Comparative Performance of 4 Dermoscopic Algorithms by Nonexperts for the Diagnosis of Melanocytic Lesions

Abstract

To assess 4 dermoscopy methods in a nonexpert setting. Sixty-one medical practitioners, mainly primary care physicians in Australia, were trained in 4 dermoscopy algorithms. Participants then assessed macroscopic and dermoscopic images of 40 melanocytic skin lesions. Each of the dermoscopic images was assessed with pattern analysis, the 7-point checklist, the ABCD rule, and the Menzies method. The Menzies method showed the highest sensitivity, 84.6%, for the diagnosis of melanoma, followed by the 7-point checklist (81.4%), the ABCD rule (77.5%), pattern analysis (68.4%), and assessment of a macroscopic image (60.9%). Pattern analysis and assessment of the macroscopic image showed the highest specificity, 85.3% and 85.4%, respectively. The ABCD rule showed a specificity of 80.4%; the Menzies method, 77.7%; and the 7-point checklist, 73%. The Menzies method had a diagnostic accuracy of 81.1%; the ABCD rule, 79.0%; the 7-point checklist, 77.2%; pattern analysis, 76.8%; and clinical assessment, 73.2%. All algorithms performed well in the hands of relatively inexpert practitioners who had undertaken self-guided training provided on compact disc. The Menzies method showed the highest diagnostic accuracy and sensitivity for melanoma diagnosis and was preferred by study participants.