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.