The coronavirus disease 2019 (COVID-19) pandemic is a scientific, medical, and social challenge. The complexity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is centered on the unpredictable clinical course of the disease that can rapidly develop, causing severe and deadly complications. The identification of effective laboratory biomarkers able to classify patients based on their risk is imperative in being able to guarantee prompt treatment. The analysis of recently published studies highlights the role of systemic vasculitis and cytokine mediated coagulation disorders as the principal actors of multi organ failure in patients with severe COVID-19 complications. The following biomarkers have been identified: hematological (lymphocyte count, neutrophil count, neutrophil-lymphocyte ratio (NLR)), inflammatory (C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT)), immunological (interleukin (IL)-6 and biochemical (D-dimer, troponin, creatine kinase (CK), aspartate aminotransferase (AST)), especially those related to coagulation cascades in disseminated intravascular coagulation (DIC) and acute respiratory distress syndrome (ARDS). New laboratory biomarkers could be identified through the accurate analysis of multicentric case series; in particular, homocysteine and angiotensin II could play a significant role.

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany.

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists.

Background Non‐invasive diagnostic techniques in dermatology gained increasing popularity in the last decade. Reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) are meanwhile established in research and clinical routine. While OCT is mainly indicated for detecting non‐melanoma skin cancer, RCM has proven its usefulness additionally in distinguishing melanocytic lesions. Line‐field confocal optical coherence tomography (LC‐OCT) is an emerging tool combining the principles of both above‐mentioned methods. Methods Healthy skin at different body sites and exemplary skin lesions (basal cell carcinoma, malignant melanoma, actinic keratosis) were examined using dermoscopy, RCM, OCT and LC‐OCT. Standard features for RCM and OCT and comparable features for LC‐OCT were analysed. Results LC‐OCT has a lower penetration depth but superior resolution compared to OCT. In comparison with RCM, which provides only horizontal sections, LC‐OCT creates both vertical and horizontal images in real time and has nearly the same cellular resolution. Discussion Our preliminary experiences suggest that LC‐OCT combines the advantages of RCM and OCT, with optimal resolution and penetration depth to diagnose all types of skin cancer. Larger systematic studies are needed to further characterize the field of use of this device and its sensitivity and specificity compared to histology.

RCM is useful for identifying the histological substrate of dermoscopic features in pigmented lesions of the face. It can provide a better definition of the lesion areas, enabling an improved diagnostic approach.

This study provides a thorough description of 3-D HD-OCT features that can permit discrimination of BCC from clinical BCC imitators and differentiation of BCC subtypes. Based on these features, a diagnostic algorithm is proposed which requires additional validation, but enhances current understanding of the morphological correlates of HD-OCT images in skin.

Ex-vivo confocal laser scanning microscopy (CLSM) offers rapid tissue examination. Current literature shows promising results in the evaluation of non-melanoma skin cancer but little is known about presentation of melanocytic lesions (ML). This study evaluates ML with ex-vivo CLSM in comparison to histology and offers an overview of ex-vivo CLSM characteristics. 31 ML were stained with acridine orange or fluorescein and examined using ex-vivo CLSM (Vivascope2500 ; Lucid Inc; Rochester NY) in reflectance and fluorescence mode. Confocal images were correlated to histopathology. Benign and malignant features of the ML were listed and results were presented. Sensitivity and specificity were calculated using contingency tables. The ML included junctional, compound, dermal, Spitz and dysplastic nevi, as well as various melanoma subtypes. The correlation of the confocal findings with histopathology allowed the identification of different types of ML and differentiation of benign and malignant features. The study offers an overview of confocal characteristics of ML in comparison to histology. Ex-vivo CLSM does not reproduce the typical in-vivo horizontal mosaics but rather reflects the vertical histological presentation. Not all typical in-vivo patterns are detectable here. These findings may help to evaluate the ex-vivo CLSM as an adjunctive tool in the immediate intraoperative diagnosis of ML. Superficial spreading malignant melanoma. Histopathology (H&E stain; 200×) correlated to the reflectance (RM; 830 nm) and fluorescence mode (FM; 488 nm) in the ex-vivo CLSM (Vivablock by VivaScan , acridine orange).

Background. Basal cell carcinoma (BCC) is the most common skin cancer in the general population. Treatments vary from Mohs surgery to topical therapy, depending on the subtype. Dermoscopy, reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) have gained a foothold in daily clinical practice to optimize diagnosis and subtype-oriented treatment. The new technique of line-field confocal OCT (LC-OCT) allows imaging at high resolution and depth, but its use has not yet been investigated in larger studies. Aim. To evaluate the main LC-OCT criteria for the diagnosis and subtyping of BCC compared with histopathology, OCT and RCM. Methods. In total, 52 histopathologically confirmed BCCs were evaluated for imaging criteria. Their frequency, predictive values and ROC curves were calculated. A multinominal regression with stepwise variables selection to distinguish BCC subtypes was performed. Results. Nodular BCCs were mainly characterized by atypical keratinocytes, altered dermoepidermal junction (DEJ), tumour nests in the dermis, dark clefting, prominent vascularization and white hyper-reflective stroma. Superficial BCCs showed a thickening of the epidermis due to a series of tumour lobules with clear connection to the DEJ (string of pearls pattern). Infiltrative BCCs were characterized by elongated hyporeflective tumour strands, surrounded by bright collagen (shoal of fish pattern). The overall BCC subtype agreement between LC-OCT and conventional histology was 90.4% (95% CI 79.0-96.8). Conclusion. LC-OCT allows noninvasive, real-time identification of BCCs and their subtypes in vertical, horizontal and three-dimension mode compared with histology, RCM and OCT. Further larger studies are needed to better explore the clinical applications of this promising device.