An Updated Algorithm Integrated With Patient Data for the Differentiation of Atypical Nevi From Early Melanomas: the IdScore 2021

Overview

Journal Dermatol Pract Concept

Specialty Dermatology

Date 2022 Sep 26

PMID 36159145

Authors

Linda Tognetti

Alessandra Cartocci

Martina Bertello

Mafalda Giordani

Elisa Cinotti

Gabriele Cevenini

Pietro Rubegni

Affiliations

Soon will be listed here.

Abstract

Introduction: It is well known that multiple patient-related risk factors contribute to the development of cutaneous melanoma, including demographic, 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 sharing equivocal dermoscopic features, excised in the suspicion of malignancy. Dermoscopic standardized images of 258 atypical nevi (aN) and 116 early melanomas (eMM) 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 demographic (2), phenotypic (5) and anamnestic (3) ones.

Results: The proposed iDScore 2021 algorithm is composed by 9 variables (age, skin phototype I/II, personal/familiar history of MM, 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 a score from 0 to 18, reached an area under the ROC curve of 92% and, with a score threshold ≥ 6, a sensitivity (SE) of 98.2% and a specificity (SP) of 50.4%, surpassing the experts in SE (+13%) and SP (+9%).

Conclusions: An integrated checklist combining multiple anamnestic data with selected relevant dermoscopic features can be useful in the differential diagnosis and management of eMM and aN exhibiting with equivocal features.

Citing Articles

Comparative Analysis of AI Models for Atypical Pigmented Facial Lesion Diagnosis.

Cartocci A, Luschi A, Tognetti L, Cinotti E, Farnetani F, Lallas A Bioengineering (Basel). 2024; 11(10).

PMID: 39451411 PMC: 11504969. DOI: 10.3390/bioengineering11101036.

A European Multicentric Investigation of Atypical Melanocytic Skin Lesions of Palms and Soles: The Database.

Tognetti L, Cartocci A, Lallas A, Moscarella E, Stanganelli I, Nazzaro G Diagnostics (Basel). 2024; 14(5).

PMID: 38472933 PMC: 10930449. DOI: 10.3390/diagnostics14050460.

Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation of Irregular Networks.

Nambisan A, Maurya A, Lama N, Phan T, Patel G, Miller K Cancers (Basel). 2023; 15(4).

PMID: 36831599 PMC: 9953766. DOI: 10.3390/cancers15041259.

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