Treatment planning with high-resolution 3D dose maps in preclinical and translational synchrotron microbeam radiation therapy

Physics and Imaging in Radiation Oncology(2024)

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摘要
Background and Purpose: Microbeam Radiation Therapy (MRT) aims to deliver higher doses to the target while minimizing radiation damage to healthy tissues using synchrotron x-ray microbeams. Translational MRT research has now started, driven by promising results from preclinical studies. This study aimed to propose a first dose-outcome model by analyzing micrometric dose distributions obtained with high-resolution 3D dose calculations, accounting for the inherent physical dose distribution complexity in MRT. The feasibility of integrating penMRT, our full Monte Carlo multiscale dose calculation algorithm based on PENELOPE into translational research on veterinary patients was also investigated. Material and Methods: Micrometric dose distributions were calculated in tumor-bearing rats and for a veterinary patient with penMRT, for conformal multi-directional MRT treatment plans. Absorbed dose maps were obtained with 0.005 × 0.005 × 1 mm3 voxel sizes. High-resolution dose-volume histograms were extracted and analyzed against radiobiology studies. Results: The complexity of the MRT dose distribution was properly rendered at a micrometer scale on 3D dose maps, with well separated dose regions observed on the differential dose-volume histograms. The median survival time of glioma-bearing rats varied linearly with the volume fraction of the planning target volume that received doses higher than 50 Gy (R2 = 0.98). The feasibility of using penMRT for treatment planning in large volumes has been shown on a veterinary patient. Conclusions: This study demonstrated the significant added value of penMRT for planning and prescribing MRT treatments. It also shed light on the correlation between the high-resolution 3D dose distributions and the treatment outcome.
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关键词
Synchrotron microbeam radiation therapy,Treatment planning,Monte-Carlo dose calculation,Translational research,Prediction models
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