Regulatory T Cells Prevent Neutrophilic Infiltration of Skin during Contact Hypersensitivity Reactions by Strengthening the Endothelial Barrier

The healing phase of contact hypersensitivity reactions is critically dependent on regulatory T cells (Tregs), but even the early inflammatory phase, that is, 6-24 hours after induction of a contact hypersensitivity reaction, is susceptible to Treg-mediated suppression. To investigate the underlying mechanisms, we injected Tregs before the challenge and analyzed the skin-infiltrating cells as early as 6 hours later. Early on, we found mainly neutrophils in the challenged skin, but only a few T cells. This influx of neutrophils was blocked by the injection of Tregs, indicating that they were able to prevent the first wave of leukocytes, which are responsible for starting an immune reaction. As an underlying mechanism, we identified that Tregs can tighten endothelial junctions by inducing intracellular cAMP, leading to protein kinase A-RhoA‒dependent signaling. This eventually reorganizes endothelial junction proteins, such as Notch3, Nectin 2, Filamin B, and VE-cadherin, all of which contribute to the tightening of the endothelial barrier. In summary, Tregs prevent the leakage of proinflammatory cells from and into the tissue, which establishes a mechanism to downregulate immune reactions.

Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition

BACKGROUND

Studies systematically unravelling possible causes for false diagnoses of deep learning convolutional neural networks (CNNs) are scarce, yet needed before broader application.

OBJECTIVES

The objective of the study was to investigate whether scale bars in dermoscopic images are associated with the diagnostic accuracy of a market-approved CNN.

METHODS

This cross-sectional analysis applied a CNN trained with more than 150,000 images (Moleanalyzer-pro®, FotoFinder Systems Inc., Bad Birnbach, Germany) to investigate seven dermoscopic image sets depicting the same 130 melanocytic lesions (107 nevi, 23 melanomas) without or with digitally superimposed scale bars of different manufacturers. Sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for the CNN's binary classification of images with or without superimposed scale bars were assessed.

RESULTS

Six dermoscopic image sets with different scale bars and one control set without scale bars (overall 910 images) were submitted to CNN analysis. In images without scale bars, the CNN attained a sensitivity [95% confidence interval] of 87.0% [67.9%-95.5%] and a specificity of 87.9% [80.3%-92.8%]. ROC AUC was 0.953 [0.914-0.992]. Scale bars were not associated with significant changes in sensitivity (range 87%-95.7%, all p ≥ 1.0). However, four scale bars induced a decrease of the CNN's specificity (range 0%-43.9%, all p < 0.001). Moreover, ROC AUC was significantly reduced by two scale bars (range 0.520-0.848, both p ≤ 0.042).

CONCLUSIONS

Superimposed scale bars in dermoscopic images may impair the CNN's diagnostic accuracy, mostly by increasing the rate of the false-positive diagnoses. We recommend avoiding scale bars in images intended for CNN analysis unless specific measures counteracting effects are implemented.

CLINICAL TRIAL NUMBER

This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; URL: https://www.drks.de/drks_web/).

[Purpura annularis telangiectodes : Case report and review of the literature]

Pupura annularis telangiectodes (PAT) is a rare entity belonging to the spectrum of the pigmented purpuric dermatoses. PAT presents clinically as symmetric, annular erythema with teleangiectasia on the lower extremities and preferably affects young women. Histology usually reveals extravasated erythrocytes accompanied by a lymphocyte-dominated inflammatory infiltrate in the superficial dermis. Medication can often be identified as causative. In patients with idiopathic disease, topical corticoidsteroids are the treatment of choice. Compression therapy may be supportive.

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.

Patterns of care and follow-up care of patients with uveal melanoma in German-speaking countries: a multinational survey of the German Dermatologic Cooperative Oncology Group (DeCOG)

Purpose

Uveal melanoma (UM) is an orphan cancer of high unmet medical need. Current patterns of care and surveillance remain unclear as they are situated in an interdisciplinary setting.

Methods

A questionnaire addressing the patterns of care and surveillance in the management of patients with uveal melanoma was distributed to 70 skin cancer centers in Austria, Germany and Switzerland. Frequency distributions of responses for each item of the questionnaire were calculated.

Results

44 of 70 (62.9%) skin cancer centers completed the questionnaire. Thirty-nine hospitals were located in Germany (88.6%), three in Switzerland (6.8%) and two in Austria (4.5%). The majority (68.2%) represented university hospitals. Most patients with metastatic disease were treated in certified skin cancer centers (70.7%, 29/41). Besides, the majority of patients with UM were referred to the respective skin cancer center by ophthalmologists (87.2%, 34/39). Treatment and organization of follow-up of patients varied across the different centers. 35.1% (14/37) of the centers stated to not perform any screening measures.

Conclusion

Treatment patterns of patients with uveal melanoma in Germany, Austria and Switzerland remain extremely heterogeneous. A guideline for the treatment and surveillance is urgently needed.

The evolving field of Dermato-oncology and the role of dermatologists: Position Paper of the EADO, EADV and Task Forces, EDF, IDS, EBDV-UEMS and EORTC Cutaneous Lymphoma Task Force

BACKGROUND

The incidence of skin cancers has been increasing steadily over the last decades. Although there have been significant breakthroughs in the management of skin cancers with the introduction of novel diagnostic tools and innovative therapies, skin cancer mortality, morbidity and costs heavily burden the society.

OBJECTIVE

Members of the European Association of Dermato-Oncology, European Academy of Dermatology and Venereology, International Dermoscopy Society, European Dermatology Forum, European Board of Dermatovenereology of the European Union of Medical Specialists and EORTC Cutaneous Lymphoma Task Force have joined this effort to emphasize the fundamental role that the specialist in Dermatology-Venereology has in the diagnosis and management of different types of skin cancer. We review the role of dermatologists in the prevention, diagnosis, treatment and follow-up of patients with melanoma, non-melanoma skin cancers and cutaneous lymphomas, and discuss approaches to optimize their involvement in effectively addressing the current needs and priorities of dermato-oncology.

DISCUSSION

Dermatologists play a crucial role in virtually all aspects of skin cancer management including the implementation of primary and secondary prevention, the formation of standardized pathways of care for patients, the establishment of specialized skin cancer treatment centres, the coordination of an efficient multidisciplinary team and the setting up of specific follow-up plans for patients.

CONCLUSION

Skin cancers represent an important health issue for modern societies. The role of dermatologists is central to improving patient care and outcomes. In view of the emerging diagnostic methods and treatments for early and advanced skin cancer, and considering the increasingly diverse skills, knowledge and expertise needed for managing this heterogeneous group of diseases, dermato-oncology should be considered as a specific subspecialty of Dermatology-Venereology.

Vascular Endothelial Growth Factor Receptor-3 Expression Predicts Sentinel Node Status in Primary Cutaneous Melanoma

This study analysed the expression of vascular endothelial growth factor-A (VEGF), VEGFR-2, and VEGFR-3 in primary cutaneous melanomas with positive and negative sentinel node status (SLN) (a total of 58 specimens divided into 2 groups of 29 for each status). The specimens were collected from the pathological archive of the department of Dermatology, Venereology and Allergology of the University Medical Center Heidelberg. A quantification score was developed for protein expression, by considering the percentage of positive melanoma cells (0: 0%, 1: up to 1%, 2: 2–10%, 3: 11–50%, and 4: > 50%) in relation to the intensity of staining (0: negative, 1: low, 2: medium, 3: strong). Tumoural VEGFR-3 expression (mean ± standard deviation) in SLN+ tumours (9.62 ± 3.09) was significantly stronger than in SLN– tumours (6.13 ± 3.87; p < 0.001). A binary logistic regression model proved VEGFR-3 expression and tumour thickness to be significant independent predictors of SLN. These data provide evidence that VEGFR-3 expression may play a critical role in the pathogenesis of malignant melanoma and that its investigation may help to improve the selection of patients with primary cutaneous melanoma for sentinel node biopsy.

Systemic immunosuppression in times of COVID-19: Do we need to rethink our standards?

The current SARS‐CoV‐2 pandemic particularly endangers older people with pre‐existing cardiopulmonary and metabolic conditions. However, it is also currently under discussion whether patients under immunosuppressive therapy also have a higher risk of suffering a severe course of the COVID‐19 disease. In principle though, there is currently no data available for a general reduction or pause of immunosuppression in patients with autoimmune diseases because of the SARS‐CoV‐2 pandemic. However, since there is currently neither an effective therapy nor corresponding vaccination protection, the indication for a prolonged immunosuppressive therapy should be made with special care. In particular, immunotherapeutic agents that produce long‐term effects (e.g., rituximab) should be used with special caution. In contrast, immunomodulating substances that do not suppress antiviral immunity (e.g. systemic immunoglobulins, doxycycline), or that have intrinsic effects on SARS‐CoV‐2 (calcineurin inhibitors, chloroquine, hydroxychloroquine) may be useful alternatives.

Systemische Immunsuppression in Zeiten von COVID‐19: Müssen wir umdenken?

Die aktuelle SARS‐CoV‐2 Pandemie gefährdet vor allem ältere Menschen mit kardiopulmonalen und metabolischen Vorerkrankungen. In aktueller Diskussion ist jedoch auch, ob Patienten unter immunsuppressiver Therapie ebenfalls ein höheres Risiko haben, im Fall einer COVID‐19‐Erkrankung einen schweren Krankheitsverlauf zu erleiden. Grundsätzlich gibt es derzeit jedoch keine Datenlage für eine generelle Reduktion oder Pausierung einer Immunsuppression bei Patienten mit Autoimmunerkrankungen in Zeiten der SARS‐CoV‐2‐Pandemie. Da es jedoch derzeit weder eine wirksame Therapie, noch einen entsprechenden Impfschutz gibt, sollten wir uns gezielt mit der Problematik chronisch‐immunsupprimierter Patienten beschäftigen. Um Risiken für Patienten zu minimieren, sollte die Indikation für eine solche Therapie mit besonderer Sorgfalt gestellt werden. Insbesondere sollten Immuntherapeutika, die langfristige Effekte erzeugen (zum Beispiel Rituximab) mit besonderer Vorsicht eingesetzt werden. Demgegenüber könnten immunmodulierende Substanzen, die keine Immunsuppression induzieren (zum Beispiel systemische Immunglobuline, Doxycyclin) oder die intrinsische Wirkungen auf SARS‐CoV‐2 haben (Calcineurininhibitoren, Chloroquin, Hydroxychloroquin), eine sinnvolle Alternative darstellen.

Metabolic reprogramming and angiogenesis in primary cutaneous Merkel cell carcinoma: expression of hypoxia-inducible factor-1α and its central downstream factors

BACKGROUND

Metabolic reprogramming and altered gene expression mediated by hypoxia-inducible factors play crucial roles during tumour growth and progression. Nevertheless, studies analysing the expression of hypoxia-inducible factor-1α and its downstream targets in Merkel cell carcinoma (MCC) are lacking but are warranted to shed more light on MCC pathogenesis and to potentially provide new therapeutic options.

OBJECTIVES

To analyse the immunohistochemical expression of hypoxia-inducible factor-1α (HIF-1α), vascular endothelial growth factor-A (referred to as VEGF throughout the manuscript), VEGF receptor-2 (VEGFR-2), VEGF receptor-3 (VEGFR-3), glucose transporter-1 (Glut-1), monocarboxylate transporter 4 (MCT4) and carbonic anhydrase IX (CAIX) in primary cutaneous MCC.

METHODS

The 16 paraffin-embedded primary cutaneous MCCs (Merkel cell polyomavirus (McPyV) positive/negative: 11/5) were analysed by immunohistochemistry, namely HIF-1α, VEGF, VEGFR-2 (KDR), VEGFR-3 (FLT4), Glut-1, MCT4 and CAIX. An established quantification score (QS) was applied to quantitate the protein expression by considering the percentage of positive tumour cells (0: 0%; 1: up to 1%; 2: 2-10%; 3: 11-50%; 4: >50%) in relation to the staining intensity (0: negative; 1: low; 2: medium; 3: strong).

RESULTS

HIF-1α was expressed in all MCCs and predominantly found at the invading edges of tumour margins. The HIF-1α downstream factors Glut-1, MCT4 and CAIX were expressed in 13 of 16 MCC (81%), 14 of 16 MCC (88%) and 16 of 16 MCC (100%), respectively. Interestingly, VEGF and VEGFR-2 were not expressed in tumour cells, whereas VEGFR-3 was expressed in all MCCs. HIF-1α was expressed significantly stronger in McPyV⁺ tumours (QS: 10.36 ± 2.41) than in McPyV^- tumours (QS: 5.40 ± 1.34; P = 0.002). Similarly, VEGFR-3 was also expressed significantly stronger in McPyV⁺ tumours (QS: 10.00 ± 2.52) than in McPyV^- tumours (QS: 5.40 ± 3.43, P = 0.019).

CONCLUSIONS

Our data provide first evidence for a role of HIF-1α in induced metabolic reprogramming contributing to MCC pathogenesis. The metabolic signatures of McPyV⁺ and McPyV^- tumours seem to show relevant differences.

Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions

BACKGROUND

Convolutional neural networks (CNNs) have shown a dermatologist-level performance in the classification of skin lesions. We aimed to deliver a head-to-head comparison of a conventional image analyser (CIA), which depends on segmentation and weighting of handcrafted features, to a CNN trained by deep learning.

METHODS

Cross-sectional study using a real-world, prospectively acquired, dermoscopic dataset of 1981 skin lesions to compare the diagnostic performance of a market-approved CNN (Moleanalyzer-Pro™, developed in 2018) to a CIA (Moleanalyzer-3™/Dynamole™; developed in 2004, all FotoFinder Systems Inc, Germany). As a reference standard, we used histopathological diagnoses (n = 785) or, in non-excised benign lesions (n = 1196), expert consensus plus an uneventful follow-up by sequential digital dermoscopy for at least 2 years.

RESULTS

A total of 281 malignant lesions and 1700 benign lesions from 435 patients (62.2% male, mean age: 52 years) were prospectively imaged. The CNN showed a sensitivity of 77.6% (95% confidence interval [CI]: [72.4%-82.1%]), specificity of 95.3% (95% CI: [94.2%-96.2%]), and receiver operating characteristic (ROC)-area under the curve (AUC) of 0.945 (95% CI: [0.930-0.961]). In contrast, the CIA achieved a sensitivity of 53.4% (95% CI: [47.5%-59.1%]), specificity of 86.6% (95% CI: [84.9%-88.1%]) and ROC-AUC of 0.738 (95% CI: [0.701-0.774]). The data set included melanomas originally diagnosed by dynamic changes during sequential digital dermoscopy (52 of 201, 20.6%), which reduced the sensitivities of both classifiers. Pairwise comparisons of sensitivities, specificities, and ROC-AUCs indicated a clear outperformance by the CNN (all p < 0.001).

CONCLUSIONS

The superior diagnostic performance of the CNN argues against a continued application of former CIAs as an aide to physicians' clinical management decisions.