Performance of the First Step of the 2-Step Dermoscopy Algorithm

The 2-step dermoscopy algorithm aims to guide the decision-making process to biopsy or not biopsy a skin lesion by providing the most probable diagnosis via a systematic approach.

To evaluate the diagnostic accuracy and potential limitations of the first step (to differentiate melanocytic from nonmelanocytic lesions) of the 2-step dermoscopy algorithm.

Retrospective study in a clinical practice of one dermatologist of biopsy data of all skin lesions from one clinic during a 10-year period. The prebiopsy and histopathology diagnoses were classified as melanocytic or nonmelanocytic.

The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for the first step were estimated using the histopathological lesion classifications as the standard.

The sensitivity of the first step for correctly identifying melanocytic lesions was 85%, and the specificity was 94%. Approximately 7% of all lesions (667 of 9168) had discordant classifications, with 415 (4.5%) being false-positive lesions (clinically classified as melanocytic and histopathologically classified as nonmelanocytic) and 252 (2.7%) being false negatives (clinically classified as nonmelanocytic and histopathologically classified as melanocytic). Common classification errors included intradermal nevus misclassified as basal cell carcinoma and nonmelanocytic lesions (eg, seborrheic keratosis, lichen planus-like keratosis, basal cell carcinomas) misclassified as melanocytic because they mimic melanoma. Clinically, 8 of 381 melanomas were misclassified as nonmelanocytic (primarily as pigmented basal cell carcinomas and squamous cell carcinomas).

The 2-step dermoscopy algorithm, including its first step, has high sensitivity, specificity, and accuracy and can be relied on to provide an accurate and specific prebiopsy diagnosis and to help guide management decisions. Some lesions had a higher chance of being misclassified, with the most common being intradermal nevi. This algorithm helps toward maximizing the detection of skin cancer to ensure that malignant lesions are not missed and aims at making more precise clinical diagnoses.