(Sunday, 7/12/2020) [Eastern Time (GMT-4)]
Room: AAPM ePoster Library
Purpose: Toxicity of organs at risk (OAR) is a great concern for re-irradiation of cancer patients in a region irradiated previously. Biologically effective dose (BED) is needed to consider the effects of different fractionations and gap between the two irradiations in order to create composite dose distributions. Purpose of this study is to develop a practical method for treatment planning of re-irradiation based on organ-specific BED to consider the fact that different OAR may respond to radiation differently.
Methods: A linear-quadratic-linear model (LQ-L) considering tissue repair was used to fit published cell survival data of 12 OARs to extract organ-specific BED parameters (e.g., a, ß and dt). The MIM software was used to host these parameters and to calculate BED in each voxel inside an OAR using the organ-specific parameters for both irradiations. The planning images of the first treatment were then registered to the second treatment images using a contour-based deformable image registration algorithm (DIR) in MIM. The composite 3D BED map is obtained by adding the BEDs of the first treatment after warping to the second images to the BEDs of the second treatment. The entire process was implemented in an MIM workflow and demonstrated by generating clinic composite plans for re-irradiations.
Results: Organ specific BED parameters were obtained, e.g., a/ß=2.5 Gy and 3.4 Gy for spinal cord and kidney, respectively. The MIM workflow was robust to covert physical dose constraints to BED constraints for treatment planning of irradiation and create composite dose distributions of the two irradiations. This method generated more optimized plan for re-irradiation as compared with using physical doses.
Conclusions: A practical method to consider organ-specific BED for re-irradiation planning was developed in MIM. The approach would improve composite dose generation and treatment planning for re-irradiation, such as SBRT following an initial conventional RT.
Keywords
Linear Quadratic Model, Modeling, Image Fusion
Taxonomy
TH- Radiobiology(RBio)/Biology(Bio): RBio- LQ/TCP/NTCP/outcome modeling
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