Abstract
Background: Due to their favorable physical and biological properties, helium ion beams are increasingly considered a promising alternative to proton beams for radiation therapy. Hence, this work aims at comparing in-silico the treatment of brain and ocular meningiomas with protons and helium ions, using for the first time a dedicated Monte Carlo (MC) based treatment planning engine (MCTP) thoroughly validated both in terms of physical and biological models. Methods: Starting from clinical treatment plans of four patients undergoing proton therapy with a fixed relative biological effectiveness (RBE) of 1.1 and a fraction dose of 1.8 Gy(RBE), new treatment plans were optimized with MCTP for both protons (with variable and fixed RBE) and helium ions (with variable RBE) under the same constraints derived from the initial clinical plans. The resulting dose distributions were dosimetrically compared in terms of dose volume histograms (DVH) parameters for the planning target volume (PTV) and the organs at risk (OARs), as well as dose difference maps. Results: In most of the cases helium ion plans provided a similar PTV coverage as protons with a consistent trend of superior OAR sparing. The latter finding was attributed to the ability of helium ions to offer sharper distal and lateral dose fall-offs, as well as a more favorable differential RBE variation in target and normal tissue. Conclusions: Although more studies are needed to investigate the clinical potential of helium ions for different tumour entities, the results of this work based on an experimentally validated MC engine support the promise of this modality with state-of-the-art pencil beam scanning delivery, especially in case of tumours growing in close proximity of multiple OARs such as meningiomas.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Physik |
Themengebiete: | 500 Naturwissenschaften und Mathematik > 530 Physik |
URN: | urn:nbn:de:bvb:19-epub-67148-8 |
ISSN: | 1748-717X |
Sprache: | Englisch |
Dokumenten ID: | 67148 |
Datum der Veröffentlichung auf Open Access LMU: | 19. Jul. 2019, 12:22 |
Letzte Änderungen: | 04. Nov. 2020, 13:49 |