Definitive Hypofractionated Radiotherapy in Elderly Patients With Non-melanoma Skin Cancer: Predicting Complete Response Using a Machine Learning-based Approach

BACKGROUND/AIM: This study aimed to develop a classification tree model to predict clinical complete response (cCR) after definitive hypofractionated RT in elderly patients with non-melanoma (basal and squamous cell carcinoma) skin cancer based on pre-treatment clinical parameters. PATIENTS AND METHODS: Data from elderly patients (aged ≥ 70 years) with histologically proven non-melanoma skin cancer lesion(s) were reviewed. All lesions were treated with definitive hypofractionated RT. A decision tree methodology was applied to define significant pre-treatment parameters influencing the cCR rate. The data set included seven predictor variables (location, histology, T stage, diameter, bleeding, pain, previous treatment) and a dependent variable (cCR). The analysis was conducted using open-source R software. Important explanatory variables were selected using the random forest approach. A classification tree that optimally partitioned patients according to cCR was then built. RESULTS: A total of 121 patients with 145 non-melanoma skin cancer lesions were identified between January 2015 and December 2023. The cCR rate after treatment was 78.6%. The three most important variables identified were T stage, location and pain at diagnosis. Based on these explanatory variables, patients were partitioned in five groups. Of these groups, three experienced cCR rates ranging from 69% to 88% and two experienced cCR rates lower than 15%. CONCLUSION: The proposed classification tree could serve as a valuable tool to guide future research in the field of non-melanoma skin cancer. The decision tree method may be an effective approach to partition elderly patients with non-melanoma skin cancer based on key clinical parameters.