Predictive Model for Differential Diagnosis of Inflammatory Papular Dermatoses of the Face

Abstract

Background: The clinical features of inflammatory papular dermatoses of the face are very similar. Their clinical manifestations have been described on the basis of a small number of case reports and are not specific. Objective: This study aimed to use computer-aided image analysis (CAIA) to compare the clinical features and parameters of inflammatory papular dermatoses of the face and to develop a formalized diagnostic algorithm based on the significant findings. Methods: The study included clinicopathologically confirmed inflammatory papular dermatoses of the face: 8 cases of eosinophilic pustular folliculitis (EPF), 13 of granulomatous periorificial dermatitis-lupus miliaris disseminatus faciei (GPD-LMDF) complex, 41 of granulomatous rosacea-papulopustular rosacea complex (GR-PPR) complex, and 4 of folliculitis. Clinical features were evaluated, and area density of papular lesions was quantitatively measured with CAIA. Based on these variables, we developed a predictive model for differential diagnosis using classification and regression tree analysis. Results: The EPF group showed lesion asymmetry and annular clusters of papules in all cases. The GPD-LMDF complex group had significantly higher periocular density. The GR-PPR complex group showed a higher area density of unilateral cheek papules and the highest to-tal area density. According to the predictive model, 3 variables were used for differential diagnosis of the 4 disease groups, and each group was diagnosed with a predicted probability of 67%similar to 100%. Conclusion: We statistically confirmed the distinct clinical features of inflammatory papular dermatoses of the face and proposed a diagnostic algorithm for clinical diagnosis.