Use of Machine Learning techniques on a database of breast cancer patients treated with the FAST-Forward adjuvant radiotherapy scheme

The FAST-Forward scheme is an adjuvant radiotherapy protocol for breast cancer treatment. In this study, we analyze a patient database treated with this scheme to demonstrate its non-inferiority to the standard protocol in terms of local tumor control and safety regarding toxicity. Our findings supp...

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Bibliographic Details
Published in:Procedia computer science 2024, Vol.246, p.5195-5204
Main Authors: López, Kristina Lacasta, Fajardo, Paloma Sosa, Ortega, Juan Antonio, Luis, José, Guerra, López, Gonzalez-Abril, Luis, de Haro Piedra, Roberto, Ortigosa, Eva Tejada, Blanco, Isabela Gaztelu
Format: Article
Language:English
Subjects:
Breast Cancer
Clustering
FAST-FORWARD
Machine Learning
Radiotherapy
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Summary:The FAST-Forward scheme is an adjuvant radiotherapy protocol for breast cancer treatment. In this study, we analyze a patient database treated with this scheme to demonstrate its non-inferiority to the standard protocol in terms of local tumor control and safety regarding toxicity. Our findings support the continued use of the FAST-Forward scheme in clinical practice. Additionally, we employ machine learning techniques to identify subgroups of patients with similar characteristics and uncover clinically relevant patterns. These algorithms have enabled us to define three distinct patient profiles based on treatment regimen, molecular subtype, and toxicity outcomes. Furthermore, we observed a correlation between maximum skin dose and long-term induration, as well as the impact of breast volume on short-term hyperpigmentation.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2024.09.617