The dermoscopic inverse approach significantly improves the accuracy of human readers for lentigo maligna diagnosis

Abstract

Background: A recently introduced dermoscopic method for the diagnosis of early lentigo maligna (LM) is based on the absence of prevalent patterns of pigmented actinic keratosis and solar lentigo/flat seborrheic keratosis. We term this the inverse approach.

Objective: To determine whether training on the inverse approach increases the diagnostic accuracy of readers compared to classic pattern analysis.

Methods: We used clinical and dermoscopic images of histopathologically diagnosed LMs, pigmented actinic keratoses, and solar lentigo/flat seborrheic keratoses. Participants in a dermoscopy masterclass classified the lesions at baseline and after training on pattern analysis and the inverse approach. We compared their diagnostic performance among the 3 timepoints and to that of a trained convolutional neural network.

Results: The mean sensitivity for LM without training was 51.5%; after training on pattern analysis, it increased to 56.7%; and after learning the inverse approach, it increased to 83.6%. The mean proportions of correct answers at the 3 timepoints were 62.1%, 65.5, and 78.5%. The percentages of readers outperforming the convolutional neural network were 6.4%, 15.4%, and 53.9%, respectively.

Limitations: The experimental setting and the inclusion of histopathologically diagnosed lesions only.

Conclusions: The inverse approach, added to the classic pattern analysis, significantly improves the sensitivity of human readers for early LM diagnosis.

Keywords: artificial intelligence; dermatoscopy; dermoscopy; diagnosis; inverse approach; maligna; melanoma; pigmented actinic keratosis; solar lentigo.