Histology-based classifier to distinguish early mycosis fungoides from atopic dermatitis.

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.