Thomas Dirscherl1, Mark Rickhey, Ludwig Bogner. 1. Universitätsklinikum Regensburg, Klinik und Poliklinik für Strahlentherapie, Regensburg, Germany. thomas.dirscherl@klinik.uni-regensburg.de
Abstract
INTRODUCTION: A biologically adaptive radiation treatment method to maximize the TCP is shown. Functional imaging is used to acquire a heterogeneous dose prescription in terms of Dose Painting by Numbers and to create a patient-specific IMRT plan. METHOD AND MATERIALS: Adapted from a method for selective dose escalation under the guidance of spatial biology distribution, a model, which translates heterogeneously distributed radiobiological parameters into voxelwise dose prescriptions, was developed. At the example of a prostate case with (18)F-choline PET imaging, different sets of reported values for the parameters were examined concerning their resulting range of dose values. Furthermore, the influence of each parameter of the linear-quadratic model was investigated. A correlation between PET signal and proliferation as well as cell density was assumed. Using our in-house treatment planning software Direct Monte Carlo Optimization (DMCO), a treatment plan based on the obtained dose prescription was generated. Gafchromic EBT films were irradiated for evaluation. RESULTS: When a TCP of 95% was aimed at, the maximal dose in a voxel of the prescription exceeded 100Gy for most considered parameter sets. One of the parameter sets resulted in a dose range of 87.1Gy to 99.3Gy, yielding a TCP of 94.7%, and was investigated more closely. The TCP of the plan decreased to 73.5% after optimization based on that prescription. The dose difference histogram of optimized and prescribed dose revealed a mean of -1.64Gy and a standard deviation of 4.02Gy. Film verification showed a reasonable agreement of planned and delivered dose. CONCLUSION: If the distribution of radiobiological parameters within a tumor is known, this model can be used to create a dose-painting by numbers plan which maximizes the TCP. It could be shown, that such a heterogeneous dose distribution is technically feasible. Copyright Â
INTRODUCTION: A biologically adaptive radiation treatment method to maximize the TCP is shown. Functional imaging is used to acquire a heterogeneous dose prescription in terms of Dose Painting by Numbers and to create a patient-specific IMRT plan. METHOD AND MATERIALS: Adapted from a method for selective dose escalation under the guidance of spatial biology distribution, a model, which translates heterogeneously distributed radiobiological parameters into voxelwise dose prescriptions, was developed. At the example of a prostate case with (18)F-choline PET imaging, different sets of reported values for the parameters were examined concerning their resulting range of dose values. Furthermore, the influence of each parameter of the linear-quadratic model was investigated. A correlation between PET signal and proliferation as well as cell density was assumed. Using our in-house treatment planning software Direct Monte Carlo Optimization (DMCO), a treatment plan based on the obtained dose prescription was generated. Gafchromic EBT films were irradiated for evaluation. RESULTS: When a TCP of 95% was aimed at, the maximal dose in a voxel of the prescription exceeded 100Gy for most considered parameter sets. One of the parameter sets resulted in a dose range of 87.1Gy to 99.3Gy, yielding a TCP of 94.7%, and was investigated more closely. The TCP of the plan decreased to 73.5% after optimization based on that prescription. The dose difference histogram of optimized and prescribed dose revealed a mean of -1.64Gy and a standard deviation of 4.02Gy. Film verification showed a reasonable agreement of planned and delivered dose. CONCLUSION: If the distribution of radiobiological parameters within a tumor is known, this model can be used to create a dose-painting by numbers plan which maximizes the TCP. It could be shown, that such a heterogeneous dose distribution is technically feasible. Copyright Â
Authors: E J Her; A Haworth; H M Reynolds; Y Sun; A Kennedy; V Panettieri; M Bangert; S Williams; M A Ebert Journal: Radiat Oncol Date: 2020-07-13 Impact factor: 3.481
Authors: Araceli Gago-Arias; Beatriz Sánchez-Nieto; Ignacio Espinoza; Christian P Karger; Juan Pardo-Montero Journal: PLoS One Date: 2018-04-26 Impact factor: 3.240