Lichen planus – a clinical guide

Lichen planus (LP) is a chronic lichenoid inflammatory disorder of the skin, mucosa and of the appendages. LP is classically characterized by the presence of a rich infiltration of inflammatory T cells, which migrate in the upper part of the dermis, arranged in a band-like pattern. Different sub types of the disease have been so far described. Albeit LP is clinically well defined, the disease still represents a therapeutic enigma. Especially with regard to mucosal or scalp affecting LP types, which often present a recalcitrant and treatment unresponsive course, efficacious therapeutic options are still lacking. Thus, LP represents a disease with a high psychosocial burden. Yet, development in the deciphering of LP pathogenesis reveals possible new druggable targets, thus paving the way for future therapeutic options. In this clinical guide, we summarize the current clinical knowledge and therapeutic standards and discuss the future perspective for the management of LP

Development of an Image Analysis-Based Prognosis Score Using Google’s Teachable Machine in Melanoma

Background: The increasing number of melanoma patients makes it necessary to establish new strategies for prognosis assessment to ensure follow-up care. Deep-learning-based image analysis of primary melanoma could be a future component of risk stratification. Objectives: To develop a risk score for overall survival based on image analysis through artificial intelligence (AI) and validate it in a test cohort. Methods: Hematoxylin and eosin (H&E) stained sections of 831 melanomas, diagnosed from 2012–2015 were photographed and used to perform deep-learning-based group classification. For this purpose, the freely available software of Google’s teachable machine was used. Five hundred patient sections were used as the training cohort, and 331 sections served as the test cohort. Results: Using Google’s Teachable Machine, a prognosis score for overall survival could be developed that achieved a statistically significant prognosis estimate with an AUC of 0.694 in a ROC analysis based solely on image sections of approximately 250 × 250 µm. The prognosis group “low-risk” (n = 230) showed an overall survival rate of 93%, whereas the prognosis group “high-risk” (n = 101) showed an overall survival rate of 77.2%. Conclusions: The study supports the possibility of using deep learning-based classification systems for risk stratification in melanoma. The AI assessment used in this study provides a significant risk estimate in melanoma, but it does not considerably improve the existing risk classification based on the TNM classification