L Tognetti1,2, G Cevenini2, E Moscarella3,4, E Cinotti1, F Farnetani5, J Mahlvey6, J L Perrot7, C Longo4,5, G Pellacani5, G Argenziano3, M Fimiani1, P Rubegni1. 1. Dermatology Unit, Department of Medical, Surgical and NeuroSciences, University of Siena, Siena, Italy. 2. Department of Medical Biotechnologies, University of Siena, Siena, Italy. 3. Dermatology Unit, University of Campania, Naples, Italy. 4. Skin Cancer Unit Arcispedale S. Maria Nuova-IRCCS, Reggio Emilia, Italy. 5. Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy. 6. Melanoma Unit, Department of Dermatology, University of Barcelona, Barcelona, Spain. 7. Dermatology Unit, University Hospital of St-Etienne, Saint Etienne, France.
Abstract
BACKGROUND: Dermoscopy revealed to be extremely useful in the diagnosis of early melanoma, the most important limitation being its subjectivity in giving a final diagnosis. To overcome this problem, several algorithms and checklists have been proposed. However, they generally demonstrated modest level of diagnostic accuracy, unsatisfactory concordance between dermoscopists and/or poor specificity. OBJECTIVE: To test a new methodological approach for the differentiation between early melanoma and atypical nevi, based on an integrated clinical-anamnestic dermoscopic risk scoring system (iDScore). METHODS: We selected a total of 435 standardized dermoscopic images of clinically atypical melanocytic skin lesion (MSL) excised in the suspect of malignancy (i.e. 134 early melanomas - MM - and 301 atypical nevi). Data concerning patient age and sex and lesion dimension and site were collected. A scoring classifier was designed based on this data set integrated with the dermoscopic evaluations performed by three experts blinded to histological diagnosis. RESULTS: A total of seven dermoscopic structures, three age groups (30-40 years, 41-60 years and >60 years), two maximum diameter categories (5-10 mm and >10 mm) and three body areas (i.e. frequently, chronically and seldom photoexposed sites) were selected by the scoring classifier as interdependently significant variables. The total risk score (S) of a lesion resulted from the simple sum of partial scores assigned to each selected variable. The iDScore-aided diagnosis showed an high accuracy (receiver operating characteristic-area under the curve = 0.903; IC: 95% = 0.887-0.918). A risk-based criticality scale corresponding to different S ranges was proposed. CONCLUSION: The iDScore checklist is proposed as a feasible and efficient tool to support dermatologists in non-invasive differentiation between atypical nevi and early MM on the basis of few selected clinical-anamnestic data and standardized dermoscopic features.
BACKGROUND: Dermoscopy revealed to be extremely useful in the diagnosis of early melanoma, the most important limitation being its subjectivity in giving a final diagnosis. To overcome this problem, several algorithms and checklists have been proposed. However, they generally demonstrated modest level of diagnostic accuracy, unsatisfactory concordance between dermoscopists and/or poor specificity. OBJECTIVE: To test a new methodological approach for the differentiation between early melanoma and atypical nevi, based on an integrated clinical-anamnestic dermoscopic risk scoring system (iDScore). METHODS: We selected a total of 435 standardized dermoscopic images of clinically atypical melanocytic skin lesion (MSL) excised in the suspect of malignancy (i.e. 134 early melanomas - MM - and 301 atypical nevi). Data concerning patient age and sex and lesion dimension and site were collected. A scoring classifier was designed based on this data set integrated with the dermoscopic evaluations performed by three experts blinded to histological diagnosis. RESULTS: A total of seven dermoscopic structures, three age groups (30-40 years, 41-60 years and >60 years), two maximum diameter categories (5-10 mm and >10 mm) and three body areas (i.e. frequently, chronically and seldom photoexposed sites) were selected by the scoring classifier as interdependently significant variables. The total risk score (S) of a lesion resulted from the simple sum of partial scores assigned to each selected variable. The iDScore-aided diagnosis showed an high accuracy (receiver operating characteristic-area under the curve = 0.903; IC: 95% = 0.887-0.918). A risk-based criticality scale corresponding to different S ranges was proposed. CONCLUSION: The iDScore checklist is proposed as a feasible and efficient tool to support dermatologists in non-invasive differentiation between atypical nevi and early MM on the basis of few selected clinical-anamnestic data and standardized dermoscopic features.