The DOI matches its OpenAlex record; 1 of the 1 references it cites were found in a registry or an index.
Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists
Abstract
BACKGROUND AND DESIGN: Epiluminescence microscopy (ELM) is a noninvasive technique that, by employing the optical phenomenon of oil immersion, makes subsurface structures of the skin accessible for in vivo examination and thus provides additional criteria for the clinical diagnosis of pigmented skin lesions. At present, almost all studies about the value and clinical importance of ELM are based on data derived from ELM experts (ie, dermatologists specifically trained in this technique). In the present study, we attempt to determine whether the clinical diagnosis of pigmented skin lesions is significantly improved using ELM and whether ELM-trained individuals and dermatologists not trained in this technique profit equally from this technique. Randomly selected histologically proven pigmented skin lesion specimens, photographed with (ELM) and without oil immersion (surface microscopy) were presented by slide projection to six ELM experts and 13 ELM nonexperts (ie, dermatologists not formally trained in ELM) for diagnosis. To evaluate the diagnostic performance of ELM experts and nonexperts with and without the oil immersion technique (ie, ELM vs surface microscopy), the following parameters were obtained: intraobserver and interobserver agreement by kappa statistics and sensitivity and specificity of diagnostic performance. RESULTS: Our results show that by using the ELM technique the ELM experts reach a substantially better intraobserver agreement than nonexperts (median kappa, 0.56 vs 0.36). The interobserver agreement was markedly increased in the ELM experts group (average gain, 7%) but decreased in the ELM nonexperts group (average loss, 6%). The sensitivity of diagnosis was significantly increased in the ELM experts group (average gain, 10%), but decreased in the nonexperts group (average loss, 10%). Finally, the specificity of diagnosis was excellent in the ELM experts group, both with and without oil immersion (0.91) and was somewhat improved by ELM in the nonexperts group (0.77 vs 0.85). CONCLUSIONS: We conclude that the ELM technique increases sensitivity in formally trained dermatologists, but may decrease the diagnostic ability in dermatologists not formally trained in the ELM technique. Consequently, formal broad-based training in ELM should be offered to the dermatologic community.
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Citations
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
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Semiological value of ABCDE criteria in the diagnosis of cutaneous pigmented tumors
Thomas, L., Tranchand, P., Berard, F., Secchi, T., Colin, C., & Moulin, G. (1998) · Dermatology · doi:10.1159/000017969 · Cited by 195
Piccolo, D., Ferrari, A., Peris, K., Daidone, R., Ruggeri, B., & Chimenti, S. (2002) · British Journal of Dermatology · doi:10.1046/j.1365-2133.2002.04978.x · Cited by 159
Bono, A., Tolomio, E., Trincone, S., Bartoli, C., Tomatis, S., Carbone, A., & Santinami, M. (2006) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2006.07396.x · Cited by 78
Metadata
- Authors
- Binder, M.
- Venue
- Archives of Dermatology
- Publisher
- American Medical Association
- Published
- 1995-03-01
- Volume
- 131
- Issue
- 3
- Pages
- 286-291
- ISSN
- 0003-987X (ISSN-L)
- OpenAlex
- W2077307030
- Cited by
- 428
- References
- 1
- FWCI
- 3.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- Binder, M. (1995). Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Archives of Dermatology. https://doi.org/10.1001/archderm.131.3.286
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.