1%; P < 0.001), while the specificity remained unchanged at 80.4% (95% CI 78.4% to 82.4%; P = 0.97). When fixing the CNN's specificity at the mean specificity of the dermatologists' management decision in level II (80.4%), the CNN's sensitivity was almost equal to that of human raters, at 95% (95% CI 83.5% to 98.6%) versus 94.1% (95% CI 93.1% to 95.1%); P = 0.1. In contrast, dermatologists were outperformed by the CNN in their level I management decisions and level I and II diagnostic decisions. More experienced dermatologists frequently surpassed the CNN's performance. CONCLUSIONS: Under less artificial conditions and in a broader spectrum of diagnoses, the CNN and most dermatologists performed on the same level. Dermatologists are trained to integrate information from a range of sources rendering comparative studies that are solely based on one single case image inadequate.

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.

Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.

BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not been investigated. OBJECTIVE: To assess the diagnostic performance of a CNN when used to differentiate melanomas from combined naevi in comparison with dermatologists. METHODS: In this study, a CNN with regulatory approval for the European market (Moleanalyzer-Pro, FotoFinder Systems GmbH, Bad Birnbach, Germany) was used. We attained a dichotomous classification (benign, malignant) in dermoscopic images of 36 combined naevi and 36 melanomas with a mean Breslow thickness of 1.3 mm. Primary outcome measures were the CNN's sensitivity, specificity and the diagnostic odds ratio (DOR) in comparison with 11 dermatologists with different levels of experience. RESULTS: The CNN revealed a sensitivity, specificity and DOR of 97.1% (95% CI [82.7-99.6]), 78.8% (95% CI [62.8-89.1.3]) and 34 (95% CI [4.8-239]), respectively. Dermatologists showed a lower mean sensitivity, specificity and DOR of 90.6% (95% CI [84.1-94.7]; P = 0.092), 71.0% (95% CI [62.6-78.1]; P = 0.256) and 24 (95% CI [11.6-48.4]; P = 0.1114). Under the assumption that dermatologists use the CNN to verify their (initial) melanoma diagnosis, dermatologists achieve an increased specificity of 90.3% (95% CI [79.8-95.6]) at an almost unchanged sensitivity. The largest benefit was observed in 'beginners', who performed worst without CNN verification (DOR = 12) but best with CNN verification (DOR = 98). CONCLUSION: The tested CNN more accurately classified combined naevi and melanomas in comparison with trained dermatologists. Their diagnostic performance could be improved if the CNN was used to confirm/overrule an initial melanoma diagnosis. Application of a CNN may therefore be of benefit to clinicians.

[Dermoscopy in special locations : Nails, acral skin, face, and mucosa]. / Dermatoskopie in Sonderlokalisationen : Nägel, akrale Haut, Gesicht und Mukosa.

The use of dermoscopy by dermatologists across Europe has become a standard examination for benign and malignant skin lesions and increasingly also for inflammatory skin diseases. However, based on the experience of the authors from numerous dermoscopy courses, knowledge about important dermoscopic features in special locations such as mucosa or nails is often limited. This may be explained by (1) a different anatomy of the skin and its adnexa in special locations in comparison to the remaining integument, (2) difficult technical access to special locations with a dermatoscope, and (3) a rather low incidence of malignant skin neoplasms in areas of special locations (with the exception of facial skin/scalp). This article aims at explaining dermoscopic characteristics and features of important benign and malignant lesions of nails, acral skin, face, and mucosa.

Clinical and dermoscopic features of genital pigmented Bowen disease.

Pigmented Bowen disease (pBD) is an uncommon variant of squamous cell carcinoma in situ. Sometimes it can show clinical and dermoscopic features that are seen in other pigmented lesions of the skin and mucosa, making the diagnosis difficult. We report six cases of pBD occurring on the anogenital area, and discuss the importance of dermoscopy for improving the diagnostic accuracy in pBD.

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.

[Dermoscopy for malignant and benign skin tumors : Indication and standardized terminology]. / Dermatoskopie bei malignen und benignen Hauttumoren : Indikation und standardisierte Terminologie.

Dermoscopy has a high diagnostic accuracy in pigmented and nonpigmented malignant and benign skin tumors. These microscopic in vivo examinations with polarized and nonpolarized light are effective in the early detection of malignant skin tumors and reduce the number of unnecessary excisions of benign skin tumors. The selection of the skin lesions is crucial for the diagnostic accuracy of the dermoscopic examination. Not only large pigmented skin lesions, but also small hypo-, de-, or nonpigmented skin lesions, should be examined dermatoscopically as well as skin lesions that have changed in shape and/or color. In clinical routine, research and teaching, the dermoscopic diagnosis should be performed by describing the visible structures, their distribution and colors by means of descriptive and/or metaphoric terminology. Optionally, a diagnostic algorithm can also be used. Especially in benign lesions, the dermatoscopic diagnosis should be uniform for the complete area. Comparison with other nearby skin tumors of the same patient (comparative approach) is helpful in the evaluation of numerous melanocytic skin tumors. If it is unclear whether the lesion is malignant, a biopsy or complete excision should be performed with subsequent histopathological examination.

The value of reflectance confocal microscopy in diagnosis of flat pigmented facial lesions: a prospective study.

BACKGROUND: Flat pigmented facial lesions are difficult to diagnose even with dermatoscopy. It is controversial how additional information obtained by in vivo reflectance confocal microscopy (RCM) impacts the diagnosis and management. OBJECTIVE: To examine what in vivo reflectance confocal microscopy of flat pigmented facial lesions adds to clinical examination using dermatoscopy including digital dermatoscopic monitoring. METHODS: We prospectively collected 70 cases of flat pigmented facial lesions and recorded diagnoses and management decisions by experts based on direct clinical examination aided by dermatoscopy including digital dermatoscopic monitoring and by remote experts who reviewed the corresponding confocal images. The expert confocal readers were blinded to the clinical and dermatoscopic appearance of the lesion. RESULTS: The sensitivity of dermatoscopy plus digital dermatoscopic monitoring was 95.0% (95% CI 75.13% to 99.87%) and the specificity was 84.0% (95% CI 70.89% to 92.83%). The sensitivity of RCM was 95.0% (95% CI 75.13% to 99.87%) and the specificity was 82.0% (95% CI 68.56% to 91.42%). CONCLUSION: Although most flat pigmented facial lesions can be managed by clinical examination and dermatoscopy alone, confocal microscopy is a useful adjunct in selected lesions. If RCM is not correlated with clinical and dermatoscopic information, there is risk of overdiagnosis of actinic keratosis, however.

Pregnancy as a driver for melanoma.

Whether or not pregnancy favours the occurrence and growth of melanoma is a source of controversy in the literature. Several case reports have shown dramatic courses of diseases in pregnancy. We present a case of a 36-year-old woman with multiple naevi with one melanoma detected in 2009 in the first trimester and a second primary melanoma in 2010 in the third trimester of her pregnancy. Both lesions have been present for at least 5 years and have been interpreted as dysplastic naevi. Because of their growth during pregnancy they were removed. No metastatic disease has been found between 2010 and early 2017. This case shows the difficulty of detecting melanomas in pregnancy, particularly when they mimic dysplastic naevi in women with multiple naevi, who are at higher risk. Therefore, we suggest that pregnant women with numerous naevi should be precautious of any changes of their naevi in size, shape and colour. Every suspicious lesion should be either excised or documented/monitored carefully, for example with sequential digital dermoscopy imaging.