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Background: Survey on the current status of dermoscopy in Germany. Methods: In the context of a pan-European internet-based study (n = 7,480) conducted by the International Dermoscopy Society, 880 German dermatologists were asked to answer questions with respect to their level of training as well as their use and perceived benefit of dermoscopy. Results: Seven hundred and sixty-two (86.6 %) participants practiced dermatology in a publicly funded health care setting; 98.4 % used a dermoscope in routine clinical practice. About 93 % (n = 814) stated to have had more than five years of experience in the use of dermoscopy. Dermoscopy was considered useful in the diagnosis of melanoma by 93.6 % (n = 824); for pigmented skin tumors, by 92.4 % (n = 813); in the follow-up of melanocytic lesions, by 88.6 % (n = 780); for non-pigmented lesions, by 71.4 % (n = 628), in the follow-up of non-melanocytic lesions, by 52.7 % (n = 464); and for inflammatory skin lesions, by 28.5 % (n = 251). Overall, 86.5 % (n = 761) of participants felt that-compared to naked-eye examination-dermoscopy increased the number of melanomas diagnosed; 77,7 % (n = 684) considered the number of unnecessary excisions of benign lesions to be decreased. Participants who personally felt that dermoscopy improved their ability to diagnose melanoma were significantly i) younger, ii) had been practicing dermatology for a shorter period of time, iii) were less commonly employed by a university-affiliated dermatology department, iv) were more frequently working in an office-based public health care setting, and v) had more frequently been trained in dermoscopy during their dermatology residency. Conclusions: The findings presented herein ought to be integrated into future residency and continuing medical education programs with the challenge to improve dermato-oncological care and to expand the diagnostic spectrum of dermoscopy to include inflammatory skin diseases.

Background Survey on the current status of dermoscopy in Germany. Methods In the context of a pan-European internet-based study (n = 7,480) conducted by the International Dermoscopy Society, 880 German dermatologists were asked to answer questions with respect to their level of training as well as their use and perceived benefit of dermoscopy. Results Seven hundred and sixty-two (86.6 %) participants practiced dermatology in a publicly funded health care setting; 98.4 % used a dermoscope in routine clinical practice. About 93 % (n = 814) stated to have had more than five years of experience in the use of dermoscopy. Dermoscopy was considered useful in the diagnosis of melanoma by 93.6 % (n = 824); for pigmented skin tumors, by 92.4 % (n = 813); in the follow-up of melanocytic lesions, by 88.6 % (n = 780); for non-pigmented lesions, by 71.4 % (n = 628), in the follow-up of non-melanocytic lesions, by 52.7 % (n = 464); and for inflammatory skin lesions, by 28.5 % (n = 251). Overall, 86.5 % (n = 761) of participants felt that - compared to naked-eye examination - dermoscopy increased the number of melanomas diagnosed; 77,7 % (n = 684) considered the number of unnecessary excisions of benign lesions to be decreased. Participants who personally felt that dermoscopy improved their ability to diagnose melanoma were significantly i) younger, ii) had been practicing dermatology for a shorter period of time, iii) were less commonly employed by an university-affiliated dermatology department, iv) were more frequently working in an office-based public health care setting, and v) had more frequently been trained in dermoscopy during their dermatology residency. Conclusions The findings presented herein ought to be integrated into future residency and continuing medical education programs with the challenge to improve dermato-oncological care and to expand the diagnostic spectrum of dermoscopy to include inflammatory skin diseases.

Background: Collision lesions as two independent and unrelated skin tumors often manifest an atypical morphology. Objective: To determine the combinations of collision skin lesions (CSLs). Methods: Twenty-one pigmented lesion clinics in nine countries included 77 histopathologically proven CSLs in this retrospective observational study. Results: Seventy-seven CSLs from 75 patients (median age 59.8 years) were analyzed; 24.7% of CSLs were located on the head and neck area, 5.2% on the upper extremities, 48.1% on the trunk, and 11.7% on the lower extremities; 40.3% revealed a melanocytic component (median age 54.7 years), followed by 45.5% with a basal cell carcinoma (BCC) (median age 62.4 years) and 11.7% with a seborrheic keratosis (median age 64.7 years). CSLs with a BCC component were more often found on the head and neck area compared to tumors with a melanocytic component (34.3% versus 16.1%). Lesions with a melanocytic component were more often detected on the trunk compared to lesions with a BCC (64.5% versus 37.1%). Patients with CSLs with epidermal-epidermal cell combination were older than patients with epidermal-dermal cell combination (63 versus 55.2 years), were more often male than female (63% versus 43.3%), more often had the lesion on the head and neck area (32.6% versus 13.3%), and less often on the upper (2.2 % versus 10%) or lower extremities (8.7% versus 16.6%). Conclusions: CSLs consist of a heterogeneous group of lesions of varying cell types. They are associated with advancing age and cumulative UV-exposure. CSLs manifest a complex morphology making it challenging to diagnose correctly.

Melanozytare Navi, also gutartige Proliferationen melanozytarer Zellen der Haut gehoren zu den haufigsten Diagnosen in einer Hautarztpraxis. Die Ahnlichkeit und Verwechslungsmoglichkeit mit einem Melanom ist fur viele Patienten der primare Konsultationsgrund („Ist das was Schlimmes?“). Besondere Aufmerksamkeit erfordern rasch entstandene Pigmentmale.

Haare, Haut und Nagel pragen entscheidend den asthetischen Gesamteindruck einer Person. Durch die Moglichkeiten der Dermatoskopie als nicht invasive morphologische Untersuchungsmethode wird die Lucke zwischen klinischer Blickdiagnose und histologischer Befunderhebung am Schnittpraparat geschlossen. Dem Untersucher erschliesen sich durch diese Technik mikroanatomische Strukturen lebender Haut in dreidimensionaler Perspektive.

Die wissenschaftlichen Arbeiten zur Dermatoskopie (Auflichtmikroskopie, Epilumineszenzmikroskopie) in den Bereichen der klinischen Forschung, der Lehre und der technischen Entwicklungen von zahlreichen deutschsprachigen Dermatoskopikern/innen haben in den letzten 30 Jahren einen wichtigen Grundstein fur die tagliche Versorgung von dermatoonkologischen Patienten nicht nur national, sondern auch international gelegt.

Background: Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking.Methods: Google's Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists' diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN's performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge.Results: In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge.Conclusions: For the first time we compared a CNN's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification.Clinical trial number: This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/). (Less)

The mainstays of the clinical diagnosis of melanoma are asymmetry, border irregularity, color variegation, and a diameter >6 mm, and any major progress in diagnostic accuracy will probably be related to the development of additional criteria. Such independent criteria might arise from the study of the geometry of the tumour surface, because this quality has been substantially disregarded until now. Our work is aimed at utilizing the surface topography for the differential diagnosis between malignant melanoma and naevocytic naevus.Dynamically focusing optical profilometry was used to measure the surfaces of negative replicas of melanocytic skin tumours and of the surrounding normal skin. 21 silicone imprints of superficial spreading melanomas and 25 imprints of naevocytic naevi were examined.Melanomas and naevi differed with respect to a variety of statistical surface parameters, and a linear discriminant analysis correctly allocated 19 out of 21 melanomas (90%) and 21 out of 25 naevi (84%). To get an unbiased estimate of the errors to be expected with this classification rule, we calculated bootstrap-corrections to the apparent errors. Estimated probabilities of correct allocation were 84.1% for melanomas and 77.1% for naevi.Our findings suggest that simple statistical parameters of surface topography can differentiate effectively between malignant melanomas and naevocytic naevi.

Dermoscopy is a noninvasive tool that can be helpful in the diagnosis of nonpigmented skin tumors. This is because dermoscopy permits the visualization of key vascular structures that are usually not visible to the naked eye. Much work has concentrated on the identification of specific morphologic types of vessels that allow a classification into melanocytic versus nonmelanocytic and benign versus malignant nonpigmented skin tumors. Among a broad spectrum of different types of vascular patterns, six main morphologies can be identified. These are comma-like, dotted, linear-irregular, hairpin, glomerular, and arborizing vessels. With some exceptions, comma, dotted, and linear irregular vessels are associated with melanocytic tumors, while the latter three vascular types are generally indicative of keratinocytic tumors. Aside from vascular morphology, the architectural arrangement of vessels within the tumor and the presence of additional dermoscopic clues are equally important for the diagnosis. This article provides a general overview of the dermoscopic evaluation of nonpigmented skin tumors and is divided into two parts. Part I discusses the dermoscopic vascular patterns of benign and malignant melanocytic skin tumors. Part II discusses the dermoscopic vascular patterns of benign and malignant nonmelanocytic nonpigmented skin tumors. In each part, additional special management guidelines for melanocytic and nonmelanocytic nonpigmented skin tumors, respectively, will be discussed.

The search for vascular structures in skin tumors by incident light microscopy has revealed a surprisingly high proportion of vascularized tumours. Characteristic vascular patterns, in some cases highly tumour-specific, have been defined. The vascular structure is a valuable feature for characterization and differentiation of skin tumours, especially for amelanotic malignant melanoma. We propose a flowchart procedure for analysis and diagnosis of skin tumours using incident light microscopy, which appears to be valuable for advanced image analytic techniques.

BACKGROUND/AIMS Quantitative measurement of skin roughness has proved to be a valuable tool in the efficacy-control of external applications, but it suffers from not yielding easily comparable results. The most important sources of inter-observer variability are high-pass filters used to separate roughness and waviness, and low-pass filters which result from the finite resolution of the instrument or from the finite sampling interval of digital measurement. In the present study, the effects of high-pass filters and sampling intervals on the roughness measured were investigated. METHODS Dynamically focusing optical profilometry was used to measure the surfaces of negative replicas of healthy human skin. High-pass cut-off wavelengths and sampling intervals were varied systematically. RESULTS/CONCLUSIONS Virtually unbiased estimates for the roughness parameters K, Sk, Rq , and Ra can be obtained using sampling intervals of 40 or even 80 μm. Regarding these roughness parameters, it is far better to do more scans than to shorten the sampling interval. The roughness parameters Rz , Rp , Rt , Rpm , Rmax , Pt , on the other hand are very sensitive to the influence of the sampling interval; to achieve satisfying estimates, the sampling interval should be no longer than 2 to 5 urn; as an important parameter'of the measurement, it is worthy of remark and should always be indicated. The way the mean square roughness Rq depends on the cut-off wavelength is not well described by the Sayles-Thomas-relation Rq ∼λc (0.5) . If the power-spectrum |h*(v)|(2) approximates sufficiently to a power law, |h*(v)|(2) ∼v(δ) , a better estimate is given by Rq ∼λc (γ) with γ=-(δ+1)/2. In many cases, γ=1 or Rq ∼λc will suffice.

We report on a patient with an abdominal type of benign symmetrical Launois-Bensaude lipomatosis. New aspects of the pathogenesis of abnormal distribution of the fatty tissue are also discussed.

Background: There is a need for better standardization of the dermoscopic terminology in assessing pigmented skin lesions. Objective: The virtual Consensus Net Meeting on Dermoscopy was organized to investigate reproducibility and validity of the various features and diagnostic algorithms. Methods: Dermoscopic images of 108 lesions were evaluated via the Internet by 40 experienced dermoscopists using a 2-step diagnostic procedure. The first-step algorithm distinguished melanocytic versus nonmelanocytic lesions. The second step in the diagnostic procedure used 4 algorithms (pattern analysis, ABCD rule, Menzies method, and 7-point checklist) to distinguish melanoma versus benign melanocytic lesions. κ Values, log odds ratios, sensitivity, specificity, and positive likelihood ratios were estimated for all diagnostic algorithms and dermoscopic features. Results: Interobserver agreement was fair to good for all diagnostic methods, but it was poor for the majority of dermoscopic criteria. Intraobserver agreement was good to excellent for all algorithms and features considered. Pattern analysis allowed the best diagnostic performance (positive likelihood ratio: 5.1), whereas alternative algorithms revealed comparable sensitivity but less specificity. Interobserver agreement on management decisions made by dermoscopy was fairly good (mean κ value: 0.53). Conclusion: The virtual Consensus Net Meeting on Dermoscopy represents a valid tool for better standardization of the dermoscopic terminology and, moreover, opens up a new territory for diagnosing and managing pigmented skin lesions. (J Am Acad Dermatol 2003;48:679-93.) J Am Acad Dermatol 2003;48:679-93.

A case of a patient with malignant acanthosis nigricans in association with carcinoma of the lung is reported, documenting successful palliative treatment of the warty papillomatous thickening of the lips with electrocautery.

In this paper, a new approach to computer supported diagnosis of skin tumors in dermatology is presented. High resolution skin surface profiles are analyzed to recognize malignant melanomas and nevocytic nevi (moles), automatically. In the first step, several types of features are extracted by 2D image analysis methods characterizing the structure of skin surface profiles: texture features based on cooccurrence matrices, Fourier features and fractal features. Then, feature selection algorithms are applied to determine suitable feature subsets for the recognition process. Feature selection is described as an optimization problem and several approaches including heuristic strategies, greedy and genetic algorithms are compared. As quality measure for feature subsets, the classification rate of the nearest neighbor classifier computed with the leaving-one-out method is used. Genetic algorithms show the best results. Finally, neural networks with error back-propagation as learning paradigm are trained using the selected feature sets. Different network topologies, learning parameters and pruning algorithms are investigated to optimize the classification performance of the neural classifiers. With the optimized recognition system a classification performance of 97.7% is achieved.

Laser profilometry offers new possibilities to improve non-invasive tumor diagnostics in dermatology. In this paper, a new approach to computer-supported analysis and interpretation of high-resolution skin-surface profiles of melanomas and nevocellular nevi is presented. Image analysis methods are used to describe the profile‘s structures by texture parameters based on co-occurrence matrices, features extracted from the Fourier power spectrum, and fractal features. Different feature selection strategies, including genetic algorithms, are applied to determine the best possible subsets of features for the classification task. Several architectures of multilayer perceptrons with error back-propagation as learning paradigm are trained for the automatic recognition of melanomas and nevi. Furthermore, network-pruning algorithms are applied to optimize the network topology. In the study, the best neural classifier showed an error rate of 4.5% and was obtained after network pruning. The smallest error rate in all, of 2.3%, was achieved with nearest neighbor classification.

A new approach to computer supported recognition of melanoma and naevocytic naevi based on high resolution skin surface profiles is presented. Profiles are generated by sampling an area of 4 X 4 mm2 at a resolution of 125 sample points per mm with a laser profilometer at a vertical resolution of 0.1 micrometers . With image analysis algorithms Haralick's texture parameters, Fourier features and features based on fractal analysis are extracted. In order to improve classification performance, a subsequent feature selection process is applied to determine the best possible subset of features. Genetic algorithms are optimized for the feature selection process, and results of different approaches are compared. As quality measure for feature subsets, the error rate of the nearest neighbor classifier estimated with the leaving-one-out method is used. In comparison to heuristic strategies and greedy algorithms, genetic algorithms show the best results for the feature selection problem. After feature selection, several architectures of feed forward neural networks with error back-propagation are evaluated. Classification performance of the neural classifier is optimized using different topologies, learning parameters and pruning algorithms. The best neural classifier achieved an error rate of 4.5% and was found after network pruning. The best result in all with an error rate of 2.3% was obtained with the nearest neighbor classifier.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper describes a new approach to automatic classification of melanocytic tumours based on features extracted from profilometric data. The clinical accuracy of dermatologists in identifying these tumours is only approximately 75%. Automatic classification is based on high resolution skin surface profiles of 4×4 mm2 size with 125 sample points per mm, generated with a laser profilometer. Three categories of profile features are extracted: Textural features, Fourier features and fractal features. Feature selection is performed to determine an optimal feature subset. As a quality measure for a given feature subset, the error rate of the nearest neighbour classifier estimated with the leaving-one-out method is used. With the optimal feature subset, feed forward neural networks with error backpropagation as learning function are trained. Several neural networks with different network topologies and learning parameters were trained to compare the classification performance. A three layer network with one hidden layer consisting of 20 units has shown the best performance of all considered neural networks with a classification error rate of 13.4%. The best results using the nearest neighbour classifier achieved an error rate of 6.8%.

Objectives To describe morphological features of seborrheic keratosis as seen by dermoscopy and to investigate their prevalence. Design Prospective cohort study using macrophotography and dermoscopy for the documentation of seborrheic keratosis. Settings Seborrheic keratoses were prospectively collected in 2 sites: a private practice in Plantation, Fla (site 1), and the Department of Dermatology at the University Hospital Geneva in Switzerland (site 2). Patients A total of 203 pigmented seborrheic keratoses (from 192 patients) with complete documentation were collected (111 from site 1 and 93 from site 2). Interventions Screening for new morphological features of seborrheic keratosis and evaluation of all lesions for the prevalence of these criteria. Main Outcome Measures Identification of new morphological criteria and evaluation of frequency. Results A total of 15 morphological dermoscopic criteria were identified. Standard criteria such as milialike cysts and comedolike openings were found in a high number of cases (135 and 144, respectively). We found network and networklike structures to be present in 94 lesions (46%). Using standard diagnostic criteria for seborrheic keratosis, 30 lesions would not have been diagnosed as such. Conclusions The classic dermoscopic criteria for seborrheic keratosis (milialike cysts and comedolike openings) have a high prevalence but the use of additional dermoscopic criteria such as fissures, hairpin blood vessels, sharp demarcation, and moth-eaten borders improves the diagnostic accuracy. The proper identification of pigment network and networklike structures is important for the correct diagnosis.