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Extracting organ-specific radiobiological model  parameters from clinical data for radiation therapy planning of head and neck cancers
  • Edwin E. Quashie
Edwin E. Quashie
Quantum Simulations Group, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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Abstract

Purpose:  Different radiation therapy (RT) strategies, e.g., conventional fractionation, hypofractionation, stereotactic body radiotherapy (SBRT), adaptive RT, and re-irradiation are often used to treat head and neck cancers. Combining and/or comparing these strategies require to calculate biological effective dose (BED). The purpose of this study is to develop a practical process to extract organ-specific radiobiologic model parameters that may be used for BED calculations in individualized RT planning for head and neck cancer. 
Methods:  The dose volume constraint data for 12 organs at risk (OAR) corresponding to no grade ≥ 3 toxicity for SBRT, hypofractionated and convectional fractionation treatments were compiled from various sources such as TG101, QUANTEC and clinical trial database. These data cover 10 fractionation schemes. A linear-quadratic-linear (LQ-L) model was used to extract model parameters (e.g., \(\alpha/\beta\),  \(\gamma/\alpha\) and \(d_t\)) from fitting the Iso-BED curves of those dose volume constraints for the OARs using an in-house python code. The obtained model parameters were integrated into a practical workflow in MIM software. Their practicality was tested for treatment planning of re-irradiation.
Results:   The LQ-L model fitted the data well as indicated by smooth transition curves from SBRT to conventional fractionation data. The organ-specific model parameters were obtained, for example, \(\alpha/\beta\)=2.57Gy,  \(\gamma/\alpha\)=9.07Gy and \(d_t\)=7.65Gy, and \(\alpha/\beta\)=2.34Gy,  \(\gamma/\alpha\)=9.67Gy and \(d_t\)=6.80Gy for spinal cord and brainstem, respectively. The composite dose plans for retreatment of head and neck cancers with considering the obtained model parameters show up to 10% difference in physical dose constraints from traditional EQD2 method with \(\alpha/\beta\)=3.0Gy.
Conclusion:   A practical process was developed to obtain organ-specific radiobiological model parameters from clinical data and was shown to generate more clinically relevant model parameters for calculating BED distributions compared to those extracted from cell line survival data. The practicality of the obtained parameters was demonstrated in creating composite plans for retreatment.