Key points are not available for this paper at this time.
Abstract Objective. This study aims to develop and validate a pencil beam (PB) algorithm for computing Mayo Clinic Florida microdosimetric kinetic model (MCF MKM)-based relative biological effectiveness (RBE) weighted doses in carbon-ion radiotherapy (CIRT), and to compare its accuracy and efficiency against Monte Carlo (MC) simulations using real patient computed tomography (CT) data. Approach. A PB algorithm was implemented to calculate both physical and microdosimetric parameters—using the abridged microdosimetry distribution methodology (AMDM)—for the MCF MKM model, and subsequently the RBE-weighted dose. Four clinical cases (brain, head and neck, lung and prostate) were planned in-house and computed using the PB algorithm and tool for particle simulation (TOPAS) MC simulations. Dose-volume histograms (DVHs), dose profiles, gamma analysis, and computational times were compared. Monochromatic and polychromatic AMDM kernels were also evaluated to assess any impact on RBE dose distributions. Main results. Except for the lung case, the PB algorithm showed strong agreement with TOPAS MC simulations, with gamma passing rates over 98% at 3%/3 mm and around 90% at 2%/2 mm for the other three cases. DVHs and dose profiles also closely matched. In the lung case, agreement was lower—87.6% at 3%/3 mm and 77.1% at 2%/2 mm—due to PB’s limitations in modeling Coulomb scattering in heterogeneous lung tissue. Still, PB calculations were completed in minutes, highlighting its potential for fast, clinically viable RBE dose evaluation. Significance. This study presents the first complete demonstration of an MCF MKM-based RBE dose calculation using a PB algorithm on actual patient CT data, providing a robust balance between accuracy and computational efficiency. Although limitations in PB modeling may introduce larger discrepancies in highly heterogeneous anatomical regions and sites, the overall performance and speed underscore the method’s viability for routine clinical CIRT planning.
Tan et al. (Wed,) studied this question.