Abstract
Breast Cancer is no longer considered a death sentence and the number of patients receiving radiotherapy is increasing every year. Advancements in RT technology allow more accurate and accumulated delivery of radiation significantly reducing the risk of side effects. The use of these techniques has led to better clinical outcomes and significantly increased long-term survival rates. Therefore, secondary cancer risk after breast conserving therapy is becoming more important. In this study, we estimate the risks of developing a solid second cancer after radiotherapy of breast cancer using the concept of organ equivalent dose (OED).
Eight breast cancer patients were retrospectively selected for this study. Three-dimensional conformal radiotherapy (3DCRT), intensity modulated radiotherapy (IMRT), and volumetric modulated arc therapy (VMAT) were planned to deliver a prescribed dose. Differential dose volume histograms (dDVHs) were created and the OEDs calculated. Secondary cancer risks of ipsilateral, contralateral lung and thyroid gland were estimated using linear, linear-exponential and plateau models.
The highest interest of our study was the evaluation of secondary cancer risk for the organs at risk (OAR), which are located far from the treatment region and are very sensitive to radiation exposure. Our results showed very high secondary cancer excess absolute risk (EAR) values for IMRT and VMAT compared with 3DCRT. It has to be noted, that a significant reduction of the EARs for the contralateral lung, ipsilateral lung and thyroid gland was observed in all dose–response models.
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This work was supported by Georgian Technical University.
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Petriashvili, L., Khomeriki, I., Topeshashvili, M., Lominadze, T., Shanidze, R., Osepashvili, M. (2023). Analysing Dose Parameters of Radiation Therapy Treatment Planning and Estimation of Their Influence on Secondary Cancer Risks. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13919. Springer, Cham. https://doi.org/10.1007/978-3-031-34953-9_18
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