Clinical consequences of relative biological effectiveness variations in proton radiotherapy of the prostate, brain and liver

Proton relative biological effectiveness (RBE) is known to depend on the (alpha/beta)(x) of irradiated tissues, with evidence of similar to 60% variation over (alpha/beta)(x) values from 1-10 Gy. The range of (alpha/beta)(x) values reported for prostate tumors (1.2-5.0 Gy), brain tumors (10-15 Gy) and liver tumors (13-17 Gy) imply that the proton RBE for these tissues could vary significantly compared to the commonly used generic value of 1.1. Our aim is to evaluate the impact of this uncertainty on the proton dose in Gy(RBE) absorbed in normal and tumor tissues. This evaluation was performed for standard and hypofractionated regimens. RBE-weighted total dose (RWTD) distributions for 15 patients (five prostate tumors, five brain tumors and five liver tumors) were calculated using an in-house developed RBE model as a function of dose, dose-averaged linear energy transfer (LETd) and (alpha/beta)(x). Variations of the dose-volume histograms (DVHs) for the gross tumor volume (GTV) and the organs at risk due to changes of (alpha/beta)(x) and fractionation regimen were calculated and the RWTD received by 10% and 90% of the organ volume reported. The goodness of the plan, bearing the uncertainties, was then evaluated compared to the delivered plan, which considers a constant RBE of 1.1. For standard fractionated regimens, the prostate tumors, liver tumors and all critical structures in the brain showed typically larger RBE values than 1.1. However, in hypofractionated regimens lower values of RBE than 1.1 were observed in most cases. Based on DVH analysis we found that the RBE variations were clinically significant in particular for the prostate GTV and the critical structures in the brain. Despite the uncertainties in the biological input parameters when estimating RBE values, the results show that the use of a variable RBE with dose, LETd and (alpha/beta)(x) could help to further optimize the target dose in proton treatment planning. Most importantly, this study shows that the consideration of RBE variations could influence the comparison of proton and photon treatments in clinical trials, in particular in the case of the prostate

Renormalization of radiobiological response functions by energy loss fluctuations and complexities in chromosome aberration induction: deactivation theory for proton therapy from cells to tumor control

We employ a multi-scale mechanistic approach to investigate radiation induced cell toxicities and deactivation mechanisms as a function of linear energy transfer in hadron therapy. Our theoretical model consists of a system of Markov chains in microscopic and macroscopic spatio-temporal landscapes, i.e., stochastic birth-death processes of cells in millimeter-scale colonies that incorporates a coarse-grained driving force to account for microscopic radiation induced damage. The coupling, hence the driving force in this process, stems from a nano-meter scale radiation induced DNA damage that incorporates the enzymatic end-joining repair and mis-repair mechanisms. We use this model for global fitting of the high-throughput and high accuracy clonogenic cell-survival data acquired under exposure of the therapeutic scanned proton beams, the experimental design that considers $\gamma$-H2AX as the biological endpoint and exhibits maximum observed achievable dose and LET, beyond which the majority of the cells undergo collective biological deactivation processes. An estimate to optimal dose and LET calculated from tumor control probability by extension to $~ 10^6$ cells per $mm$-size voxels is presented. We attribute the increase in degree of complexity in chromosome aberration to variabilities in the observed biological responses as the beam linear energy transfer (LET) increases, and verify consistency of the predicted cell death probability with the in-vitro cell survival assay of approximately 100 non-small cell lung cancer (NSCLC) cells

A kernel‐based algorithm for the spectral fluence of clinical proton beams to calculate dose‐averaged LET and other dosimetric quantities of interest

PURPOSE: To introduce a new analytical methodology to calculate quantities of interest in particle radiotherapy inside the treatment planning system. Models are proposed to calculate dose-averaged LET (LETd) in proton radiotherapy. MATERIAL AND METHODS: A kernel-based approach for the spectral fluence of particles is developed by means of analytical functions depending on depth and lateral position. These functions are obtained by fitting them to data calculated with Monte Carlo (MC) simulations using Geant4 in liquid water for energies from 50 to 250 MeV. Contributions of primary, secondary protons and alpha particles are modeled separately. Lateral profiles and spectra are modeled as Gaussian functions to be convolved with the fluence coming from the nozzle. LETd is obtained by integrating the stopping power curves from the PSTAR and ASTAR databases weighted by the spectrum at each position. The fast MC code MCsquare is employed to benchmark the results. RESULTS: Considering the nine energies simulated, fits for the functions modeling the fluence in-depth provide an average R2R2 equal to 0.998, 0.995 and 0.986 for each one of the particles considered. Fits for the Gaussian lateral functions yield average R2R2 of 0.997, 0.982 and 0.993, respectively. Similarly, the Gaussian functions fitted to the computed spectra lead to average R2R2 of 0.995, 0.938 and 0.902. LETd calculation in water shows mean differences of -0.007 ± 0.008 keV/μm with respect to MCsquare if only protons are considered and 0.022 ± 0.007 keV/μm including alpha particles. In a prostate case, mean difference for all voxels with dose >5% of prescribed dose is 0.28 ± 0.23 keV/μm. CONCLUSION: This new spectral fluence-based methodology allows for simultaneous calculations of quantities of interest in proton radiotherapy such as dose, LETd or microdosimetric quantities. The method also enables the inclusion of more particles by following an analogous process

Segment-averaged LET concept and analytical calculation from microdosimetric quantities in proton radiation therapy

Purpose: This work introduces the concept of segment-averaged linear energy transfer (LET) as a new approach to average distributions of LET of proton beams based on a revisiting of microdosimetry theory. The concept of segment-averaged LET is then used to generate an analytical model from Monte Carlo simulations data to perform fast and accurate calculations of LET distributions for proton beams and material: The distribution of energy imparted by a proton beam into a representative biological structure or site is influenced by the distributions of (1) LET, (2) segment length, which is the section of the proton track in the site, and (3) energy straggling of the proton beam. The distribution of LET is thus generated by the LET of each component of the beam in the site. However, the situation when the LET of each single proton varies appreciably along its path in the site is not defined. Therefore, a new distribution can be obtained if the particle track segment is decomposed into smaller portions in which LET is roughly constant. We have called “segment distribution” of LET the one generated by the contribution of each portion. The average of that distribution is called segment-averaged LET. This quantity is obtained in the microdosimetry theory from the average and standard deviation of the distributions of energy imparted to the site, segment length and energy imparted per collision. All this information is calculated for protons of clinically relevant energies by means of Geant4-DNA microdosimetric simulations. Finally, a set of analytical functions is proposed for each one of the previous quantities. The presented model functions are fitted to data from Geant4-DNA simulations for monoenergetic beams from 100 keV to 100 MeV and for spherical sites of 1 μm, 5 μm and 10 μm in diameter. Results: The average differences along the considered energy range between calculations based on our analytical models and MC for segment-averaged dose-averaged restricted LET are -0.2 ± 0.7 keV/μm for the 1 μm case, 0.0 ± 0.9 keV/μm for the 5 μm case and -0.3 ± 1.1 keV/μm for the 10 μm case, respectively. All average differences are below the average standard deviation (1σ) of the MC calculations. Conclusions: A new way of averaging LET for a proton beam is performed to incorporate the effects produced by the variation of stopping power of each individual proton along microscopic biological structures. An analytical model based on MC simulations allows for fast and accurate calculations of segment-averaged dose-averaged restricted LET for proton beams, which otherwise would need to be calculated from exhaustive MC simulations of clinical plans.Spanish Ministry of Economy and Competitiveness under Grant No. FPA2016-77689-C2-1-

Calculation of clinical dose distributions in proton therapy from microdosimetry

Purpose: To introduce a new algorithm—MicroCalc—for dose calculation by modeling microdosimetric energy depositions and the spectral fluence at each point of a particle beam. Proton beams are considered as a particular case of the general methodology. By comparing the results obtained against Monte Carlo computations, we aim to validate the microdosimetric formalism presented here and in previous works. Materials and methods: In previous works, we developed a function on the energy for the average energy imparted to a microdosimetric site per event and a model to compute the energetic spectrum at each point of the patient image. The number of events in a voxel is estimated assuming a model in which the voxel is completely filled by microdosimetric sites. Then, dose at every voxel is computed by integrating the average energy imparted per event multiplied by the number of events per energy beam of the spectral distribution within the voxel. Our method is compared with the proton convolution superposition (PCS) algorithm implemented in Eclipse™ and the fast Monte Carlo code MCsquare, which is here considered the benchmark, for in-water calculations, using in both cases clinically validated beam data. Two clinical cases are considered: a brain and a prostate case. Results: For a SOBP beam in water, the mean difference at the central axis found for MicroCalc is of 0.86% against 1.03% for PCS. Three-dimensional gamma analyses in the PTVs compared with MCsquare for criterion (3%, 3 mm) provide gamma index of 95.07% with MicroCalc vs 94.50% with PCS for the brain case and 99.90% vs 100.00%, respectively, for the prostate case. For selected organs at risk in each case (brainstem and rectum), mean and maximum difference with respect to MCsquare dose are analyzed. In the brainstem, mean differences are 0.25 Gy (MicroCalc) vs 0.56 Gy (PCS), whereas for the rectum, these values are 0.05 Gy (MicroCalc) vs 0.07 Gy (PCS). Conclusions: The accuracy of MicroCalc seems to be, at least, not inferior to PCS, showing similar or better agreement with MCsquare in the considered cases. Additionally, the algorithm enables simultaneous computation of other quantities of interest. These results seem to validate the microdosimetric methodology in which the algorithm is based on

Performance of a hybrid Monte Carlo-Pencil beam dose algorithm for proton therapy

PURPOSE Analytical algorithms have limited accuracy when modeling very heterogeneous tumor sites. This work addresses the performance of a hybrid dose optimizer that combines both Monte Carlo (MC) and pencil beam (PB) dose engines to get the best outcome for proton therapy plans in terms of speed and accuracy. MATERIALS AND METHODS The hybrid optimization strategy calculates the optimal spot weights (w) using the analytical beamlets matrix (PPB) and a correction term C. After a first optimization where C = 0, the method alternates optimization of w using PPB with updates of C = DMC – DPB, where DMC results from a regular MC computation and DPB = PPB * w. Updates of C can be triggered as often as necessary by calling the MC with the last corrected weights w as input. The hybrid method was applied to three cases (prostate, lung and brain) and compared with full MC-based plans (PMC). For simplicity, PTV-based plans were created but the method can be equally applied in robust optimization. The plans were created with our in-house treatment planning system (TPS), which is coupled with a PB algorithm and a super-fast MC. RESULTS The MC recomputed doses after initial optimization (C=0, before correction) showed important dose degradation for all patients, with D95 below the clinical constraint (D95 > 95% of dose prescription, Dp) and significant overdose (D5 > 105% Dp). The hybrid method was able to recover excellent target coverage and reduced overdose after only a single update of C. The computation time of hybrid plans was reduced by a factor 7 w.r.t the full MC-based plans. CONCLUSIONS This study demonstrated the successful performance of hybrid MC-PB optimization for proton therapy, which can be immediately considered as an option for improving the dose calculation accuracy of commercial analytical TPS

Performance of a hybrid Monte Carlo-Pencil beam dose algorithm for proton therapy

PURPOSE Analytical algorithms have limited accuracy when modeling very heterogeneous tumor sites. This work addresses the performance of a hybrid dose optimizer that combines both Monte Carlo (MC) and pencil beam (PB) dose engines to get the best outcome for proton therapy plans in terms of speed and accuracy. MATERIALS AND METHODS The hybrid optimization strategy calculates the optimal spot weights (w) using the analytical beamlets matrix (PPB) and a correction term C. After a first optimization where C = 0, the method alternates optimization of w using PPB with updates of C = DMC – DPB, where DMC results from a regular MC computation and DPB = PPB * w. Updates of C can be triggered as often as necessary by calling the MC with the last corrected weights w as input. The hybrid method was applied to three cases (prostate, lung and brain) and compared with full MC-based plans (PMC). For simplicity, PTV-based plans were created but the method can be equally applied in robust optimization. The plans were created with our in-house treatment planning system (TPS), which is coupled with a PB algorithm and a super-fast MC. RESULTS The MC recomputed doses after initial optimization (C=0, before correction) showed important dose degradation for all patients, with D95 below the clinical constraint (D95 > 95% of dose prescription, Dp) and significant overdose (D5 > 105% Dp). The hybrid method was able to recover excellent target coverage and reduced overdose after only a single update of C. The computation time of hybrid plans was reduced by a factor 7 w.r.t the full MC-based plans. CONCLUSIONS This study demonstrated the successful performance of hybrid MC-PB optimization for proton therapy, which can be immediately considered as an option for improving the dose calculation accuracy of commercial analytical TPS

LGM2605 Reduces Space Radiation-Induced NLRP3 Inflammasome Activation and Damage in In Vitro Lung Vascular Networks

Updated measurements of charged particle fluxes during the transit from Earth to Mars as well as on site measurements by Curiosity of Martian surface radiation fluxes identified potential health hazards associated with radiation exposure for human space missions. Designing mitigation strategies of radiation risks to astronauts is critical. We investigated radiation-induced endothelial cell damage and its mitigation by LGM2605, a radioprotector with antioxidant and free radical scavenging properties. We used an in vitro model of lung vascular networks (flow-adapted endothelial cells; FAECs), exposed to gamma rays, low/higher linear energy transfer (LET) protons (3–4 or 8–10 keV/µm, respectively), and mixed field radiation sources (gamma and protons), given at mission-relevant doses (0.25 gray (Gy)–1 Gy). We evaluated endothelial inflammatory phenotype, NLRP3 inflammasome activation, and oxidative cell injury. LGM2605 (100 µM) was added 30 min post radiation exposure and gene expression changes evaluated 24 h later. Radiation induced a robust increase in mRNA levels of antioxidant enzymes post 0.25 Gy and 0.5 Gy gamma radiation, which was significantly decreased by LGM2605. Intercellular cell adhesion molecule-1 (ICAM-1) and NOD-like receptor protein 3 (NLRP3) induction by individual or mixed-field exposures were also significantly blunted by LGM2605. We conclude that LGM2605 is a likely candidate to reduce tissue damage from space-relevant radiation exposure