Our understanding of radiation-induced cellular damage has greatly improved over the past few decades. Despite this progress, there are still many obstacles to fully understand how radiation interacts with biologically relevant cellular components, such as DNA, to cause observable end points such as cell killing. Damage in DNA is identified as a major route of cell killing. One hurdle when modeling biological effects is the difficulty in directly comparing results generated by members of different research groups. Multiple Monte Carlo codes have been developed to simulate damage induction at the DNA scale, while at the same time various groups have developed models that describe DNA repair processes with varying levels of detail. These repair models are intrinsically linked to the damage model employed in their development, making it difficult to disentangle systematic effects in either part of the modeling chain. These modeling chains typically consist of track-structure Monte Carlo simulations of the physical interactions creating direct damages to DNA, followed by simulations of the production and initial reactions of chemical species causing so-called “indirect” damages. After the induction of DNA damage, DNA repair models combine the simulated damage patterns with biological models to determine the biological consequences of the damage. To date, the effect of the environment, such as molecular oxygen (normoxic vs. hypoxic), has been poorly considered. We propose a new standard DNA damage (SDD) data format to unify the interface between the simulation of damage induction in DNA and the biological modeling of DNA repair processes, and introduce the effect of the environment (molecular oxygen or other compounds) as a flexible parameter. Such a standard greatly facilitates inter-model comparisons, providing an ideal environment to tease out model assumptions and identify persistent, underlying mechanisms. Through inter-model comparisons, this unified standard has the potential to greatly advance our under-standing of the underlying mechanisms of radiation-induced DNA damage and the resulting observable biological effects when radiation parameters and/or environmental conditions change.

Proton and ion beams are radiotherapy modalities of increasing importance and interest. Because of the different biological dose response of these radiations as compared with high-energy photon beams, the current approach of treatment prescription is based on the product of the absorbed dose to water and a biological weighting factor, but this is found to be insufficient for providing a generic method to quantify the biological outcome of radiation. It is therefore suggested to define new dosimetric quantities that allow a transparent separation of the physical processes from the biological ones. Given the complexity of the initiation and occurrence of biological processes on various time and length scales, and given that neither microdosimetry nor nanodosimetry on their own can fully describe the biological effects as a function of the distribution of energy deposition or ionization, a multiscale approach is needed to lay the foundation for the aforementioned new physical quantities relating track structure to relative biological effectiveness in proton and ion beam therapy. This article reviews the state-of-the-art microdosimetry, nanodosimetry, track structure simulations, quantification of reactive species, reference radiobiological data, cross-section data and multiscale models of biological response in the context of realizing the new quantities. It also introduces the European metrology project, Biologically Weighted Quantities in Radiotherapy, which aims to investigate the feasibility of establishing a multiscale model as the basis of the new quantities. A tentative generic expression of how the weighting of physical quantities at different length scales could be carried out is presented.

Rabus, H. (2013). Ionization cross section data of nitrogen, methane, and propane for light ions and electrons and their suitability for use in track structure simulations. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 88 (4), 043308-1-043308-21.Ionization cross section data of nitrogen, methane, and propane for light ions and electrons and their suitability for use in track structure simulations AbstractTrack structure Monte Carlo simulations are frequently applied in micro-and nanodosimetry to calculate the radiation transport in detail. The use of a well-validated set of cross section data in such simulation codes ensures accurate calculations of transport parameters, such as ionization yields. These cross section data are, however, scarce and often discrepant when measured by different groups. This work surveys literature data on ionization and charge-transfer cross sections of nitrogen, methane, and propane for electrons, protons, and helium particles, focusing on the energy range between 100 keV and 20 MeV. Based on the evaluated data, different models for the parametrization of the cross section data are implemented in the code PTRA, developed for simulating proton and alpha particle transport in an ion-counting nanodosimeter. The suitability of the cross section data is investigated by comparing the calculated mean ionization cluster size and energy loss with experimental results in either nitrogen or propane. For protons, generally good agreement between measured and simulated data is found when the Rudd model is used in PTRA. For alpha particles, however, a considerable influence of different parametrizations of cross sections for ionization and charge transfer is observed. The PTRA code using the charge-transfer data is, nevertheless, successfully benchmarked by the experimental data for the calculation of nanodosimetric quantities, but remaining discrepancies still have to be further investigated (up to 13% lower energy loss and 19% lower mean ionization cluster size than in the experiment). A continuation of this work should investigate data for the energy loss per interaction as well as differential cross section data of nitrogen and propane. Interpolation models for ionization and chargetransfer data are proposed. The Barkas model, frequently used for a determination of the effective charge in the ionization cross section, significantly underestimates both the energy loss (by up to 19%) and the mean ionization cluster size (up to 65%) for alpha particles. It is, therefore, not recommended for particle-track simulations. Track structure Monte Carlo simulations are frequently applied in micro-and nanodosimetry to calculate the radiation transport in detail. The use of a well-validated set of cross section data in such simulation codes ensures accurate calculations of transport parameters, such as ionization yields. These cross section data are, however, scarce and often discrepant when measured by different groups. This work surveys literature data on ionization and charge-tra...

Microbeam radiation therapy (MRT) is a new oncology method currently under development for the treatment of inoperable pediatric brain tumors. Monte Carlo simulation, or the computational study of radiation transport in matter, is often used in radiotherapy to theoretically estimate the dose required for treatment. However, its potential use in MRT dose planning systems is currently hindered by the significant discrepancies that have been observed between measured and theoretical dose and the PVDR (peak to valley dose ratio). The need to resolve these discrepancies is driven by the desirability of making MRT available to humans in the next few years. This article aims to resolve some of the discrepancies by examining the simplifications adopted in previous MRT Monte Carlo studies, such as the common practice of commencing microbeam transport on the surface of the target which neglects the influence of the distributed synchrotron source, multislit collimator, and the beam divergence between them. This article uses PENELOPE Monte Carlo simulation to investigate the influence of these beamline components upstream of the target on the lateral dose profiles and PVDRs of an array of 25 microbeams. It also compares the dose profiles and PVDRs of a microbeam array produced from a single simulation (full array) to those produced from the superposition of a single microbeam profile (sup array). The effect of modeling the distributed source and the beam divergence was an increase in the absorbed dose in the penumbral and valley regions of the microbeam profiles. Inclusion of the multislit collimator resulted in differences of up to 5 microm in the FWHM of microbeam profiles across the array, which led to minor variations in the corresponding PVDR yields.

Nanoparticles (NPs) containing high atomic number (high-Z) materials have been shown to enhance the radiobiological effectiveness of ionizing radiation. This effect is often attributed to an enhancement of the absorbed dose in the vicinity of the NPs, based on Monte Carlo simulations that show a significant local enhancement of the energy deposition on the microscopic scale. The results of such simulations may be significantly biased and lead to a severe overestimation of the dose enhancement if the condition of secondary particle equilibrium is not met in the simulation setup. This current work shows an approach to estimate a ‘realistic’ dose enhancement from the results of such biased simulations which is based on published photon interaction data and provides a way for correcting biased results.