A Practical Primer on Particle Therapy.

PURPOSE: Particle therapy is a promising treatment technique that is becoming more commonly used. Although proton beam therapy remains the most commonly used particle therapy, multiple other heavier ions have been used in the preclinical and clinical settings, each with its own unique properties. This practical review aims to summarize the differences between the studied particles, discussing their radiobiological and physical properties with additional review of the available clinical data. METHODS AND MATERIALS: A search was carried out on the PubMed databases with search terms related to each particle. Relevant radiobiology, physics, and clinical studies were included. The articles were summarized to provide a practical resource for practicing clinicians. RESULTS: A total of 113 articles and texts were included in our narrative review. Currently, proton beam therapy has the most data and is the most widely used, followed by carbon, helium, and neutrons. Although oxygen, neon, silicon, and argon have been used clinically, their future use will likely remain limited as monotherapy. CONCLUSIONS: This review summarizes the properties of each of the clinically relevant particles. Protons, helium, and carbon will likely remain the most commonly used, although multi-ion therapy is an emerging technique.

GPU-accelerated calculation of proton microdosimetric spectra as a function of target size, proton energy, and bounding volume size.

Objective.Shortcomings of dose-averaged linear energy transfer (LETD), the quantity which is most commonly used to quantify proton relative biological effectiveness, have long been recognized. Microdosimetric spectra may overcome the limitations of LETDbut are extremely computationally demanding to calculate. A systematic library of lineal energy spectra for monoenergetic protons could enable rapid determination of microdosimetric spectra in a clinical environment. The objective of this work was to calculate and validate such a library of lineal energy spectra.Approach. SuperTrack, a GPU-accelerated CUDA/C++ based application, was developed to superimpose tracks calculated using Geant4 onto targets of interest and to compute microdosimetric spectra. Lineal energy spectra of protons with energies from 0.1 to 100 MeV were determined in spherical targets of diameters from 1 nm to 10µm and in bounding voxels with side lengths of 5µm and 3 mm.Main results.Compared to an analogous Geant4-based application, SuperTrack is up to 3500 times more computationally efficient if each track is resampled 1000 times. Dose spectra of lineal energy and dose-mean lineal energy calculated with SuperTrack were consistent with values published in the literature and with comparison to a Geant4 simulation. Using SuperTrack, we developed the largest known library of proton microdosimetric spectra as a function of primary proton energy, target size, and bounding volume size.Significance. SuperTrack greatly increases the computational efficiency of the calculation of microdosimetric spectra. The elevated lineal energy observed in a 3 mm side length bounding volume suggests that lineal energy spectra determined experimentally or computed in small bounding volumes may not be representative of the lineal energy spectra in voxels of a dose calculation grid. The library of lineal energy spectra calculated in this work could be integrated with a treatment planning system for rapid determination of lineal energy spectra in patient geometries.

Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning.

The purpose of this work is to assess the robustness of treatment plans when spot delivery errors were predicted with a machine learning (ML) model for intensity modulated proton therapy (IMPT). Over 6000 machine log files from delivered IMPT treatment plans were included in this study. From these log files, over 4.1  × $ \times \ $ 106 delivered proton spots were used to train the ML model. The presented model was tested and used to predict the spot position as well as the monitor units (MU) per spot, based on the original planning parameters. Two patient plans (one accelerated partial breast irradiation [APBI] and one ependymoma) were recalculated with the predicted spot position/MUs by the ML model and then were re-analyzed for robustness. Plans with ML predicted spots were less robust than the original clinical plans. In the APBI plan, dosimetric changes to the left lung and heart were not clinically relevant. In the ependymoma plan, the hot spot in the brainstem decreased and the hot spot in the cervical cord increased. Despite these differences, after robustness analysis, both ML spot delivery error plans resulted in >95% of the CTV receiving >95% of the prescription dose. The presented workflow has the potential benefit of including realistic spots information for plan quality checks in IMPT. This work demonstrates that in the two example plans, the plans were still robust when accounting for spot delivery errors as predicted by the ML model.

Comparing 2 Monte Carlo Systems in Use for Proton Therapy Research.

PURPOSE: Several Monte Carlo transport codes are available for medical physics users. To ensure confidence in the accuracy of the codes, they must be continually cross-validated. This study provides comparisons between MC2 and Tool for Particle Simulation (TOPAS) simulations, that is, between medical physics applications for Monte Carlo N-Particle Transport Code (MCNPX) and Geant4. MATERIALS AND METHODS: Monte Carlo simulations were repeated with 2 wrapper codes: TOPAS (based on Geant4) and MC2 (based on MCNPX). Simulations increased in geometrical complexity from a monoenergetic beam incident on a water phantom, to a monoenergetic beam incident on a water phantom with a bone or tissue slab at various depths, to a spread-out Bragg peak incident on a voxelized computed tomography (CT) geometry. The CT geometry cases consisted of head and neck tissue and lung tissue. The results of the simulations were compared with one another through dose or energy deposition profiles, r 90 calculations, and γ-analyses. RESULTS: Both codes gave very similar results with monoenergetic beams incident on a water phantom. Systematic differences were observed between MC2 and TOPAS simulations when using a lung or bone slab in a water phantom, particularly in the r 90 values, where TOPAS consistently calculated r 90 to be deeper by about 0.4%. When comparing the performance of the 2 codes in a CT geometry, the results were still very similar, exemplified by a 3-dimensional γ-analysis pass rate > 95% at the 2%-2-mm criterion for tissues from both head and neck and lung. CONCLUSION: Differences between TOPAS and MC2 were minor and were not considered clinically relevant.

Using the Proton Energy Spectrum and Microdosimetry to Model Proton Relative Biological Effectiveness.

PURPOSE: We introduce a methodology to calculate the microdosimetric quantity dose-mean lineal energy for input into the microdosimetric kinetic model (MKM) to model the relative biological effectiveness (RBE) of proton irradiation experiments. METHODS AND MATERIALS: The data from 7 individual proton RBE experiments were included in this study. In each experiment, the RBE at several points along the Bragg curve was measured. Monte Carlo simulations to calculate the lineal energy probability density function of 172 different proton energies were carried out with use of Geant4 DNA. We calculated the fluence-weighted lineal energy probability density function (fw(y)), based on the proton energy spectra calculated through Monte Carlo at each experimental depth, calculated the dose-mean lineal energy yD¯ for input into the MKM, and then computed the RBE. The radius of the domain (rd) was varied to reach the best agreement between the MKM-predicted RBE and experimental RBE. A generic RBE model as a function of dose-averaged linear energy transfer (LETD) with 1 fitting parameter was presented and fit to the experimental RBE data as well to facilitate a comparison to the MKM. RESULTS: Both the MKM and LETD-based models modeled the RBE from experiments well. Values for rd were similar to those of other cell lines under proton irradiation that were modeled with the MKM. Analysis of the performance of each model revealed that neither model was clearly superior to the other. CONCLUSIONS: Our 3 key accomplishments include the following: (1) We developed a method that uses the proton energy spectra and lineal energy distributions of those protons to calculate dose-mean lineal energy. (2) We demonstrated that our application of the MKM provides theoretical validation of proton irradiation experiments that show that RBE is significantly greater than 1.1. (3) We showed that there is no clear evidence that the MKM is better than LETD-based RBE models.

A model for relative biological effectiveness of therapeutic proton beams based on a global fit of cell survival data.

We introduce an approach for global fitting of the recently published high-throughput and high accuracy clonogenic cell-survival data for therapeutic scanned proton beams. Our fitting procedure accounts for the correlation between the cell-survival, the absorbed (physical) dose and the proton linear energy transfer (LET). The fitting polynomials and constraints have been constructed upon generalization of the microdosimetric kinetic model (gMKM) adapted to account for the low energy and high lineal-energy spectrum of the beam where the current radiobiological models may underestimate the reported relative biological effectiveness (RBE). The parameters (α, ß) of the linear-quadratic (LQ) model calculated by the presented method reveal a smooth transition from low to high LETs which is an advantage of the current method over methods previously employed to fit the same clonogenic data. Finally, the presented approach provides insight into underlying microscopic mechanisms which, with future study, may help to elucidate radiobiological responses along the Bragg curve and resolve discrepancies between experimental data and current RBE models.