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Abstract: Pityriasis rubra pilaris (PRP) is a rare inflammatory skin disease that affects men and women of all ages and also children. The clinical appearance of PRP is highly variable, as is the individual prognosis. Therefore, stratifying PRP into six disease subtypes represents a first step to personalized medicine for this rare inflammatory skin disease. The next step should be to associate specific therapeutic strategies with these subtypes of PRP. However, no randomized, controlled trials on the treatment of PRP have been performed. Consequently, the actual treatment algorithm for PRP will be based on clinical experience, small case series and case reports. The majority of published evidence is on type I PRP, whereas the treatment experience for other clinical types of PRP is still sparse and has to be gained. Nevertheless, it is now time to start developing valid algorithms as a basis for the diagnosis and treatment of PRP based on the data available. This review makes use of algorithms developed in psoriasis and atopic eczema and puts together recent insights into the pathogenesis, diagnosis and treatment experience of PRP. The innovative intention of this appraisal is to develop a structured algorithm for PRP treatment that should be further developed going forward. [ABSTRACT FROM AUTHOR]

Background: Histopathological differentiation of early mycosis fungoides (MF) from benign chronic inflammatory dermatoses remains difficult and often impossible, despite the inclusion of all available diagnostic parameters., Objective: To identify the most impactful histological criteria for a predictive diagnostic model to discriminate MF from atopic dermatitis (AD)., Methods: In this multicentre study, two cohorts of patients with either unequivocal AD or MF were evaluated by two independent dermatopathologists. Based on 32 histological attributes, a hypothesis-free prediction model was developed and validated on an independent patient's cohort., Results: A reduced set of two histological features (presence of atypical lymphocytes in either epidermis or dermis) was trained. In an independent validation cohort, this model showed high predictive power (95% sensitivity and 100% specificity) to differentiate MF from AD and robustness against inter-individual investigator differences., Limitations: The study investigated a limited number of cases and the classifier is based on subjectively evaluated histological criteria., Conclusion: Aiming at distinguishing early MF from AD, the proposed binary classifier performed well in an independent cohort and across observers. Combining this histological classifier with immunohistochemical and/or molecular techniques (such as clonality analysis or molecular classifiers) could further promote differentiation of early MF and AD., (© 2023 The Authors. Journal of the European Academy of Dermatology and Venereology published by John Wiley & Sons Ltd on behalf of European Academy of Dermatology and Venereology.)