| Article | |
| Title | In Vivo Validation of the BIANCA Biophysical Model: Benchmarking against Rat Spinal Cord RBE Data |
| Author(s) | Carante, Mario P (INFN, Pavia) ; Aricò, Giulia (CERN) ; Ferrari, Alfredo (CERN) ; Karger, Christian P (Heidelberg, Inst. Radiology) ; Kozlowska, Wioletta (CERN ; Med. U. of Vienna, Spitalgasse) ; Mairani, Andrea (HITS, Heidelberg) ; Sala, Paola (INFN, Milan) ; Ballarini, Francesca (INFN, Pavia ; Pavia U.) |
| Publication | 2020 |
| Number of pages | 11 |
| In: | International Journal of Molecular Sciences 21 (2020) 3973 |
| DOI | 10.3390/ijms21113973 |
| Subject category | Health Physics and Radiation Effects |
| Abstract | (1) Background: Cancer ion therapy is constantly growing thanks to its increased precision and, for heavy ions, its increased biological effectiveness (RBE) with respect to conventional photon therapy. The complex dependence of RBE on many factors demands biophysical modeling. Up to now, only the Local Effect Model (LEM), the Microdosimetric Kinetic Model (MKM), and the “mixed-beam” model are used in clinics. (2) Methods: In this work, the BIANCA biophysical model, after extensive benchmarking in vitro, was applied to develop a database predicting cell survival for different ions, energies, and doses. Following interface with the FLUKA Monte Carlo transport code, for the first time, BIANCA was benchmarked against in vivo data obtained by C-ion or proton irradiation of the rat spinal cord. The latter is a well-established model for CNS (central nervous system) late effects, which, in turn, are the main dose-limiting factors for head-and-neck tumors. Furthermore, these data have been considered to validate the LEM version applied in clinics. (3) Results: Although further benchmarking is desirable, the agreement between simulations and data suggests that BIANCA can predict RBE for C-ion or proton treatment of head-and-neck tumors. In particular, the agreement with proton data may be relevant if the current assumption of a constant proton RBE of 1.1 is revised. (4) Conclusions: This work provides the basis for future benchmarking against patient data, as well as the development of other databases for specific tumor types and/or normal tissues. |
| Copyright/License | publication: © 2020-2024 the authors (License: CC-BY-4.0) |
Corresponding record in: Inspire
Notice créée le 2022-02-12, modifiée le 2022-10-31
