The main advantages of proton therapy are the reduced total energy deposited in the patient as compared to photon techniques and the finite range of the proton beam. The latter adds an additional degree of freedom to treatment planning. The range in tissue is associated with considerable uncertainties caused by imaging, patient setup, beam delivery and dose calculation. Reducing the uncertainties would allow a reduction of the treatment volume and thus allow a better utilization of the advantages of protons. This paper summarizes the role of Monte Carlo simulations when aiming at a reduction of range uncertainties in proton therapy. Differences in dose calculation when comparing Monte Carlo with analytical algorithms are analyzed as well as range uncertainties due to material constants and CT conversion. Range uncertainties due to biological effects and the role of Monte Carlo for in vivo range verification are discussed. Furthermore, the current range uncertainty recipes used at several proton therapy facilities are revisited. We conclude that a significant impact of Monte Carlo dose calculation can be expected in complex geometries where local range uncertainties due to multiple Coulomb scattering will reduce the accuracy of analytical algorithms. In these cases Monte Carlo techniques might reduce the range uncertainty by several mm.

The dose distribution delivered in charged particle therapy is due to both primary and secondary particles. The secondaries, originating from non-elastic nuclear interactions, are of interest for three reasons. First, if fast Monte Carlo treatment planning is envisaged, the question arises whether all nuclear interaction products deliver a significant contribution to the total dose and, hence, need to be tracked. Second, there could be an enhanced relative biological effectiveness (RBE) due to low energy and/or heavy secondaries. Third, neutrons originating from nuclear interactions may deliver dose outside the target volume. The particle yield from different nuclear interaction channels as a function of proton penetration depth was studied theoretically for different proton beam energies. Three-dimensional dose distributions from primary and secondary particles were simulated for an unmodulated 160 MeV proton beam with and without including a slice of bone material and for a spread-out Bragg peak (SOBP) of 3 x 3 x 3 cm3 in water. Secondary protons deliver up to 10% of the total dose proximal to the Bragg peak of an unmodulated proton beam and they affect the flatness of the SOBP. Furthermore, they cause a dose build-up due to forward emission of secondary particles from nuclear interactions. The dose deposited by d, t, 3He and alpha-particles was found to contribute less than 0.1% of the total dose. The dose distal to the target volume caused by liberated neutrons was studied for four proton beam energies in the range of 160-250 MeV and found to be below 0.05% (2 cm distal to SOBP) of the prescribed target dose for a 3 x 3 x 3 cm3 target. RBE values relative to 60Co were calculated proximal to and within the SOBP. The RBE proximal to the Bragg peak (100% dose) is influenced by secondary particles (mainly protons and a-particles) with a strong dose dependency resulting in RBE values up to 1.2 (2 Gy; inactivation of V79). Depending on the endpoint considered, secondary particles cause a shift in RBE by up to 8% at 2 Gy. In contrast, the RBE in the Bragg peak is almost entirely determined by primary protons due to a decreasing secondary particle fluence with depth. RBE values up to 1.3 (2 Gy; inactivation of V79) at 1 cm distal to the Bragg peak maximum were found. The inactivations of human skin fibroblasts and mouse lymphoma cells were also analysed and reveal a substantial tissue dependency of the total RBE. The outcome of this study shows that elevated RBE values occur not only at the distal edge of the SOBP. Although the variations are modest, and in most cases might have no observable clinical effect, they might have to be considered in certain treatment situations. The biological effect downstream of the target caused by neutrons was analysed using a radiation quality factor of 10. The biological dose was found to be below 0.5% of the prescribed target dose (for a 3 x 3 x 3 cm3 SOBP) but depends on the size of the SOBP. This dose should not be significant with respect to late effects, e.g. cancer induction.

Accurate radiation dose calculation is essential for successful proton radiotherapy. Monte Carlo (MC) simulation is considered to be the most accurate method. However, the long computation time limits it from routine clinical applications. Recently, graphics processing units (GPUs) have been widely used to accelerate computationally intensive tasks in radiotherapy. We have developed a fast MC dose calculation package, gPMC, for proton dose calculation on a GPU. In gPMC, proton transport is modeled by the class II condensed history simulation scheme with a continuous slowing down approximation. Ionization, elastic and inelastic proton nucleus interactions are considered. Energy straggling and multiple scattering are modeled. Secondary electrons are not transported and their energies are locally deposited. After an inelastic nuclear interaction event, a variety of products are generated using an empirical model. Among them, charged nuclear fragments are terminated with energy locally deposited. Secondary protons are stored in a stack and transported after finishing transport of the primary protons, while secondary neutral particles are neglected. gPMC is implemented on the GPU under the CUDA platform. We have validated gPMC using the TOPAS/Geant4 MC code as the gold standard. For various cases including homogeneous and inhomogeneous phantoms as well as a patient case, good agreements between gPMC and TOPAS/Geant4 are observed. The gamma passing rate for the 2%/2 mm criterion is over 98.7% in the region with dose greater than 10% maximum dose in all cases, excluding low-density air regions. With gPMC it takes only 6-22 s to simulate 10 million source protons to achieve ∼1% relative statistical uncertainty, depending on the phantoms and energy. This is an extremely high efficiency compared to the computational time of tens of CPU hours for TOPAS/Geant4. Our fast GPU-based code can thus facilitate the routine use of MC dose calculation in proton therapy.