ci-959 and Melanoma

Dermoscopic algorithms for melanoma diagnosis could be time-expending, and their reliability in daily practice lower than expected.. To propose a simplified dermoscopic algorithm for melanoma diagnosis.. A multicenter retrospective analysis of 1,120 dermoscopic images of atypical melanocytic tumors (320 melanomas and 800 non-melanomas) was performed. An algorithm based on polychromia, asymmetry in colors or structures, and some melanoma-specific structures was designed. Univariate and multivariate logistic regression analysis was calculated to estimate the coefficients of each potential predictor for melanoma diagnosis. A score was developed based on the dermoscopic evaluations performed by four experts blinded to histological diagnosis.. Most melanomas had ≥3 colors (280; 84.5%), asymmetry in colors or structures (289; 90.3%), and at least one melanoma-specific structure (316; 98.7%). PASS score ≥3 had a 91.9% sensibility, 87% specificity, and 88.4% diagnostic accuracy for melanoma. PASS algorithm showed an area under the curve (AUC) of 0.947 (95% CI 0.935-0.959).. This study was retrospective. A comparison between the performances of different dermoscopic algorithms is difficult because of their designs.. PASS algorithm showed a very good diagnostic accuracy, independently of the observers' experience, and it seems easier to perform than previous dermoscopic algorithms.

Topics: Algorithms; Dermoscopy; Humans; Melanoma; Reproducibility of Results; Retrospective Studies; Skin Neoplasms; Syndrome