Agreement on classification of clinical photographs of pigmentary lesions: exercise after a training course with young dermatologists


Published: 14 July 2022
Abstract Views: 715
PDF: 209
HTML: 5
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

Smartphone apps may help promoting the early diagnosis of melanoma. The reliability of specialist judgment on lesions should be assessed. Hereby, we evaluated the agreement of 6 young dermatologists, after a specific training. Clinical judgment was evaluated during 2 online sessions, 1 month apart, on a series of 45 pigmentary lesions. Lesions were classified as highly suspicious, suspicious, non-suspicious or not assessable. Cohen’s and Fleiss’ kappa were used to calculate intra- and inter-rater agreement. The overall intra-rater agreement was 0.42 (95% confidence interval - CI: 0.33-0.50), varying between 0.12-0.59 on single raters. The inter-rater agreement during the first phase was 0.29 (95% CI: 0.24-0.34). When considering the agreement for each category of judgment, kappa varied from 0.19 for not assessable to 0.48 for highly suspicious lesions. Similar results were obtained in the second exercise. The study showed a less than satisfactory agreement among young dermatologists. Our data point to the need for improving the reliability of the clinical diagnoses of melanoma especially when assessing small lesions and when dealing with thin melanomas at a population level.


Farberg AS, Rigel DS. The Importance of Early Recognition of Skin Cancer. Dermatol Clin 2017;35:xv-xvi.

Coroiu A, Moran C, Bergeron C, et al. Operationalization of skin self-examination in randomized controlled trials with individuals at increased risk for melanoma: A systematic review. Patient Educ Couns 2020;103:1013-26.

Ana FA, Loreto MS, José LM, et al. Mobile applications in oncology: A systematic review of health science databases. Int J Med Inform 2020;133:104001.

Chuchu N, Takwoingi Y, Dinnes J, et al. Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma. Cochrane Database Syst Rev 2018;12:CD013192.

Kittler H, Pehamberger H, Wolff K, Binder M. Diagnostic accuracy of dermoscopy. Lancet Oncol 2002;3:159-65.

Cazzaniga S, Castelli E, Di Landro A, et al. Mobile teledermatology for melanoma detection: Assessment of the validity in the framework of a population-based skin cancer awareness campaign in northern Italy. J Am Acad Dermatol 2019;81:257-60.

Roush GC, Barnhill RL, Ernstoff MS, Kirkwood JM. Inter-clinician agreement on the recognition of clinical pigmentary characteristics of patients with cutaneous malignant melanoma. Studies of melanocytic nevi, VI. Br J Cancer 1991;64:373-6.

Yu P, Li X, Huang Y, et al. Inter- and intra-observer agreement in dermatologists' diagnoses of hyperpigmented facial lesions and development of an algorithm for automated diagnosis. Skin Res Technol 2019;25:777-86.

Ontario Health (Quality). Pigmented Lesion Assay for Suspected Melanoma Lesions: A Health Technology Assessment. Ont Health Technol Assess Ser 2021;21:1-81.

Pizzichetta MA, Talamini R, Piccolo D, et al. Interobserver agreement of the dermoscopic diagnosis of 129 small melanocytic skin lesions. Tumori 2002;88:234-8.

Carrera C, Marchetti MA, Dusza SW, et al. Validity and reliability of dermoscopic criteria used to differentiate nevi from melanoma: a web-based International Dermoscopy Society study. JAMA Dermatol 2016;152:798-806.

Carli P, De Giorgi V, Naldi L, Dosi G. Reliability and inter-observer agreement of dermoscopic diagnosis of melanoma and melanocytic naevi. Dermoscopy Panel. Eur J Cancer Prev 1998;7:397-402.

Winkler JK, Sies K, Fink C, et al. Collective human intelligence outperforms artificial intelligence in a skin lesion classification task. J Dtsch Dermatol Ges 2021;19:1178-84.

Popescu D, El-Khatib M, El-Khatib H, Ichim L. New trends in melanoma detection using neural networks: a systematic review. Sensors (Basel) 2022;22:496.

Cazzaniga, S., De Ponti, L., Baratelli, G. M., Francione, S., La Vecchia, C., Di Landro, A., Carugno, A., Di Mercurio, M., Germi, L., Trevisan, G., Fenaroli, M., Capasso, C., Pezza, M., Dri, P., Castelli, E., & Naldi, L. . (2022). Agreement on classification of clinical photographs of pigmentary lesions: exercise after a training course with young dermatologists. Dermatology Reports, 15(1). https://doi.org/10.4081/dr.2022.9500

Downloads

Download data is not yet available.

Citations