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

Aimilios Lallas, Konstantinos Lallas, Philipp Tschandl, Harald Kittler, Zoe Apalla, Caterina Longo, Giuseppe Argenziano

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalJournal of the American Academy of Dermatology
Publication statusE-pub ahead of print - Jun 24 2020


Dive into the research topics of 'The dermoscopic inverse approach significantly improves the accuracy of human readers for lentigo maligna diagnosis'. Together they form a unique fingerprint.

Cite this