Linear patterns of the skin and their dermatoses

Knowledge about the linear patterns of the skin is a key competence of dermatologists. Four major groups of linear patterns can be distinguished: Langer lines, dermatomes, Blaschko lines and exogenous patterns. Langer lines run in the direction of the underlying collagen fibers (least skin tension) and play an important diagnostic role for some exanthematous skin diseases. In the thoracodorsal region, the distribution of the Langer lines gives rise to what is referred to as a ’Christmas tree pattern’. A dermatome is an area of skin that is supplied by a single spinal nerve. Disorders with a neuronal origin follow this pattern of distribution. The lines of Blaschko delineate the lines of migration of epidermal cells during embryogenesis. Exogenous linear patterns are caused by external factors. The present CME article will highlight important skin disorders that primarily present in the form of one of the aforementioned patterns. In addition, we will also address skin conditions that may secondarily follow with these patterns (or distinctly not do so) as the result of various mechanisms such as the Koebner phenomenon, reverse Koebner phenomenon, and Wolf’s isotopic response

Matrix gla protein (MGP): an overexpressed and migration-promoting mesenchymal component in glioblastoma

<p>Abstract</p> <p>Background</p> <p>Recent studies have demonstrated that a molecular subtype of glioblastoma is characterized by overexpression of extracellular matrix (ECM)/mesenchymal components and shorter survival. Specifically, gene expression profiling studies revealed that matrix gla protein (MGP), whose function has traditionally been linked to inhibition of calcification of arteries and cartilage, is overexpressed in glioblastomas and associated with worse outcome.</p> <p>Methods</p> <p>In order to analyze the role of MGP in glioblastomas, we performed expression, migration and proliferation studies.</p> <p>Results</p> <p>Real-time PCR and ELISA assays confirmed overexpression of MGP in glioblastoma biopsy specimens and cell lines at mRNA and protein levels as compared to normal brain tissue. Immunohistochemistry verified positivity of glial tumor cells for MGP. RNAi-mediated knockdown of MGP in three glioma cell lines (U343MG, U373MG, H4) led to marked reduction of migration, as demonstrated by wound healing and transwell assays, while no effect on proliferation was seen.</p> <p>Conclusion</p> <p>Our data suggest that upregulation of MGP (and possibly other ECM-related components as well) results in unfavorable prognosis via increased migration.</p

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd