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

Authors

Francesca DE Felice, Lavinia Spinelli, Paola Critelli, Melissa Scricciolo, Daniela Musio, Annalisa Magnante, Stefania Martini, Giuseppe Minniti, Anna Merlotti

Published in

In vivo (Athens, Greece). Volume 40. Issue 6. Pages 3685-3691.

Abstract

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.
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.
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%.
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.

PMID:
42957261
Bibliographic data and abstract were imported from PubMed on 30 Oct 2026.

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