Experimentelle Studie zur Wirksamkeit der niedermolekularen Tyrosinkinase-Inhibitoren BMS-777607, LDC-41267 und MPCD-84111 in Modellen des Glioblastoms

Purpose: Multiple studies highlight the role of the receptor tyrosine kinase Axl as a relevant mediator of proliferation, migration and invasion of glioma cells. Overexpression of Axl was found in the majority of human glioblastomas. In conclusion, Axl is considered as a potential target in glioblastoma therapy. The aim of this study was to evaluate the effects of small molecule tyrosine kinase inhibitors BMS-777607, LDC-41267 and MPCD-84111 targeting Axl in glioblastoma models in vitro, ex vivo and in vivo. Experimental Design: Axl inhibitors were tested using glioma cells SF126 and U118MG. The impact on cell viability, apoptosis, migration and invasion was analyzed by functional assays (MTT assay, CPP32 assay, boyden chamber migration assay, orthotopic brain slice invasion assay). For in vivo experiments, glioma cells were implanted stereotactically into the brains of CD1 Nu/Nu mice. Tumor growth was measured using MRI. Proliferation, apoptosis and vascularization in the tumor tissue were investigated by immunohistochemistry (Ki-67, ApopTag®, CD31). Results: Treatment with BMS-777607, LDC-41267 and MPCD-84111 resulted in a decreased cell viability, migration and invasion of SF126 and U118MG glioma cells in vitro and ex vivo. BMS-777607 induced a 56 % reduction of tumor growth in SF126 xenografts and a reduction of more than 90 % in U118MG xenografts in vivo. Furthermore, BMS-777607 caused a decrease of Ki-67-positive cells and an increase of ApopTag®-positive cells in the tumor tissue. Treatment with LDC-41267 showed no impact on tumor growth in vivo. Animal experiments with MPCD-84111 were discontinued because of observed toxicity. Conclusion: BMS-777607, LDC-41267 und MPCD-84111 displayed multiple anti-tumor effects in glioblastoma models. Here we demonstrate that small molecule tyrosine kinase inhibitors targeting Axl could provide a promising treatment approach for patients with glioblastoma.Zielsetzung: Die Rezeptortyrosinkinase Axl stellt einen bedeutenden Vermittler der Proliferation, Migration und Invasion der Gliomzellen dar. In der Mehrzahl der humanen Glioblastome findet sich eine Überexpression von Axl. Daher gilt Axl als potenzielles Angriffsziel in der Behandlung des Glioblastoms. In dieser Studie wurden die Effekte einer zielgerichteten Inhibierung von Axl durch die selektiven, niedermolekularen Tyrosinkinase-Inhibitoren BMS-777607, LDC-41267 und MPCD-84111 in Modellen des Glioblastoms in vitro, ex vivo und in vivo evaluiert. Methodik: Die Axl-Inhibitoren wurden an den Gliom-Zelllinien SF126 und U118MG getestet. Die Effekte auf die Zellviabilität, Apoptose, Zellmigration und Invasion wurden mittels funktioneller Assays (MTT-Assay, CPP32-Assay, Boyden-Kammer-Assay, Sphäroid-Invasions- Assay) analysiert. Zur Testung der Axl-Inhibitoren unter in vivo-Bedingungen erfolgte die stereotaktische, intrazerebrale Implantation der Gliomzellen in CD1 Nu/Nu-Mäusen. Das intrakranielle Tumorwachstum wurde mittels MRT-Kontrollen beurteilt. Das Tumorgewebe wurde hinsichtlich der intratumoralen Proliferation, Apoptose und Vaskularisation durch Immunfluoreszenzfärbungen (Ki-67, ApopTag®, CD31) untersucht. Ergebnisse: Die Behandlung mit BMS-777607, LDC-41267 und MPCD-84111 resultierte in einer signifikanten Reduktion der Zellviabilität, der Zellmigration sowie der Invasion der Gliomzellen in vitro und ex vivo. BMS-777607 induzierte eine Reduktion des intrakraniellen Tumorwachstums von 56 % im Tumormodell SF126 sowie von über 90 % im Tumormodell U118MG in vivo. Weiterhin erzielte BMS-777607 eine Reduktion Ki-67-positiver Gliomzellen sowie einen signifikanten Anstieg ApopTag®-positiver Gliomzellen im Tumorgewebe. Unter LDC-41267 zeigte sich kein Effekt auf das Tumorwachstum in vivo. Tierversuche mit MPCD-84111 wurden aufgrund toxischer Effekte abgebrochen. Fazit: BMS-777607, LDC-41267 und MPCD-84111 erzielten multiple Anti-Tumor-Effekte in Modellen des Glioblastoms. Unsere Studie zeigt, dass die zielgerichtete Inhibierung von Axl durch selektive, niedermolekulare Tyrosinkinase-Inhibitoren einen vielversprechenden Therapieansatz für Patienten mit Glioblastom darstellen könnte

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma poses severe obstacles to user acceptance. Explainable artificial intelligence (XAI) methods can help to increase transparency, but most XAI methods are unable to produce precisely located domain-specific explanations, making the explanations difficult to interpret. Moreover, the impact of XAI methods on dermatologists has not yet been evaluated. Extending on two existing classifiers, we developed an XAI system that produces text and region based explanations that are easily interpretable by dermatologists alongside its differential diagnoses of melanomas and nevi. To evaluate this system, we conducted a three-part reader study to assess its impact on clinicians' diagnostic accuracy, confidence, and trust in the XAI-support. We showed that our XAI's explanations were highly aligned with clinicians' explanations and that both the clinicians' trust in the support system and their confidence in their diagnoses were significantly increased when using our XAI compared to using a conventional AI system. The clinicians' diagnostic accuracy was numerically, albeit not significantly, increased. This work demonstrates that clinicians are willing to adopt such an XAI system, motivating their future use in the clinic

Laugier–Hunziker syndrome. A rare differential diagnosis of mucocutaneous hyperpigmentation

Laugier-Hunziker syndrome (LHS) is characterized by lentiginous hyperpigmentation of the oral mucosa and lips. In addition, longitudinal melanonychia and palmoplantar hyperpigmented lesions may occur. LHS is a clinical diagnosis of exclusion. Herein, we report the case of a 66-year-old woman with LHS. The clinical and histopathologic features of LHS are presented and important differential diagnoses are discussed.Das Laugier-Hunziker-Syndrom (LHS) ist durch lentiginöse Hyperpigmentierungen der Mundschleimhaut und Lippen gekennzeichnet. Zusätzlich können longitudinale bzw. striäre Melanonychien und palmoplantare Pigmentläsionen auftreten. Es handelt sich um eine klinische Ausschlussdiagnose. Wir berichten hier über eine 66-jährige Patientin mit LHS. Die klinischen und histopathologischen Merkmale des LHS werden vorgestellt und wichtige Differenzialdiagnosen diskutiert

Model soups improve performance of dermoscopic skin cancer classifiers

Background: Image-based cancer classifiers suffer from a variety of problems which negatively affect their performance. For example, variation in image brightness or different cameras can already suffice to diminish performance. Ensemble solutions, where multiple model predictions are combined into one, can improve these problems. However, ensembles are computationally intensive and less transparent to practitioners than single model solutions. Constructing model soups, by averaging the weights of multiple models into a single model, could circumvent these limitations while still improving performance. Objective: To investigate the performance of model soups for a dermoscopic melanoma-nevus skin cancer classification task with respect to (1) generalisation to images from other clinics, (2) robustness against small image changes and (3) calibration such that the confidences correspond closely to the actual predictive uncertainties. Methods: We construct model soups by fine-tuning pre-trained models on seven different image resolutions and subsequently averaging their weights. Performance is evaluated on a multi-source dataset including holdout and external components. Results: We find that model soups improve generalisation and calibration on the external component while maintaining performance on the holdout component. For robustness, we observe performance improvements for pertubated test images, while the performance on corrupted test images remains on par. Conclusions: Overall, souping for skin cancer classifiers has a positive effect on generalisation, robustness and calibration. It is easy for practitioners to implement and by combining multiple models into a single model, complexity is reduced. This could be an important factor in achieving clinical applicability, as less complexity generally means more transparency. (c) 2022 The Authors. Published by Elsevier Ltd

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Abstract 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