The dermoscopic inverse approach significantly improves the accuracy of human readers for lentigo maligna diagnosis - 11/01/21
, Konstantinos Lallas, MD a, Philipp Tschandl, MD, PhD b, Harald Kittler, MD, PhD b, Zoe Apalla, MD, PhD c, Caterina Longo, MD, PhD d, e, Giuseppe Argenziano, MD, PhD fAbstract |
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.
Le texte complet de cet article est disponible en PDF.Key words : artificial intelligence, dermatoscopy, dermoscopy, diagnosis, inverse approach, maligna, melanoma, pigmented actinic keratosis, solar lentigo
Abbreviations used : AI, CNN, LM, PAK, SL/SK
Plan
| Funding sources: None. |
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| Conflicts of interest: None disclosed. |
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| IRB approval status: Not applicable. |
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| Reprints not available from the authors. |
Vol 84 - N° 2
P. 381-389 - février 2021 Retour au numéro
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