Semi-supervised planning method for breast electronic tissue compensation treatments based on breast radius and separation.

Autor:	Podgorsak AR; State University of New York at Buffalo, Department of Medical Physics, Buffalo, New York, United States.; Roswell Park Comprehensive Cancer Center, Department of Radiation Medicine, Buffalo, New York, United States., Kumaraswamy LK; State University of New York at Buffalo, Department of Medical Physics, Buffalo, New York, United States.; Roswell Park Comprehensive Cancer Center, Department of Radiation Medicine, Buffalo, New York, United States.
Jazyk:	angličtina
Zdroj:	Radiology and oncology [Radiol Oncol] 2020 Dec 22; Vol. 55 (1), pp. 106-115. Date of Electronic Publication: 2020 Dec 22.
DOI:	10.2478/raon-2020-0073
Abstrakt:	Background: The aim of the study was to develop and assess a technique for the optimization of breast electronic tissue compensation (ECOMP) treatment plans based on the breast radius and separation. Materials and Methods: Ten ECOMP plans for 10 breast cancer patients delivered at our institute were collected for this work. Pre-treatment CT-simulation images were anonymized and input to a framework for estimation of the breast radius and separation for each axial slice. Optimal treatment fluence was estimated based on the breast radius and separation, and a total beam fluence map for both medial and lateral fields was generated. These maps were then imported into the Eclipse Treatment Planning System and used to calculate a dose distribution. The distribution was compared to the original treatment hand-optimized by a medical dosimetrist. An additional comparison was performed by generating plans assuming a single tissue penetration depth determined by averaging the breast radius and separation over the entire treatment volume. Comparisons between treatment plans used the dose homogeneity index (HI; lower number is better). Results: HI was non-inferior between our algorithm (HI = 12.6) and the dosimetrist plans (HI = 9.9) ( p -value > 0.05), and was superior than plans obtained using a single penetration depth (HI = 17.0) ( p -value < 0.05) averaged over the 10 collected plans. Our semi-supervised algorithm takes approximately 20 seconds for treatment plan generation and runs with minimal user input, which compares favorably with the dosimetrist plans that can take up to 30 minutes of attention for full optimization. Conclusions: This work indicates the potential clinical utility of a technique for the optimization of ECOMP breast treatments. (© 2021 Alexander R. Podgorsak, Lalith K. Kumaraswamy, published by Sciendo.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu