The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Dermoscopy of pigmented skin lesions: Results of a consensus meeting via the Internet
Open accessAbstract
BACKGROUND There is a need for better standardization of the dermoscopic terminology in assessing pigmented skin lesions. OBJECTIVE The virtual Consensus Net Meeting on Dermoscopy was organized to investigate reproducibility and validity of the various features and diagnostic algorithms. METHODS Dermoscopic images of 108 lesions were evaluated via the Internet by 40 experienced dermoscopists using a 2-step diagnostic procedure. The first-step algorithm distinguished melanocytic versus nonmelanocytic lesions. The second step in the diagnostic procedure used 4 algorithms (pattern analysis, ABCD rule, Menzies method, and 7-point checklist) to distinguish melanoma versus benign melanocytic lesions. kappa Values, log odds ratios, sensitivity, specificity, and positive likelihood ratios were estimated for all diagnostic algorithms and dermoscopic features. RESULTS Interobserver agreement was fair to good for all diagnostic methods, but it was poor for the majority of dermoscopic criteria. Intraobserver agreement was good to excellent for all algorithms and features considered. Pattern analysis allowed the best diagnostic performance (positive likelihood ratio: 5.1), whereas alternative algorithms revealed comparable sensitivity but less specificity. Interobserver agreement on management decisions made by dermoscopy was fairly good (mean kappa value: 0.53). CONCLUSION The virtual Consensus Net Meeting on Dermoscopy represents a valid tool for better standardization of the dermoscopic terminology and, moreover, opens up a new territory for diagnosing and managing pigmented skin lesions.
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Citations
Cites
Measuring nominal scale agreement among many raters.
Fleiss, J. L. (1971) · Psychological Bulletin · doi:10.1037/h0031619 · Cited by 8,733
Diagnostic accuracy of dermoscopy
Kittler, H., Pehamberger, H., Wolff, K., & Binder, M. (2002) · The Lancet Oncology · doi:10.1016/s1470-2045(02)00679-4 · Cited by 1,349
Self-citationDermoscopy of pigmented skin lesions – a valuable tool for early
Argenziano, G., & Soyer, H. P. (2001) · The Lancet Oncology · doi:10.1016/s1470-2045(00)00422-8 · Cited by 391
Self-citationTeledermoscopy - results of a multicentre study on 43 pigmented skin lesions
Piccolo, D., Smolle, J., Argenziano, G., Wolf, I. H., Braun, R., Cerroni, L., Ferrari, A., Hofmann-Wellenhof, R., Kenet, R. O., Magrini, F., et al. (2000) · Journal of Telemedicine and Telecare · doi:10.1258/1357633001935202 · Cited by 136
Self-citation
Cited by
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620
Self-citationDiagnostic accuracy of dermoscopy
Kittler, H., Pehamberger, H., Wolff, K., & Binder, M. (2002) · The Lancet Oncology · doi:10.1016/s1470-2045(02)00679-4 · Cited by 1,349
Self-citationSeven-point checklist and skin lesion classification using multitask multimodal neural nets
Kawahara, J., Daneshvar, S., Argenziano, G., & Hamarneh, G. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2824327 · Cited by 584
Self-citationVascular Structures in Skin Tumors: A Dermoscopy Study
Argenziano, G., Zalaudek, I., Corona, R., Sera, F., Cicale, L., Petrillo, G., Ruocco, E., Hofmann-Wellenhof, R., & Soyer, H. P. (2004) · Archives of Dermatology · doi:10.1001/archderm.140.12.1485 · Cited by 366
Self-citationHow to diagnose nonpigmented skin tumors: A review of vascular structures seen with dermoscopy
Zalaudek, I., Kreusch, J., Giacomel, J., Ferrara, G., Catricalà, C., & Argenziano, G. (2010) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2009.11.697 · Cited by 346
Self-citationDermoscopy in General Dermatology
Zalaudek, I., Argenziano, G., Di Stefani, A., Ferrara, G., Marghoob, A. A., Hofmann-Wellenhof, R., Soyer, H. P., Braun, R., & Kerl, H. (2006) · Dermatology · doi:10.1159/000089015 · Cited by 252
Self-citationAmelanotic/hypomelanotic melanoma: clinical and dermoscopic features
Pizzichetta, M. A., Talamini, R., Stanganelli, I., Puddu, P., Bono, R., Argenziano, G., Veronesi, A., Trevisan, G., Rabinovitz, H., & Soyer, H. P. (2004) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2004.05928.x · Cited by 236
Self-citationAccuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin
Sinz, C., Tschandl, P., Rosendahl, C., Akay, B. N., Argenziano, G., Blum, A., Braun, R. P., Cabo, H., Gourhant, J.-Y., Kreusch, J., et al. (2017) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2017.07.022 · Cited by 127
Self-citationBibi, S., Khan, M. A., Shah, J. H., Damaševičius, R., Alasiry, A., Marzougui, M., Alhaisoni, M., & Masood, A. (2023) · Diagnostics · doi:10.3390/diagnostics13193063 · Cited by 88
“The capacity of dermatologists to discriminate between melanoma and non-melanoma images has been improved by the development of several traditional approaches, including the ABCD rule [17], 7-point checklist [18], Menzies procedure [19], and CASH [20].”
MethodVascularización en dermatoscopia
Martín, J. M., Bella-Navarro, R., & Jordá, E. (2012) · Actas Dermo-Sifiliográficas · doi:10.1016/j.ad.2011.11.005 · Cited by 81
Vascular patterns in basal cell carcinoma
Micantonio, T., Gulia, A., Altobelli, E., Di Cesare, A., Fidanza, R., Riitano, A., Fargnoli, M., & Peris, K. (2011) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/j.1468-3083.2010.03734.x · Cited by 76
Self-citationMassone, C., Wurm, E. M. T., Hofmann-Wellenhof, R., & Soyer, H. P. (2008) · Seminars in Cutaneous Medicine and Surgery · doi:10.1016/j.sder.2007.12.002 · Cited by 74
Self-citationImplementation of the 7-point checklist for melanoma detection on smart handheld devices
Wadhawan, T., Situ, N., Rui, H., Lancaster, K., Yuan, X., & Zouridakis, G. (2011) · 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2011.6090866 · Cited by 72
(2013) · PubMed · Cited by 67
Actinic Keratosis and Non-Invasive Diagnostic Techniques: An Update
Casari, A., Chester, J., & Pellacani, G. (2018) · Biomedicines · doi:10.3390/biomedicines6010008 · Cited by 64
Nonmelanocytic Lesions Defying the Two-Step Dermoscopy Algorithm
SCOPE, A., BENVENUTO-ANDRADE, C., AGERO, A. L. C., & MARGHOOB, A. A. (2006) · Dermatologic Surgery · doi:10.1111/j.1524-4725.2006.32312.x · Cited by 64
The epidemiology, prevention, and detection of melanoma.
(2010) · PubMed Central · Cited by 54
van der Heijden, J. P., Thijssing, L., Witkamp, L., Spuls, P. I., & de Keizer, N. F. (2013) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x13503437 · Cited by 47
Ferrara, G., Argenziano, G., Cerroni, L., Cusano, F., Di Blasi, A., Urso, C., & Soyer, H. P. (2004) · Journal of Telemedicine and Telecare · doi:10.1258/135763304322764176 · Cited by 50
Self-citationCazzato, G., Massaro, A., Colagrande, A., Lettini, T., Cicco, S., Parente, P., Nacchiero, E., Lospalluti, L., Cascardi, E., Giudice, G., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12081972 · Cited by 34
“[22] in 2003 evaluated the sensitivity, specificity and, therefore, diagnostic accuracy of criteria commonly used for the diagnosis of MM.”
MethodStricklin, S. M., Stoecker, W. V., Oliviero, M. C., Rabinovitz, H. S., & Mahajan, S. K. (2011) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/j.1468-3083.2010.03920.x · Cited by 33
Self-citationBandic, J., Kovacevic, S., Karabeg, R., Bandic, M., Lazarov, A., & Opric, D. (2020) · Acta Informatica Medica · doi:10.5455/aim.2020.28.37-41 · Cited by 32
Enhancing skin lesion classification: a CNN approach with human baseline comparison
Ajabani, D., Shaikh, Z. A., Yousef, A., Ali, K., & Albahar, M. A. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2795 · Cited by 18
Mobile Medical Applications for Melanoma Risk Assessment: False Assurance or Valuable Tool?
Chadwick, X., Loescher, L. J., Janda, M., & Soyer, H. P. (2014) · 2014 47th Hawaii International Conference on System Sciences · doi:10.1109/hicss.2014.337 · Cited by 17
Self-citationA comparative study of teledermatoscopy and face-to-face examination of pigmented skin lesions
Ishioka, P., Tenório, J. M., Lopes, P. R., Yamada, S., Michalany, N. S., Amaral, M. B., Pisa, I. T., Hirata, S. H., & Almeida, F. A. (2009) · Journal of Telemedicine and Telecare · doi:10.1258/jtt.2009.081107 · Cited by 17
Metadata
- Authors
- Argenziano, G., Soyer, H. P., Chimenti, S., Talamini, R., Corona, R., Sera, F., Binder, M., Cerroni, L., De Rosa, G., Ferrara, G., Hofmann-Wellenhof, R., Landthaler, M., Menzies, S. W., Pehamberger, H., Piccolo, D., Rabinovitz, H. S., Schiffner, R., Staibano, S., Stolz, W., Bartenjev, I., Blum, A., Braun, R., Cabo, H., Carli, P., De Giorgi, V., Fleming, M. G., Grichnik, J. M., Grin, C. M., Halpern, A. C., Johr, R., Katz, B., Kenet, R. O., Kittler, H., Kreusch, J., Malvehy, J., Mazzocchetti, G., Oliviero, M., zdemir, F., Peris, K., Perotti, R., Perusquia, A., Pizzichetta, M. A., Puig, S., Rao, B., Rubegni, P., Saida, T., Scalvenzi, M., Seidenari, S., Stanganelli, I., Tanaka, M., Westerhoff, K., Wolf, I. H., Braun-Falco, O., Kerl, H., Nishikawa, T., Wolff, K., & Kopf, A. W.
- Venue
- Journal of the American Academy of Dermatology
- Publisher
- Elsevier BV
- Published
- 2003-05
- Volume
- 48
- Issue
- 5
- Pages
- 679-693
- ISSN
- 0190-9622 (ISSN-L)
- OpenAlex
- W1980621031
- Cited by
- 1,208
- References
- 4
- FWCI
- 20.21
- Type
- journal article
- Language
- en
- Open access
- green
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