An updated algorithm integrated with patient data for the differentiation of atypical nevi from early melanomas: the idScore 2021 - Tognetti, Linda; Cartocci, Alessandra; Bertello, Martina; Giordani, Mafalda; Cinotti, Elisa; Cevenini, Gabriele; Rubegni, Pietro (Dermatology practical & conceptual, 2022)
Background: It is well known that multiple patient-related risk factors contribute to the development of cutaneous melanoma, including anagraphic, phenotypic and anamnestic factors. Objectives: We aimed to investigate which MM risk factors were relevant to be incorporated in a risk scoring-classifier based clinico-dermoscopic algorithm. Methods: This retrospective study was performed on a monocentric dataset of 374 atypical melanocytic skin lesions (aMSLs) sharing equivocal dermoscopic features, excised in the suspicion of malignancy. Dermoscopic standardized images of 258 atypical nevi and 116 early melanomas were collected along with objective lesional data (i.e., maximum diameter, specific body site and body area) and 7 dermoscopic data. All cases were combined with a series of 10 MM risk factors, including anagraphic (2), phenotypic (5) and anamnestic (3) ones. Results: According to the stepwise analysis logistic regression, the proposed iDScore 2021 algorithm is composed by 9 variables, including 3 patient data (age, skin phototype I/II, personal/familiar history of MM), 2 lesion characteristics (maximum diameter, location on the lower extremities (thighs/legs/ankles/back of the feet) and 4 dermoscopic features (irregular dots and globules, irregular streaks, blue gray peppering, blue white veil). The algorithm assigned to each lesion/case a progressive score from 0 to 18. The performance obtained in the ROC curve analysis reached 92% AUC in differentiating early melanomas from atypical nevi. Conclusions: An integrated checklist combining multiple anamnestic data with selected relevant dermoscopic features can be useful in the differential diagnosis and management of early melanomas and atypical nevi exhibiting equivocal features.