Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.
Authors
Affiliations (6)
Affiliations (6)
- Markusovszky University Teaching Hospital, Markusovszky str. 5, Szombathely, 9700, Hungary. [email protected].
- Markusovszky University Teaching Hospital, Markusovszky str. 5, Szombathely, 9700, Hungary.
- Miskolci SZC Bláthy Ottó Villamosipari Technikum, Soltész Nagy Kálmán str 7, Miskolc, 3527, Hungary.
- Jósa András University Teaching Hospital, Szent István str. 68, Nyíregyháza, 4400, Hungary.
- University of Nyíregyháza, Sóstói str.31/B, Nyíregyháza, 4400, Hungary.
- HUN-REN Institute for Nuclear Research, Bem square 18/c, Debrecen, 4026, Hungary.
Abstract
One possible adverse effect of breast irradiation is the development of pulmonary fibrosis. The aim of this study was to determine whether planning CT scans can predict which patients are more likely to develop lung lesions after treatment. A retrospective analysis of 242 patient records was performed using different machine learning models. These models showed a remarkable correlation between the occurrence of fibrosis and the hounsfield units of lungs in CT data. Three different classification methods (Tree, Kernel-based, k-Nearest Neighbors) showed predictive values above 60%. The human predictive factor (HPF), a mathematical predictive model, further strengthened the association between lung hounsfield unit (HU) metrics and radiation-induced lung injury (RILI). These approaches optimize radiation treatment plans to preserve lung health. Machine learning models and HPF can also provide effective diagnostic and therapeutic support for other diseases.