Background: The incidence and prevalence of skin cancer is rising. A detection model could support the (screening) process of diagnosing non-melanoma skin cancer. Methods: A questionnaire was developed containing potential actinic keratosis (AK) and basal cell carcinoma (BCC) characteristics. Three nurses diagnosed 204 patients with a lesion suspicious of skin (pre)malignancy and filled in the questionnaire. Logistic regression analyses generated prediction models for AK and BCC. Results: A prediction model containing nine characteristics correctly predicted the presence or absence of AK in 83.2% of the cases. BCC was predicted correctly in 91.4% of the cases by a model containing eight characteristics. The nurses correctly diagnosed AK in 88.3% and BCC in 90.9% of the cases. Conclusions: Detection or screening models for AK and BCC could be made with a limited number of variables. Nurses also diagnosed skin lesions correctly in a high percentage of cases. Further research is necessary to investigate the robustness of these findings, whether the percentage of correct diagnoses can be improved and how best to implement model-based prediction in the diagnostic process.

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Dermatology: international journal for clinical and investigative dermatology
Department of Dermatology

van der Geer, S., Kleingeld, P. A. M., Snijders, C. C. P., Rinkens, F. J. C. H., Jansen, G. A. E., Neumann, M., & Krekels, G. (2015). Development of a non-melanoma skin cancer detection model. Dermatology: international journal for clinical and investigative dermatology, 230(2), 161–169. doi:10.1159/000369790