Delphi Consensus Among International Experts on the Diagnosis, Management, and Surveillance for Lentigo Maligna

INTRODUCTION

Melanoma of the lentigo maligna (LM) type is challenging. There is lack of consensus on the optimal diagnosis, treatment, and follow-up.

OBJECTIVES

To obtain general consensus on the diagnosis, treatment, and follow-up for LM.

METHODS

A modified Delphi method was used. The invited participants were either members of the International Dermoscopy Society, academic experts, or authors of published articles relating to skin cancer and melanoma. Participants were required to respond across three rounds using a 4-point Likert scale). Consensus was defined as >75% of participants agreeing/strongly agreeing or disagreeing/strongly disagreeing.

RESULTS

Of the 31 experts invited to participate in this Delphi study, 29 participants completed Round 1 (89.9% response rate), 25/31 completed Round 2 (77.5% response rate), and 25/31 completed Round 3 (77.5% response rate). Experts agreed that LM diagnosis should be based on a clinical and dermatoscopic approach (92%) followed by a biopsy. The most appropriate primary treatment of LM was deemed to be margin-controlled surgery (83.3%), although non-surgical modalities, especially imiquimod, were commonly used either as alternative off-label primary treatment in selected patients or as adjuvant therapy following surgery; 62% participants responded life-long clinical follow-up was needed for LM.

CONCLUSIONS

Clinical and histological diagnosis of LM is challenging and should be based on macroscopic, dermatoscopic, and RCM examination followed by a biopsy. Different treatment modalities and follow-up should be carefully discussed with the patient.

Observational study investigating the level of support from a convolutional neural network in face and scalp lesions deemed diagnostically 'unclear' by dermatologists

BACKGROUND

The clinical diagnosis of face and scalp lesions (FSL) is challenging due to overlapping features. Dermatologists encountering diagnostically 'unclear' lesions may benefit from artificial intelligence support via convolutional neural networks (CNN).

METHODS

In a web-based classification task, dermatologists (n = 64) diagnosed a convenience sample of 100 FSL as 'benign', 'malignant', or 'unclear' and indicated their management decisions ('no action', 'follow-up', 'treatment/excision'). A market-approved CNN (Moleanalyzer-Pro®, FotoFinder Systems, Germany) was applied for binary classifications (benign/malignant) of dermoscopic images.

RESULTS

After reviewing one dermoscopic image per case, dermatologists labelled 562 of 6400 diagnoses (8.8%) as 'unclear' and mostly managed these by follow-up examinations (57.3%, n = 322) or excisions (42.5%, n = 239). Management was incorrect in 58.8% of 291 truly malignant cases (171 'follow-up' or 'no action') and 43.9% of 271 truly benign cases (119 'excision'). Accepting CNN classifications in unclear cases would have reduced false management decisions to 4.1% in truly malignant and 31.7% in truly benign lesions (both p < 0.01). After receiving full case information 239 diagnoses (3.7%) remained 'unclear' to dermatologists, now triggering more excisions (72.0%) than follow-up examinations (28.0%). These management decisions were incorrect in 32.8% of 116 truly malignant cases and 76.4% of 123 truly benign cases. Accepting CNN classifications would have reduced false management decisions to 6.9% in truly malignant lesions and to 38.2% in truly benign cases (both p < 0.01).

CONCLUSIONS

Dermatologists mostly managed diagnostically 'unclear' FSL by treatment/excision or follow-up examination. Following CNN classifications as guidance in unclear cases seems suitable to significantly reduce incorrect decisions.

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

Introduction

In patients with multiple nevi, sequential imaging using total body skin photography (TBSP) coupled with digital dermoscopy (DD) documentation reduces unnecessary excisions and improves the early detection of melanoma. Correct patient selection is essential for optimizing the efficacy of this diagnostic approach.

Objectives

The purpose of the study was to identify, via expert consensus, the best indications for TBSP and DD follow-up.

Methods

This study was performed on behalf of the International Dermoscopy Society (IDS). We attained consensus by using an e-Delphi methodology. The panel of participants included international experts in dermoscopy. In each Delphi round, experts were asked to select from a list of indications for TBSP and DD.

Results

Expert consensus was attained after 3 rounds of Delphi. Participants considered a total nevus count of 60 or more nevi or the presence of a CDKN2A mutation sufficient to refer the patient for digital monitoring. Patients with more than 40 nevi were only considered an indication in case of personal history of melanoma or red hair and/or a MC1R mutation or history of organ transplantation.

Conclusions

Our recommendations support clinicians in choosing appropriate follow-up regimens for patients with multiple nevi and in applying the time-consuming procedure of sequential imaging more efficiently. Further studies and real-life data are needed to confirm the usefulness of this list of indications in clinical practice.

Does sex matter? Analysis of sex-related differences in the diagnostic performance of a market-approved convolutional neural network for skin cancer detection

BACKGROUND

Advances in biomedical artificial intelligence may introduce or perpetuate sex and gender discriminations. Convolutional neural networks (CNN) have proven a dermatologist-level performance in image classification tasks but have not been assessed for sex and gender biases that may affect training data and diagnostic performance. In this study, we investigated sex-related imbalances in training data and diagnostic performance of a market-approved CNN for skin cancer classification (Moleanalyzer Pro®, Fotofinder Systems GmbH, Bad Birnbach, Germany).

METHODS

We screened open-access dermoscopic image repositories widely used for CNN training for distribution of sex. Moreover, the sex-related diagnostic performance of the market-approved CNN was tested in 1549 dermoscopic images stratified by sex (female n = 773; male n = 776).

RESULTS

Most open-access repositories showed a marked under-representation of images originating from female (40%) versus male (60%) patients. Despite these imbalances and well-known sex-related differences in skin anatomy or skin-directed behaviour, the tested CNN achieved a comparable sensitivity of 87.0% [80.9%-91.3%] versus 87.1% [81.1%-91.4%], specificity of 98.7% [97.4%-99.3%] versus 96.9% [95.2%-98.0%] and ROC-AUC of 0.984 [0.975-0.993] versus 0.979 [0.969-0.988] in dermoscopic images of female versus male origin, respectively. In the sample at hand, sex-related differences in ROC-AUCs were not statistically significant in the per-image analysis nor in an additional per-individual analysis (p ≥ 0.59).

CONCLUSION

Design and training of artificial intelligence algorithms for medical applications should generally acknowledge sex and gender dimensions. Despite sex-related imbalances in open-access training data, the diagnostic performance of the tested CNN showed no sex-related bias in the classification of skin lesions.

Collective human intelligence outperforms artificial intelligence in a skin lesion classification task

BACKGROUND AND OBJECTIVES

Convolutional neural networks (CNN) enable accurate diagnosis of medical images and perform on or above the level of individual physicians. Recently, collective human intelligence (CoHI) was shown to exceed the diagnostic accuracy of individuals. Thus, diagnostic performance of CoHI (120 dermatologists) versus individual dermatologists versus two state-of-the-art CNN was investigated.

PATIENTS AND METHODS

Cross-sectional reader study with presentation of 30 clinical cases to 120 dermatologists. Six diagnoses were offered and votes collected via remote voting devices (quizzbox®, Quizzbox Solutions GmbH, Stuttgart, Germany). Dermatoscopic images were classified by a binary and multiclass CNN (FotoFinder Systems GmbH, Bad Birnbach, Germany). Three sets of diagnostic classifications were scored against ground truth: (1) CoHI, (2) individual dermatologists, and (3) CNN.

RESULTS

CoHI attained a significantly higher accuracy [95 % confidence interval] (80.0 % [62.7 %-90.5 %]) than individual dermatologists (75.7 % [73.8 %-77.5 %]) and CNN (70.0 % [52.1 %-83.3 %]; all P < 0.001) in binary classifications. Moreover, CoHI achieved a higher sensitivity (82.4 % [59.0 %-93.8 %]) and specificity (76.9 % [49.7 %-91.8 %]) than individual dermatologists (sensitivity 77.8 % [75.3 %-80.2 %], specificity 73.0 % [70.6 %-75.4 %]) and CNN (sensitivity 70.6 % [46.9 %-86.7 %], specificity 69.2 % [42.4 %-87.3 %]). The diagnostic accuracy of CoHI was superior to that of individual dermatologists (P < 0.001) in multiclass evaluation, with the accuracy of the latter comparable to multiclass CNN.

CONCLUSIONS

Our analysis revealed that the majority vote of an interconnected group of dermatologists (CoHI) outperformed individuals and CNN in a demanding skin lesion classification task.

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.

Skin lesions of face and scalp - Classification by a market-approved convolutional neural network in comparison with 64 dermatologists

BACKGROUND

The clinical differentiation of face and scalp lesions (FSLs) is challenging even for trained dermatologists. Studies comparing the diagnostic performance of a convolutional neural network (CNN) with dermatologists in FSL are lacking.

METHODS

A market-approved CNN (Moleanalyzer-Pro, FotoFinder Systems) was used for binary classifications of 100 dermoscopic images of FSL. The same lesions were used in a two-level reader study including 64 dermatologists (level I: dermoscopy only; level II: dermoscopy, clinical close-up images, textual information). Primary endpoints were the CNN's sensitivity and specificity in comparison with the dermatologists' management decisions in level II. Generalizability of the CNN results was tested by using four additional external data sets.

RESULTS

The CNN's sensitivity, specificity and ROC AUC were 96.2% [87.0%-98.9%], 68.8% [54.7%-80.1%] and 0.929 [0.880-0.978], respectively. In level II, the dermatologists' management decisions showed a mean sensitivity of 84.2% [82.2%-86.2%] and specificity of 69.4% [66.0%-72.8%]. When fixing the CNN's specificity at the dermatologists' mean specificity (69.4%), the CNN's sensitivity (96.2% [87.0%-98.9%]) was significantly higher than that of dermatologists (84.2% [82.2%-86.2%]; p < 0.001). Dermatologists of all training levels were outperformed by the CNN (all p < 0.001). In confirmation, the CNN's accuracy (83.0%) was significantly higher than dermatologists' accuracies in level II management decisions (all p < 0.001). The CNN's performance was largely confirmed in three additional external data sets but particularly showed a reduced specificity in one Australian data set including FSL on severely sun-damaged skin.

CONCLUSIONS

When applied as an assistant system, the CNN's higher sensitivity at an equivalent specificity may result in an improved early detection of face and scalp skin cancers.

Dermal fillers do not induce upregulation of NLRP3 inflammasomes or expression of inflammatory cytokines in granulomas

BACKGROUND

Filling materials have increasingly been used in aesthetics over the last decades. Understanding the pathophysiology of granuloma formation as a very relevant unwanted side effect of filler application may be essential to help avoid these adverse events.

AIMS

Our aim was to investigate the role of the inflammasome in the formation of filler granuloma, as a central column of the innate immune response.

METHODS

RPMI 1640 medium was used for growth of THP-1 cells and the induction of THP-1 macrophages. Sonication was applied in order to crush the acrylic particles of the filler. ELISA was the method of analysis for the specific cytokines. Biopsy specimens of filler granuloma were analyzed by various immunohistochemical methods. GraphPad Prism 5 software was used for the statistical data analysis.

RESULTS

Neither was the sensor NALP3 overexpressed, nor could an elevated expression of cleaved IL-1β, IL-18, or IFN-γ be detected. Furthermore, no increased expression of IL-8 or IL-1β was detectable in vitro.

CONCLUSION

No increased inflammasome activation could be observed; however, filler granulomas were infiltrated with granulocytes and macrophages. Therefore, we speculate that an unspecific immune response might be the key player in the formation of filler granuloma.

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.

Differentiation of combined nevi and melanomas: Case-control study with comparative analysis of dermoscopic features

BACKGROUND AND OBJECTIVES

Combined nevi (CN) show two or more components of major nevus subtypes and simulate melanomas. We investigated a panel of dermoscopic features and three dermoscopic algorithms for differentiating CN from melanomas.

PATIENTS AND METHODS

Retrospective, blinded case-control study using dermoscopic images of 36 CN and 36 melanoma controls. Twenty-one dermoscopic features validated for the diagnosis of melanocytic lesions, the number of colors, and three dermoscopic algorithms were investigated (ABCD rule of dermoscopy, Menzies scoring method, 7-point checklist).

RESULTS

Five of seven features indicative of nevi were observed significantly more frequently in CN than in melanomas (all p < 0.05) and two were exclusively found in CN. Eleven out of 14 features indicative of melanomas were observed significantly more frequently in melanomas than in CN (all p < 0.03) and five were exclusively found in melanomas. The mean (± SD) number of colors in CN was lower than in melanomas (2.1 ± 0.6 versus 3.4 ± 0.7; p < 0.001). Among tested algorithms the ABCD rule of dermoscopy performed best (sensitivity 91.7 %, specificity 77.8 %).

CONCLUSIONS

The ABCD rule of dermoscopy differentiated CN from melanomas most efficiently. Additional knowledge of dermoscopic features to be expected exclusively in either CN or melanomas should help dermatologists to make a correct clinical diagnosis.

Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition

Importance

Deep learning convolutional neural networks (CNNs) have shown a performance at the level of dermatologists in the diagnosis of melanoma. Accordingly, further exploring the potential limitations of CNN technology before broadly applying it is of special interest.

Objective

To investigate the association between gentian violet surgical skin markings in dermoscopic images and the diagnostic performance of a CNN approved for use as a medical device in the European market.

Design and Setting

A cross-sectional analysis was conducted from August 1, 2018, to November 30, 2018, using a CNN architecture trained with more than 120 000 dermoscopic images of skin neoplasms and corresponding diagnoses. The association of gentian violet skin markings in dermoscopic images with the performance of the CNN was investigated in 3 image sets of 130 melanocytic lesions each (107 benign nevi, 23 melanomas).

Exposures

The same lesions were sequentially imaged with and without the application of a gentian violet surgical skin marker and then evaluated by the CNN for their probability of being a melanoma. In addition, the markings were removed by manually cropping the dermoscopic images to focus on the melanocytic lesion.

Main Outcomes and Measures

Sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the CNN's diagnostic classification in unmarked, marked, and cropped images.

Results

In all, 130 melanocytic lesions (107 benign nevi and 23 melanomas) were imaged. In unmarked lesions, the CNN achieved a sensitivity of 95.7% (95% CI, 79%-99.2%) and a specificity of 84.1% (95% CI, 76.0%-89.8%). The ROC AUC was 0.969. In marked lesions, an increase in melanoma probability scores was observed that resulted in a sensitivity of 100% (95% CI, 85.7%-100%) and a significantly reduced specificity of 45.8% (95% CI, 36.7%-55.2%, P < .001). The ROC AUC was 0.922. Cropping images led to the highest sensitivity of 100% (95% CI, 85.7%-100%), specificity of 97.2% (95% CI, 92.1%-99.0%), and ROC AUC of 0.993. Heat maps created by vanilla gradient descent backpropagation indicated that the blue markings were associated with the increased false-positive rate.

Conclusions and Relevance

This study's findings suggest that skin markings significantly interfered with the CNN's correct diagnosis of nevi by increasing the melanoma probability scores and consequently the false-positive rate. A predominance of skin markings in melanoma training images may have induced the CNN's association of markings with a melanoma diagnosis. Accordingly, these findings suggest that skin markings should be avoided in dermoscopic images intended for analysis by a CNN.

Trial Registration

German Clinical Trial Register (DRKS) Identifier: DRKS00013570.

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.

Second-harmonic generation as a probe for structural and electronic properties of buried GaP/Si(0 0 1) interfaces

Optical second-harmonic generation is demonstrated to be a sensitive probe of the buried interface between the lattice-matched semiconductors gallium phosphide and silicon with (0 0 1) orientation. Ex situ rotational anisotropy measurements on GaP/Si heterostructures show a strong isotropic component of the second-harmonic response not present for pure Si(0 0 1) or GaP(0 0 1). The strength of the overlaying anisotropic response directly correlates with the quality of the interface as determined by atomically resolved scanning transmission electron microscopy. Systematic comparison of samples fabricated under different growth conditions in metal-organic vapor phase epitaxy reveals that the anisotropy for different polarization combinations can be used as a selective fingerprint for the occurrence of anti-phase domains and twins. This all-optical technique can be applied as an in situ and non-invasive monitor even during growth.

Enhanced Absorption by Linewidth Narrowing in Optically Excited Type-II Semiconductor Heterostructures

We experimentally report a surprising linewidth narrowing of the direct exitonic 1  s heavy-hole transition in a type-II quantum well system. This narrowing, which builds up on a pico- to nanosecond timescale, causes a transient enhanced absorption at the spectral peak position of the excitonic resonance. We discuss how this effect depends on experimental parameters such as excitation density, temperature, and barrier width. We cannot attribute this effect to known physical mechanisms.

The status of dermoscopy in Germany - results of the cross-sectional Pan-Euro-Dermoscopy Study

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.

High-temperature operation of electrical injection type-II (GaIn)As/Ga(AsSb)/(GaIn)As "W"-quantum well lasers emitting at 1.3 µm

Electrical injection lasers emitting in the 1.3 μm wavelength regime based on (GaIn)As/Ga(AsSb)/(GaIn)As type-II double "W"-quantum well heterostructures grown on GaAs substrate are demonstrated. The structure is designed by applying a fully microscopic theory and fabricated using metal organic vapor phase epitaxy. Temperature-dependent electroluminescence measurements as well as broad-area edge-emitting laser studies are carried out in order to characterize the resulting devices. Laser emission based on the fundamental type-II transition is demonstrated for a 975 μm long laser bar in the temperature range between 10 °C and 100 °C. The device exhibits a differential efficiency of 41 % and a threshold current density of 1.0 kA/cm2 at room temperature. Temperature-dependent laser studies reveal characteristic temperatures of T0 = (132 ± 3) K over the whole temperature range and T1 = (159 ± 13) K between 10 °C and 70 °C and T1 = (40 ± 1) K between 80 °C and 100 °C.

Validity and Reliability of Dermoscopic Criteria Used to Differentiate Nevi From Melanoma: A Web-Based International Dermoscopy Society Study

IMPORTANCE

The comparative diagnostic performance of dermoscopic algorithms and their individual criteria are not well studied.

OBJECTIVES

To analyze the discriminatory power and reliability of dermoscopic criteria used in melanoma detection and compare the diagnostic accuracy of existing algorithms.

DESIGN, SETTING, AND PARTICIPANTS

This was a retrospective, observational study of 477 lesions (119 melanomas [24.9%] and 358 nevi [75.1%]), which were divided into 12 image sets that consisted of 39 or 40 images per set. A link on the International Dermoscopy Society website from January 1, 2011, through December 31, 2011, directed participants to the study website. Data analysis was performed from June 1, 2013, through May 31, 2015. Participants included physicians, residents, and medical students, and there were no specialty-type or experience-level restrictions. Participants were randomly assigned to evaluate 1 of the 12 image sets.

MAIN OUTCOMES AND MEASURES

Associations with melanoma and intraclass correlation coefficients (ICCs) were evaluated for the presence of dermoscopic criteria. Diagnostic accuracy measures were estimated for the following algorithms: the ABCD rule, the Menzies method, the 7-point checklist, the 3-point checklist, chaos and clues, and CASH (color, architecture, symmetry, and homogeneity).

RESULTS

A total of 240 participants registered, and 103 (42.9%) evaluated all images. The 110 participants (45.8%) who evaluated fewer than 20 lesions were excluded, resulting in data from 130 participants (54.2%), 121 (93.1%) of whom were regular dermoscopy users. Criteria associated with melanoma included marked architectural disorder (odds ratio [OR], 6.6; 95% CI, 5.6-7.8), pattern asymmetry (OR, 4.9; 95% CI, 4.1-5.8), nonorganized pattern (OR, 3.3; 95% CI, 2.9-3.7), border score of 6 (OR, 3.3; 95% CI, 2.5-4.3), and contour asymmetry (OR, 3.2; 95% CI, 2.7-3.7) (P < .001 for all). Most dermoscopic criteria had poor to fair interobserver agreement. Criteria that reached moderate levels of agreement included comma vessels (ICC, 0.44; 95% CI, 0.40-0.49), absence of vessels (ICC, 0.46; 95% CI, 0.42-0.51), dark brown color (ICC, 0.40; 95% CI, 0.35-0.44), and architectural disorder (ICC, 0.43; 95% CI, 0.39-0.48). The Menzies method had the highest sensitivity for melanoma diagnosis (95.1%) but the lowest specificity (24.8%) compared with any other method (P < .001). The ABCD rule had the highest specificity (59.4%). All methods had similar areas under the receiver operating characteristic curves.

CONCLUSIONS AND RELEVANCE

Important dermoscopic criteria for melanoma recognition were revalidated by participants with varied experience. Six algorithms tested had similar but modest levels of diagnostic accuracy, and the interobserver agreement of most individual criteria was poor.

Recombination dynamics of type-II excitons in (Ga,In)As/GaAs/Ga(As,Sb) heterostructures

(Ga,In)As/GaAs/Ga(As,Sb) multi-quantum well heterostructures have been investigated using continuous wave and time-resolved photoluminescence spectroscopy at various temperatures. A complex interplay was observed between the excitonic type-II transitions with electrons in the (Ga,In)As well and holes in the Ga(As,Sb) well and the type-I excitons in the (Ga,In)As and Ga(As,Sb) wells. The type-II luminescence exhibits a strongly non-exponential temporal behavior below a critical temperature of T _c = 70 K. The transients were analyzed in the framework of a rate-equation model. It was found that the exciton relaxation and hopping in the localized states of the disordered ternary Ga(As,Sb) are the decisive processes to describe the dynamics of the type-II excitons correctly.

Clinical and dermoscopic clues to differentiate pigmented nail bands: an International Dermoscopy Society study

BACKGROUND

Longitudinal melanonychia might be difficult to differentiate and the use of dermoscopy can be useful for the preoperative evaluation and management decision.

OBJECTIVES

The aim of our study was to investigate clinical and dermoscopic criteria of acquired longitudinal melanonychia in adults to identify the best predictors of melanoma using a multivariate analysis and to explore eventual new dermoscopic criteria for nail melanoma diagnosis.

METHODS

In this retrospective observational study, 82 histopathologically diagnosed, acquired nail pigmented bands were collected and examined. All variables were included in the analysis and examined as possible predictors of nail melanoma. Both univariate and multivariable analyses have been performed.

RESULTS

Among 82 cases, 25 were diagnosed as nail melanoma and 57 as benign lesions (including 32 melanocytic nevi and 25 benign melanocytic hyperplasia). Melanoma cases were significantly associated with a width of the pigmented band higher than 2/3 of the nail plate, grey and black colours, irregularly pigmented lines, Hutchinson and micro-Hutchinson signs, and nail dystrophy. Granular pigmentation, a newly defined dermoscopic criterion, was found in 40% of melanomas and only in 3.51% of benign lesions.

CONCLUSIONS

Dermoscopic examination of longitudinal melanonychia provides useful information that could help clinicians to improve melanoma recognition.

Standardization of terminology in dermoscopy/dermatoscopy: Results of the third consensus conference of the International Society of Dermoscopy

BACKGROUND

Evolving dermoscopic terminology motivated us to initiate a new consensus.

OBJECTIVE

We sought to establish a dictionary of standardized terms.

METHODS

We reviewed the medical literature, conducted a survey, and convened a discussion among experts.

RESULTS

Two competitive terminologies exist, a more metaphoric terminology that includes numerous terms and a descriptive terminology based on 5 basic terms. In a survey among members of the International Society of Dermoscopy (IDS) 23.5% (n = 201) participants preferentially use descriptive terminology, 20.1% (n = 172) use metaphoric terminology, and 484 (56.5%) use both. More participants who had been initially trained by metaphoric terminology prefer using descriptive terminology than vice versa (9.7% vs 2.6%, P < .001). Most new terms that were published since the last consensus conference in 2003 were unknown to the majority of the participants. There was uniform consensus that both terminologies are suitable, that metaphoric terms need definitions, that synonyms should be avoided, and that the creation of new metaphoric terms should be discouraged. The expert panel proposed a dictionary of standardized terms taking account of metaphoric and descriptive terms.

LIMITATIONS

A consensus seeks a workable compromise but does not guarantee its implementation.

CONCLUSION

The new consensus provides a revised framework of standardized terms to enhance the consistent use of dermoscopic terminology.

Novel type-II material system for laser applications in the near-infrared regime

The design and experimental realization of a type-II "W"-multiple quantum well heterostructure for emission in the λ > 1.2 μm range is presented. The experimental photoluminescence spectra for different excitation intensities are analyzed using microscopic quantum theory. On the basis of the good theory-experiment agreement, the gain properties of the system are computed using the semiconductor Bloch equations. Gain values comparable to those of type-I systems are obtained.

When all you have is a dermatoscope- start looking at the nails

Pigmented and non-pigmented nail alterations are a frequent challenge for dermatologists. A profound knowledge of clinical and dermatoscopic features of nail disorders is crucial because a range of differential diagnoses and even potentially life-threatening diseases are possible underlying causes. Nail matrix melanocytes of unaffected individuals are in a dormant state, and, therefore, fingernails and toenails physiologically are non-pigmented. The formation of continuous, longitudinal pigmented streaks (longitudinal melanonychia) may either be caused by a benign activation of matrix melanocytes (e.g., as a result of trauma, inflammation, or adverse drug reactions) or by a true melanocytic proliferation (e.g., in a nevus or melanoma). In general, non-continuous nail alterations, affecting only limited parts of the nail apparatus, are most frequently of non-melanocytic origin. Important and common differential diagnoses in these cases are subungual hemorrhage or onychomycosis. In addition, foreign bodies, bacterial infections, traumatic injuries, or artificial discolorations of the nail unit may less frequently cause non-continuous nail alterations. Many systemic diseases that may also show involvement of the nails (e.g., psoriasis, atopic dermatitis, lichen planus, alopecia areata) tend to induce alterations in numerous if not all nails of the hands and feet. A similar extensive and generalized alteration of nails has been reported after treatment with a number of systemic drugs, especially antibiotics and cytostatics. Benign or malignant neoplasms that may also affect the nail unit include glomus tumor, Bowen's disease, squamous cell carcinoma, and rare collision tumors. This review aims to assist clinicians in correctly evaluating and diagnosing nail disorders with the help of dermatoscopy.

Cold atmospheric plasma devices for medical issues

Cold atmospheric plasma science is an innovative upcoming technology for the medical sector. The plasma composition and subsequent effects on cells, tissues and pathogens can vary enormously depending on the plasma source, the plasma settings and the ambient conditions. Cold atmospheric plasmas consist of a highly reactive mix of ions and electrons, reactive molecules, excited species, electric fields and to some extent also UV radiation. In the last year, this partly ionized gas has been demonstrated to have a broad antimicrobial activity, while resistance and resistance development are unlikely. Furthermore, recent research has indicated that plasmas also have a strong influence on various cell lines and cell functions, including anticancer properties. This review summarizes the major plasma designs available and their main benefits, as well as assessing possible risks of this new technology.

Cold atmospheric plasma for local infection control and subsequent pain reduction in a patient with chronic post-operative ear infection

Following surgery of cholesteatoma, a patient developed a chronic infection of the external auditory canal, including extended-spectrum β-lactamase producing Escherichia coli, which caused severe pain. The application of cold atmospheric plasma resulted in a significant reduction in pain and clearance of bacterial carriage, allowing antibiotics and analgesics to be ceased.