The dermoscopic inverse approach significantly improves the accuracy of human readers for lentigo maligna diagnosis.
J Am Acad Dermatol
; 84(2): 381-389, 2021 Feb.
Article
em En
| MEDLINE
| ID: mdl-32592885
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Pele
/
Neoplasias Cutâneas
/
Sarda Melanótica de Hutchinson
/
Dermoscopia
/
Detecção Precoce de Câncer
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limite:
Adult
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Aged
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Female
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Humans
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Male
/
Middle aged
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article