Definitive Hypofractionated Radiotherapy in Elderly Patients With Non-melanoma Skin Cancer: Predicting Complete Response Using a Machine Learning-based Approach
4 affiliations
- 1Department of Radiotherapy, Policlinico Umberto I, Department of Radiological, Oncological and Pathological Sciences, “Sapienza” University of Rome, Rome, Italy
- 2Radiotherapy Department S.Croce & Carle Teaching Hospital, Cuneo, Italy
- 3Department of Radiation Oncology, Ospedale dell’Angelo, Venice, Italy
- 4Department of Radiotherapy Santa Maria Addolorata Hospital, Rome, Italy
Abstract
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.
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BibTeX
@article{felice2026definitive,
title = {{Definitive Hypofractionated Radiotherapy in Elderly Patients With Non-melanoma Skin Cancer: Predicting Complete Response Using a Machine Learning-based Approach}},
author = {FRANCESCA DE FELICE and LAVINIA SPINELLI and PAOLA CRITELLI and MELISSA SCRICCIOLO and DANIELA MUSIO and ANNALISA MAGNANTE and STEFANIA MARTINI and GIUSEPPE MINNITI and ANNA MERLOTTI},
journal = {In Vivo},
year = {2026},
month = oct,
doi = {10.21873/invivo.14507},
url = {https://doi.org/10.21873/invivo.14507},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Definitive Hypofractionated Radiotherapy in Elderly Patients With Non-melanoma Skin Cancer: Predicting Complete Response Using a Machine Learning-based Approach
AU - FRANCESCA DE FELICE
AU - LAVINIA SPINELLI
AU - PAOLA CRITELLI
AU - MELISSA SCRICCIOLO
AU - DANIELA MUSIO
AU - ANNALISA MAGNANTE
AU - STEFANIA MARTINI
AU - GIUSEPPE MINNITI
AU - ANNA MERLOTTI
PY - 2026
DA - 2026/10/29
JO - In Vivo
DO - 10.21873/invivo.14507
UR - https://doi.org/10.21873/invivo.14507
AB - 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.
SN - 0258-851X
SN - 1791-7549
ER -