Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic.

Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making by such algorithms is essentially a black-box process that renders it difficult for physicians to judge whether the decisions are reliable. The use of explainable artificial intelligence (XAI) is often suggested as a solution to this problem. We investigate how XAI is used for skin cancer detection: how is it used during the development of new DNNs? What kinds of visualisations are commonly used? Are there systematic evaluations of XAI with dermatologists or dermatopathologists? Google Scholar, PubMed, IEEE Explore, Science Direct and Scopus were searched for peer-reviewed studies published between January 2017 and October 2021 applying XAI to dermatological images: the search terms histopathological image, whole-slide image, clinical image, dermoscopic image, skin, dermatology, explainable, interpretable and XAI were used in various combinations. Only studies concerned with skin cancer were included. 37 publications fulfilled our inclusion criteria. Most studies (19/37) simply applied existing XAI methods to their classifier to interpret its decision-making. Some studies (4/37) proposed new XAI methods or improved upon existing techniques. 14/37 studies addressed specific questions such as bias detection and impact of XAI on man-machine-interactions. However, only three of them evaluated the performance and confidence of humans using CAD systems with XAI. XAI is commonly applied during the development of DNNs for skin cancer detection. However, a systematic and rigorous evaluation of its usefulness in this scenario is lacking.

Background: Immune checkpoint inhibition and in particular anti-PD-1 immunotherapy have revolutionized the treatment of advanced melanoma. In this regard, higher tumoral PD-L1 protein (gene name: CD274) expression is associated with better clinical response and increased survival to anti-PD-1 therapy. Moreover, there is increasing evidence that tumor suppressor proteins are involved in immune regulation and are capable of modulating the expression of immune checkpoint proteins. Here, we determined the role of p53 protein (gene name: TP53) in the regulation of PD-L1 expression in melanoma. Methods: We analyzed publicly available mRNA and protein expression data from the cancer genome/proteome atlas and performed immunohistochemistry on tumors with known TP53 status. Constitutive and IFN-ɣ-induced PD-L1 expression upon p53 knockdown in wildtype, TP53-mutated or JAK2-overexpressing melanoma cells or in cells, in which p53 was rendered transcriptionally inactive by CRISPR/Cas9, was determined by immunoblot or flow cytometry. Similarly, PD-L1 expression was investigated after overexpression of a transcriptionally-impaired p53 (L22Q, W23S) in TP53-wt or a TP53-knockout melanoma cell line. Immunoblot was applied to analyze the IFN-ɣ signaling pathway. Results: For TP53-mutated tumors, an increased CD274 mRNA expression and a higher frequency of PD-L1 positivity was observed. Interestingly, positive correlations of IFNG mRNA and PD-L1 protein in both TP53-wt and -mutated samples and of p53 and PD-L1 protein suggest a non-transcriptional mode of action of p53. Indeed, cell line experiments revealed a diminished IFN-ɣ-induced PD-L1 expression upon p53 knockdown in both wildtype and TP53-mutated melanoma cells, which was not the case when p53 wildtype protein was rendered transcriptionally inactive or by ectopic expression of p53^L22Q,W23S, a transcriptionally-impaired variant, in TP53-wt cells. Accordingly, expression of p53^L22Q,W23S in a TP53-knockout melanoma cell line boosted IFN-ɣ-induced PD-L1 expression. The impaired PD-L1-inducibility after p53 knockdown was associated with a reduced JAK2 expression in the cells and was almost abrogated by JAK2 overexpression. Conclusions: While having only a small impact on basal PD-L1 expression, both wildtype and mutated p53 play an important positive role for IFN-ɣ-induced PD-L1 expression in melanoma cells by supporting JAK2 expression. Future studies should address, whether p53 expression levels might influence response to anti-PD-1 immunotherapy.

Immune checkpoint blockade therapy is beneficial and even curative for some cancer patients. However, the majority don’t respond to immune therapy. Across different tumor types, pre-existing T cell infiltrates predict response to checkpoint-based immunotherapy. Based on in vitro pharmacological studies, mouse models and analyses of human melanoma patients, we show that the cytokine GDF-15 impairs LFA-1/β2-integrin-mediated adhesion of T cells to activated endothelial cells, which is a pre-requisite of T cell extravasation. In melanoma patients, GDF-15 serum levels strongly correlate with failure of PD-1-based immune checkpoint blockade therapy. Neutralization of GDF-15 improves both T cell trafficking and therapy efficiency in murine tumor models. Thus GDF-15, beside its known role in cancer-related anorexia and cachexia, emerges as a regulator of T cell extravasation into the tumor microenvironment, which provides an even stronger rationale for therapeutic anti-GDF-15 antibody development.

Background Pemphigus vulgaris and pemphigus foliaceus are potentially life-threatening autoimmune disorders triggered by immunoglobulin G (IgG) autoantibodies against mucosal and epidermal desmogleins. There is an unmet need for fast-acting drugs that enable patients to achieve early sustained remission with reduced corticosteroid reliance. Objective To investigate efgartigimod, an engineered Fc fragment that inhibits the activity of the neonatal Fc receptor, thereby reducing serum IgG levels, for treating pemphigus. Methods Thirty-four patients with mild to moderate pemphigus vulgaris or foliaceus were enrolled in an open-label phase 2 adaptive trial. In sequential cohorts, efgartigimod was dosed at 10 or 25 mg/kg intravenously with various dosing frequencies, as monotherapy or as add-on therapy to low-dose oral prednisone. Safety endpoints comprised the primary outcome. Results Adverse events were mostly mild and reported by 16/19 (84%) patients receiving efgartigimod 10 mg/kg and 13/15 (87%) patients receiving the 25 mg/kg dose, with similar adverse event profiles between dose groups. A major decrease in serum total IgG and anti-desmoglein (Dsg) autoantibodies was observed and correlated with improved pemphigus disease area index (PDAI) scores. Efgartigimod, as monotherapy or combined with prednisone, demonstrated early disease control in 28/31 (90%) patients after a median of 17 days. Optimized, prolonged treatment with efgartigimod in combination with a median dose of 0.26 (range 0.06-0.48) mg/kg/day prednisone led to complete clinical remission in 14/22 (64%) patients within 2-41 weeks. Conclusion Efgartigimod was well-tolerated and exhibited an early effect on disease activity and outcome parameters, providing support for further evaluation as a therapy for pemphigus. The study is registered at ClinicalTrials.gov (identifier: NCT03334058).