An ion treatment planning framework for inclusion of nanodosimetric ionization detail through cluster dose
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arXiv
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| Format: | Preprint |
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2025
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| author | Facchiano, Simona Ortiz, Ramon Cristoforetti, Remo D-Kondo, Naoki Jaekel, Oliver Faddegon, Bruce Wahl, Niklas |
| author_facet | Facchiano, Simona Ortiz, Ramon Cristoforetti, Remo D-Kondo, Naoki Jaekel, Oliver Faddegon, Bruce Wahl, Niklas |
| contents | Nanodosimetry relates Ionization Detail (ID) and ionization parameters (Ip) to biological endpoints relevant for charged-particle radiotherapy. This supports a more physics-based modeling of biological effectiveness than traditional dose-response relationships and RBE models. Faddegon et al. (2023) introduced cluster dose g(Ip) as a physical quantity, bridging ID to the treatment planning level, which can be directly optimized. We developed a framework enabling cluster dose optimization via a pencil-beam (PB) algorithm, and validated against Monte Carlo (MC) simulations. The framework, integrated into the treatment planning toolkit matRad, uses precomputed Ip values obtained from MC track-structure simulations. We applied our tool for plan optimization with protons, helium, and carbon ions in a water phantom and a prostate case. Recalculation with TOPAS showed 3D gamma passing rates >97% (phantom) and >98% (patient) using a 3%/3mm criterion and a threshold of 10% of the maximum dose. Cluster dose F5 optimization produced homogeneous target coverage, with heavier ions requiring lower absorbed doses for the same prescribed cluster dose level. This demonstrates the feasibility of fast, accurate cluster dose optimization using PB algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09667 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An ion treatment planning framework for inclusion of nanodosimetric ionization detail through cluster dose Facchiano, Simona Ortiz, Ramon Cristoforetti, Remo D-Kondo, Naoki Jaekel, Oliver Faddegon, Bruce Wahl, Niklas Medical Physics Nanodosimetry relates Ionization Detail (ID) and ionization parameters (Ip) to biological endpoints relevant for charged-particle radiotherapy. This supports a more physics-based modeling of biological effectiveness than traditional dose-response relationships and RBE models. Faddegon et al. (2023) introduced cluster dose g(Ip) as a physical quantity, bridging ID to the treatment planning level, which can be directly optimized. We developed a framework enabling cluster dose optimization via a pencil-beam (PB) algorithm, and validated against Monte Carlo (MC) simulations. The framework, integrated into the treatment planning toolkit matRad, uses precomputed Ip values obtained from MC track-structure simulations. We applied our tool for plan optimization with protons, helium, and carbon ions in a water phantom and a prostate case. Recalculation with TOPAS showed 3D gamma passing rates >97% (phantom) and >98% (patient) using a 3%/3mm criterion and a threshold of 10% of the maximum dose. Cluster dose F5 optimization produced homogeneous target coverage, with heavier ions requiring lower absorbed doses for the same prescribed cluster dose level. This demonstrates the feasibility of fast, accurate cluster dose optimization using PB algorithms. |
| title | An ion treatment planning framework for inclusion of nanodosimetric ionization detail through cluster dose |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2505.09667 |