S2k guideline basal cell carcinoma of the skin (update 2023).

Basal cell carcinoma is the most common malignant tumor in the fair-skinned population and its incidence continues to rise. An update of the S2k guideline with the participation of all specialist societies familiar with the clinical picture and previous literature research is of great importance for the quality of care for affected patients. In addition to epidemiology, diagnostics and histology are discussed.

Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions.

Importance: Convolutional neural networks (CNN) have shown performance equal to trained dermatologists in differentiating benign from malignant skin lesions. To improve clinicians' management decisions, additional classifications into diagnostic categories might be helpful.

Methods: A convenience sample of 100 pigmented/non-pigmented skin lesions was used for a cross-sectional two-level reader study including 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information).

Dermatoscopic patterns of cutaneous metastases: A multicentre cross-sectional study of the International Dermoscopy Society.

Background: The detection of cutaneous metastases (CMs) from various primary tumours represents a diagnostic challenge.

Objectives: Our aim was to evaluate the general characteristics and dermatoscopic features of CMs from different primary tumours.

Methods: Retrospective, multicentre, descriptive, cross-sectional study of biopsy-proven CMs.

Position statement of the EADV Artificial Intelligence (AI) Task Force on AI-assisted smartphone apps and web-based services for skin disease.

Background: As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer.

Objective: This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection.

Implementation of a dermatoscopy curriculum during residency at Augsburg University Hospital in Germany.

Background And Objectives: To date, there is no structured program for dermatoscopy training during residency in Germany. Whether and how much dermatoscopy training is acquired is left to the initiative of each resident, although dermatoscopy is one of the core competencies of dermatological training and daily practice. The aim of the study was to establish a structured dermatoscopy curriculum during residency at the University Hospital Augsburg.

Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study: Human With Machine.

Importance: Studies suggest that convolutional neural networks (CNNs) perform equally to trained dermatologists in skin lesion classification tasks. Despite the approval of the first neural networks for clinical use, prospective studies demonstrating benefits of human with machine cooperation are lacking.

Objective: To assess whether dermatologists benefit from cooperation with a market-approved CNN in classifying melanocytic lesions.

Identification of M4205─A Highly Selective Inhibitor of KIT Mutations for Treatment of Unresectable Metastatic or Recurrent Gastrointestinal Stromal Tumors.

The treatment of gastrointestinal stromal tumors (GISTs) driven by activating mutations in the gene is a prime example of targeted therapy for treatment of cancer. The approval of the tyrosine kinase inhibitor imatinib has significantly improved patient survival, but emerging resistance under treatment and relapse is observed. Several additional KIT inhibitors have been approved; still, there is a high unmet need for KIT inhibitors with high selectivity and broad coverage of all clinically relevant KIT mutants.

Vascular Diameter as Clue for the Diagnosis of Clinically and/or Dermoscopically Equivocal Pigmented and Non-Pigmented Basal Cell Carcinomas and Nodular Melanomas.

: Dermoscopy is a useful tool for the early and non-invasive diagnosis of skin malignancies. Besides many progresses, heavily pigmented and amelanotic skin tumors remain still a challenge. We aimed to investigate by dermoscopy if distinctive morphologic characteristics of vessels may help the diagnosis of equivocal nodular lesions.

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.

MSC-1186, a Highly Selective Pan-SRPK Inhibitor Based on an Exceptionally Decorated Benzimidazole-Pyrimidine Core.

The highly conserved catalytic sites in protein kinases make it difficult to identify ATP competitive inhibitors with kinome-wide selectivity. Serendipitously, during a dedicated fragment campaign for the focal adhesion kinase (FAK), a scaffold that had lost its initial FAK affinity showed remarkable potency and selectivity for serine-arginine-protein kinases 1-3 (SRPK1-3). Non-conserved interactions with the uniquely structured hinge region of the SRPK family were the key drivers of the exclusive selectivity of the discovered fragment hit.

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).

Clinical and Dermoscopic Features of Melanocytic Lesions on the Face Versus the External Ear.

Introduction: Melanoma of the external ear is a rare condition accounting for 7-20% of all melanomas of the head and neck region. They present classical features of extra-facial melanomas clinically and dermoscopically. In contrast, facial melanomas show peculiar patterns in dermoscopy.

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.

Background: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice.

Objective: The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians.

Methods: PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma.

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.