Development of a non-melanoma skin cancer detection model

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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