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Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques

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A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 34041

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Special Issue Editors

Dr. Dimitris Emfietzoglou

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Guest Editor

Medical Physics Laboratory, Department of Medicine, University of Ioannina, 45110 Ioannina, Greece
Interests: Monte Carlo radiation transport; microdosimetry; track-structure; radiation physics, medical physics
Special Issues, Collections and Topics in MDPI journals

Dr. Sebastien Incerti

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Guest Editor

Bordeaux University, CNRS/IN2P3, CENBG, UMR 5797, 33170 Gradignan, France
Interests: Monte Carlo; Geant4-DNA; Geant4; radiation biophysics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ionizing radiation is often referred to as a double-edged sword capable of both inducing as well as curing cancer. Currently, one out of two cancer patients are treated by high doses of ionizing radiation in the form of photons (X-rays, γ-rays) or charged particles (electrons, protons, alpha, and heavier ions) while exposure to low doses in diagnostic examinations is also of concern due to the associated cancer risk. Despite continuous advances in tumor biology at the cellular and molecular level, the practice of radiation therapy largely follows a macroscopic, top-down approach relying, mainly, on empirical clinical experience. This compromises the application and efficacy of new and emerging radiotherapy modalities with beams or sources of ionizing radiations of very different physical characteristics, such as, for example, high-energy hadrons or Auger electrons. Similarly, cancer risk assessment in radiation protection is mostly based on epidemiological data which cover a limited range of dose levels and types of ionizing radiations. The debate over the radiation quality problem, that is, the different radiobiological effectiveness of ionizing radiations at the same level of delivered dose, which extends over the whole spectrum of energies and particles used in therapy and diagnosis, perhaps best reflects our limited understanding of the action of ionizing radiation in biological matter, especially at the microscopic scale of cells and subcellular structures. It is envisioned that mechanistically-inspired bottom-up approaches to treatment planning and risk assessment, will play a key role in supporting further progress in the use of and protection by ionizing radiation. In this context, advancements in the dosimetry of ionizing radiation at the scale of single cells down to the DNA level, along with the development of biophysical dose-response models and radiobiological techniques will offer valuable input.

Dr. Dimitris Emfietzoglou
Dr. Sebastien Incerti
Guest Editors

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Keywords

Radiation Dosimetry
Microdosimetry
Nanodosimetry
Monte Carlo simulation
Radiobiological techniques
Biophysical dose-response models
Radiation Cancer Risk

Published Papers (12 papers)

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Research

Jump to: Review

26 pages, 13909 KiB

Open AccessArticle

Estimate of the Biological Dose in Hadrontherapy Using GATE

by Yasmine Ali, Caterina Monini, Etienne Russeil, Jean Michel Létang, Etienne Testa, Lydia Maigne and Michael Beuve

Cancers 2022, 14(7), 1667; https://doi.org/10.3390/cancers14071667 - 25 Mar 2022

Cited by 4 | Viewed by 1877

Abstract

For the evaluation of the biological effects, Monte Carlo toolkits were used to provide an RBE-weighted dose using databases of survival fraction coefficients predicted through biophysical models. Biophysics models, such as the mMKM and NanOx models, have previously been developed to estimate a [...] Read more.

z_{1 D}^{*}

(Gy) and the dose at 10% D₁₀ for human salivary gland (HSG) cells using Monte Carlo Track Structure codes LPCHEM and Geant4-DNA, and compared these with data from the literature for monoenergetic ions. These two models were used to create databases of survival fraction coefficients for several ion types (hydrogen, carbon, helium and oxygen) and for energies ranging from 0.1 to 400 MeV/n. We calculated α values as a function of LET with the mMKM and the NanOx models, and compared these with the literature. In order to estimate the biological dose for SOBPs, these databases were used with a Monte Carlo toolkit. We considered GATE, an open-source software based on the GEANT4 Monte Carlo toolkit. We implemented a tool, the BioDoseActor, in GATE, using the mMKM and NanOx databases of cell survival predictions as input, to estimate, at a voxel scale, biological outcomes when treating a patient. We modeled the HIBMC 320 MeV/u carbon-ion beam line. We then tested the BioDoseActor for the estimation of biological dose, the relative biological effectiveness (RBE) and the cell survival fraction for the irradiation of the HSG cell line. We then tested the implementation for the prediction of cell survival fraction, RBE and biological dose for the HIBMC 320 MeV/u carbon-ion beamline. For the cell survival fraction, we obtained satisfying results. Concerning the prediction of the biological dose, a 10% relative difference between mMKM and NanOx was reported. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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14 pages, 18065 KiB

Open AccessArticle

Virtual Clinical Trials in 2D and 3D X-ray Breast Imaging and Dosimetry: Comparison of CPU-Based and GPU-Based Monte Carlo Codes

by Giovanni Mettivier, Antonio Sarno, Youfang Lai, Bruno Golosio, Viviana Fanti, Maria Elena Italiano, Xun Jia and Paolo Russo

Cancers 2022, 14(4), 1027; https://doi.org/10.3390/cancers14041027 - 17 Feb 2022

Cited by 9 | Viewed by 2322

Abstract

Computational reproductions of medical imaging tests, a form of virtual clinical trials (VCTs), are increasingly being used, particularly in breast imaging research. The accuracy of the computational platform that is used for the imaging and dosimetry simulation processes is a fundamental requirement. Moreover, for practical usage, the imaging simulation computation time should be compatible with the clinical workflow. We compared three different platforms for in-silico X-ray 3D breast imaging: the Agata (University & INFN Napoli) that was based on the Geant4 toolkit and running on a CPU-based server architecture; the XRMC Monte Carlo (University of Cagliari) that was based on the use of variance reduction techniques, running on a CPU hardware; and the Monte Carlo code gCTD (University of Texas Southwestern Medical Center) running on a single GPU platform with CUDA environment. The tests simulated the irradiation of cylindrical objects as well as anthropomorphic breast phantoms and produced 2D and 3D images and 3D maps of absorbed dose. All the codes showed compatible results in terms of simulated dose maps and imaging values within a maximum discrepancy of 3%. The GPU-based code produced a reduction of the computation time up to factor 10⁴, and so permits real-time VCT studies for X-ray breast imaging. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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16 pages, 2375 KiB

Open AccessArticle

Evaluating Iodine-125 DNA Damage Benchmarks of Monte Carlo DNA Damage Models

by Shannon J. Thompson, Aoife Rooney, Kevin M. Prise and Stephen J. McMahon

Cancers 2022, 14(3), 463; https://doi.org/10.3390/cancers14030463 - 18 Jan 2022

Cited by 5 | Viewed by 1666

Abstract

A wide range of Monte Carlo models have been applied to predict yields of DNA damage based on nanoscale track structure calculations. While often similar on the macroscopic scale, these models frequently employ different assumptions which lead to significant differences in nanoscale dose deposition. However, the impact of these differences on key biological readouts remains unclear. A major challenge in this area is the lack of robust datasets which can be used to benchmark models, due to a lack of resolution at the base pair level required to deeply test nanoscale dose deposition. Studies investigating the distribution of strand breakage in short DNA strands following the decay of incorporated ¹²⁵I offer one of the few benchmarks for model predictions on this scale. In this work, we have used TOPAS-nBio to evaluate the performance of three Geant4-DNA physics models at predicting the distribution and yield of strand breaks in this irradiation scenario. For each model, energy and OH radical distributions were simulated and used to generate predictions of strand breakage, varying energy thresholds for strand breakage and OH interaction rates to fit to the experimental data. All three models could fit well to the observed data, although the best-fitting strand break energy thresholds ranged from 29.5 to 32.5 eV, significantly higher than previous studies. However, despite well describing the resulting DNA fragment distribution, these fit models differed significantly with other endpoints, such as the total yield of breaks, which varied by 70%. Limitations in the underlying data due to inherent normalisation mean it is not possible to distinguish clearly between the models in terms of total yield. This suggests that, while these physics models can effectively fit some biological data, they may not always generalise in the same way to other endpoints, requiring caution in their extrapolation to new systems and the use of multiple different data sources for robust model benchmarking. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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13 pages, 1837 KiB

Open AccessArticle

Comparison and Evaluation of Different Radiotherapy Techniques Using Biodosimetry Based on Cytogenetics

by Aggeliki Nikolakopoulou, Vasiliki Peppa, Antigoni Alexiou, George Pissakas, Georgia Terzoudi and Pantelis Karaiskos

Cancers 2022, 14(1), 146; https://doi.org/10.3390/cancers14010146 - 29 Dec 2021

Cited by 1 | Viewed by 1733

Abstract

While rapid technological advances in radiotherapy techniques have led to a more precise delivery of radiation dose and to a decreased risk of side effects, there is still a need to evaluate the efficacy of the new techniques estimating the biological dose and to investigate the radiobiological impact of the protracted radiotherapy treatment duration. The aim of this study is to compare, at a cytogenetic level, advanced radiotherapy techniques VMAT and IMRT with the conventional 3D-CRT, using biological dosimetry. A dicentric biodosimetry assay based on the frequency of dicentrics chromosomes scored in peripheral blood lymphocytes from prostate cancer patients and PC3 human prostate cancer cell line was used. For each patient blood sample and each subpopulation of the cultured cell line, three different irradiations were performed using the 3D-CRT, IMRT, and VMAT technique. The absorbed dose was estimated with the biodosimetry method based on the induced dicentric chromosomes. The results showed a statistically significant underestimation of the biological absorbed dose of ~6% for the IMRT and VMAT compared to 3D-CRT irradiations for peripheral blood lymphocytes, whereas IMRT and VMAT results were comparable without a statistically significant difference, although slightly lower values were observed for VMAT compared to IMRT irradiation. Similar results were obtained using the PC3 cell line. The observed biological dose underestimation could be associated with the relative decreased dose rate and increase irradiation time met in modulated techniques compared to the conventional 3D-CRT irradiations. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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16 pages, 720 KiB

Open AccessArticle

Performance Evaluation for Repair of HSGc-C5 Carcinoma Cell Using Geant4-DNA

by Dousatsu Sakata, Masao Suzuki, Ryoichi Hirayama, Yasushi Abe, Masayuki Muramatsu, Shinji Sato, Oleg Belov, Ioanna Kyriakou, Dimitris Emfietzoglou, Susanna Guatelli, Sebastien Incerti and Taku Inaniwa

Cancers 2021, 13(23), 6046; https://doi.org/10.3390/cancers13236046 - 30 Nov 2021

Cited by 6 | Viewed by 2540

Abstract

Track-structure Monte Carlo simulations are useful tools to evaluate initial DNA damage induced by irradiation. In the previous study, we have developed a Gean4-DNA-based application to estimate the cell surviving fraction of V79 cells after irradiation, bridging the gap between the initial DNA damage and the DNA rejoining kinetics by means of the two-lesion kinetics (TLK) model. However, since the DNA repair performance depends on cell line, the same model parameters cannot be used for different cell lines. Thus, we extended the Geant4-DNA application with a TLK model for the evaluation of DNA damage repair performance in HSGc-C5 carcinoma cells which are typically used for evaluating proton/carbon radiation treatment effects. For this evaluation, we also performed experimental measurements for cell surviving fractions and DNA rejoining kinetics of the HSGc-C5 cells irradiated by 70 MeV protons at the cyclotron facility at the National Institutes for Quantum and Radiological Science and Technology (QST). Concerning fast- and slow-DNA rejoining, the TLK model parameters were adequately optimized with the simulated initial DNA damage. The optimized DNA rejoining speeds were reasonably agreed with the experimental DNA rejoining speeds. Using the optimized TLK model, the Geant4-DNA simulation is now able to predict cell survival and DNA-rejoining kinetics for HSGc-C5 cells. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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30 pages, 26376 KiB

Open AccessArticle

Application of High-Z Gold Nanoparticles in Targeted Cancer Radiotherapy—Pharmacokinetic Modeling, Monte Carlo Simulation and Radiobiological Effect Modeling

by Wei Bo Li, Stefan Stangl, Alexander Klapproth, Maxim Shevtsov, Alicia Hernandez, Melanie A. Kimm, Jan Schuemann, Rui Qiu, Bernhard Michalke, Mario A. Bernal, Junli Li, Kerstin Hürkamp, Yibao Zhang and Gabriele Multhoff

Cancers 2021, 13(21), 5370; https://doi.org/10.3390/cancers13215370 - 26 Oct 2021

Cited by 9 | Viewed by 3206

Abstract

High-Z gold nanoparticles (AuNPs) conjugated to a targeting antibody can help to improve tumor control in radiotherapy while simultaneously minimizing radiotoxicity to adjacent healthy tissue. This paper summarizes the main findings of a joint research program which applied AuNP-conjugates in preclinical modeling of radiotherapy at the Klinikum rechts der Isar, Technical University of Munich and Helmholtz Zentrum München. A pharmacokinetic model of superparamagnetic iron oxide nanoparticles was developed in preparation for a model simulating the uptake and distribution of AuNPs in mice. Multi-scale Monte Carlo simulations were performed on a single AuNP and multiple AuNPs in tumor cells at cellular and molecular levels to determine enhancements in the radiation dose and generation of chemical radicals in close proximity to AuNPs. A biologically based mathematical model was developed to predict the biological response of AuNPs in radiation enhancement. Although simulations of a single AuNP demonstrated a clear dose enhancement, simulations relating to the generation of chemical radicals and the induction of DNA strand breaks induced by multiple AuNPs showed only a minor dose enhancement. The differences in the simulated enhancements at molecular and cellular levels indicate that further investigations are necessary to better understand the impact of the physical, chemical, and biological parameters in preclinical experimental settings prior to a translation of these AuNPs models into targeted cancer radiotherapy. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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14 pages, 2691 KiB

Open AccessArticle

Standardization and Validation of Brachytherapy Seeds’ Modelling Using GATE and GGEMS Monte Carlo Toolkits

by Konstantinos P. Chatzipapas, Dimitris Plachouris, Panagiotis Papadimitroulas, Konstantinos A. Mountris, Julien Bert, Dimitris Visvikis, Dimitris Mihailidis and George C. Kagadis

Cancers 2021, 13(21), 5315; https://doi.org/10.3390/cancers13215315 - 22 Oct 2021

Cited by 2 | Viewed by 2312

Abstract

This study aims to validate GATE and GGEMS simulation toolkits for brachytherapy applications and to provide accurate models for six commercial brachytherapy seeds, which will be freely available for research purposes. The AAPM TG-43 guidelines were used for the validation of two Low Dose Rate (LDR), three High Dose Rate (HDR), and one Pulsed Dose Rate (PDR) brachytherapy seeds. Each seed was represented as a 3D model and then simulated in GATE to produce one single Phase-Space (PHSP) per seed. To test the validity of the simulations’ outcome, referenced data (provided by the TG-43) was compared with GATE results. Next, validation of the GGEMS toolkit was achieved by comparing its outcome with the GATE MC simulations, incorporating clinical data. The simulation outcomes on the radial dose function (RDF), anisotropy function (AF), and dose rate constant (DRC) for the six commercial seeds were compared with TG-43 values. The statistical uncertainty was limited to 1% for RDF, to 6% (maximum) for AF, and to 2.7% (maximum) for the DRC. GGEMS provided a good agreement with GATE when compared in different situations: (a) Homogeneous water sphere, (b) heterogeneous CT phantom, and (c) a realistic clinical case. In addition, GGEMS has the advantage of very fast simulations. For the clinical case, where TG-186 guidelines were considered, GATE required 1 h for the simulation while GGEMS needed 162 s to reach the same statistical uncertainty. This study produced accurate models and simulations of their emitted spectrum of commonly used commercial brachytherapy seeds which are freely available to the scientific community. Furthermore, GGEMS was validated as an MC GPU based tool for brachytherapy. More research is deemed necessary for the expansion of brachytherapy seed modeling. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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16 pages, 4189 KiB

Open AccessArticle

A Geant4-DNA Evaluation of Radiation-Induced DNA Damage on a Human Fibroblast

by Wook-Geun Shin, Dousatsu Sakata, Nathanael Lampe, Oleg Belov, Ngoc Hoang Tran, Ivan Petrovic, Aleksandra Ristic-Fira, Milos Dordevic, Mario A. Bernal, Marie-Claude Bordage, Ziad Francis, Ioanna Kyriakou, Yann Perrot, Takashi Sasaki, Carmen Villagrasa, Susanna Guatelli, Vincent Breton, Dimitris Emfietzoglou and Sebastien Incerti

Cancers 2021, 13(19), 4940; https://doi.org/10.3390/cancers13194940 - 30 Sep 2021

Cited by 14 | Viewed by 3854

Abstract

Accurately modeling the radiobiological mechanisms responsible for the induction of DNA damage remains a major scientific challenge, particularly for understanding the effects of low doses of ionizing radiation on living beings, such as the induction of carcinogenesis. A computational approach based on the Monte Carlo technique to simulate track structures in a biological medium is currently the most reliable method for calculating the early effects induced by ionizing radiation on DNA, the primary cellular target of such effects. The Geant4-DNA Monte Carlo toolkit can simulate not only the physical, but also the physico-chemical and chemical stages of water radiolysis. These stages can be combined with simplified geometric models of biological targets, such as DNA, to assess direct and indirect early DNA damage. In this study, DNA damage induced in a human fibroblast cell was evaluated using Geant4-DNA as a function of incident particle type (gammas, protons, and alphas) and energy. The resulting double-strand break yields as a function of linear energy transfer closely reproduced recent experimental data. Other quantities, such as fragment length distribution, scavengeable damage fraction, and time evolution of damage within an analytical repair model also supported the plausibility of predicting DNA damage using Geant4-DNA.The complete simulation chain application “molecularDNA”, an example for users of Geant4-DNA, will soon be distributed through Geant4. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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23 pages, 728 KiB

Open AccessArticle

Biological Impact of Target Fragments on Proton Treatment Plans: An Analysis Based on the Current Cross-Section Data and a Full Mixed Field Approach

by Elettra Valentina Bellinzona, Leszek Grzanka, Andrea Attili, Francesco Tommasino, Thomas Friedrich, Michael Krämer, Michael Scholz, Giuseppe Battistoni, Alessia Embriaco, Davide Chiappara, Giuseppe A. P. Cirrone, Giada Petringa, Marco Durante and Emanuele Scifoni

Cancers 2021, 13(19), 4768; https://doi.org/10.3390/cancers13194768 - 24 Sep 2021

Cited by 7 | Viewed by 2231

Abstract

Clinical routine in proton therapy currently neglects the radiobiological impact of nuclear target fragments generated by proton beams. This is partially due to the difficult characterization of the irradiation field. The detection of low energetic fragments, secondary protons and fragments, is in fact challenging due to their very short range. However, considering their low residual energy and therefore high LET, the possible contribution of such heavy particles to the overall biological effect could be not negligible. In this context, we performed a systematic analysis aimed at an explicit assessment of the RBE (relative biological effectiveness, i.e., the ratio of photon to proton physical dose needed to achieve the same biological effect) contribution of target fragments in the biological dose calculations of proton fields. The TOPAS Monte Carlo code has been used to characterize the radiation field, i.e., for the scoring of primary protons and fragments in an exemplary water target. TRiP98, in combination with LEM IV RBE tables, was then employed to evaluate the RBE with a mixed field approach accounting for fragments’ contributions. The results were compared with that obtained by considering only primary protons for the pristine beam and spread out Bragg peak (SOBP) irradiations, in order to estimate the relative weight of target fragments to the overall RBE. A sensitivity analysis of the secondary particles production cross-sections to the biological dose has been also carried out in this study. Finally, our modeling approach was applied to the analysis of a selection of cell survival and RBE data extracted from published in vitro studies. Our results indicate that, for high energy proton beams, the main contribution to the biological effect due to the secondary particles can be attributed to secondary protons, while the contribution of heavier fragments is mainly due to helium. The impact of target fragments on the biological dose is maximized in the entrance channels and for small

α / β

values. When applied to the description of survival data, model predictions including all fragments allowed better agreement to experimental data at high energies, while a minor effect was observed in the peak region. An improved description was also obtained when including the fragments’ contribution to describe RBE data. Overall, this analysis indicates that a minor contribution can be expected to the overall RBE resulting from target fragments. However, considering the fragmentation effects can improve the agreement with experimental data for high energy proton beams. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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13 pages, 3071 KiB

Open AccessArticle

On the Equivalence of the Biological Effect Induced by Irradiation of Clusters of Heavy Atom Nanoparticles and Homogeneous Heavy Atom-Water Mixtures

by Balder Villagomez-Bernabe, José Ramos-Méndez and Frederick J. Currell

Cancers 2021, 13(9), 2034; https://doi.org/10.3390/cancers13092034 - 23 Apr 2021

Cited by 4 | Viewed by 1562

Abstract

A multiscale local effect model (LEM)-based framework was implemented to study the cell damage caused by the irradiation of clusters of gold nanoparticles (GNPs) under clinically relevant conditions. The results were compared with those obtained by a homogeneous mixture of water and gold (MixNP) irradiated under similar conditions. To that end, Monte Carlo simulations were performed for the irradiation of GNP clusters of different sizes and MixNPs with a 6 MV Linac spectrum to calculate the dose enhancement factor in water. The capabilities of our framework for the prediction of cell damage trends are examined and discussed. We found that the difference of the main parameter driving the cell damage between a cluster of GNPs and the MixNP was less than 1.6% for all cluster sizes. Our results demonstrate for the first time a simple route to intuit the radiobiological effects of clusters of nanoparticles through the consideration of an equivalent homogenous gold/water mixture. Furthermore, the negligible difference on cell damage between a cluster of GNPs and MixNP simplifies the modelling for the complex geometries of nanoparticle aggregations and saves computational resources. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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12 pages, 2703 KiB

Open AccessArticle

A Monte Carlo Determination of Dose and Range Uncertainties for Preclinical Studies with a Proton Beam

by Arthur Bongrand, Charbel Koumeir, Daphnée Villoing, Arnaud Guertin, Ferid Haddad, Vincent Métivier, Freddy Poirier, Vincent Potiron, Noël Servagent, Stéphane Supiot, Grégory Delpon and Sophie Chiavassa

Cancers 2021, 13(8), 1889; https://doi.org/10.3390/cancers13081889 - 15 Apr 2021

Cited by 6 | Viewed by 2511

Abstract

Proton therapy (PRT) is an irradiation technique that aims at limiting normal tissue damage while maintaining the tumor response. To study its specificities, the ARRONAX cyclotron is currently developing a preclinical structure compatible with biological experiments. A prerequisite is to identify and control uncertainties on the ARRONAX beamline, which can lead to significant biases in the observed biological results and dose–response relationships, as for any facility. This paper summarizes and quantifies the impact of uncertainty on proton range, absorbed dose, and dose homogeneity in a preclinical context of cell or small animal irradiation on the Bragg curve, using Monte Carlo simulations. All possible sources of uncertainty were investigated and discussed independently. Those with a significant impact were identified, and protocols were established to reduce their consequences. Overall, the uncertainties evaluated were similar to those from clinical practice and are considered compatible with the performance of radiobiological experiments, as well as the study of dose–response relationships on this proton beam. Another conclusion of this study is that Monte Carlo simulations can be used to help build preclinical lines in other setups. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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Review

Jump to: Research

26 pages, 3988 KiB

Open AccessReview

Review of the Geant4-DNA Simulation Toolkit for Radiobiological Applications at the Cellular and DNA Level

by Ioanna Kyriakou, Dousatsu Sakata, Hoang Ngoc Tran, Yann Perrot, Wook-Geun Shin, Nathanael Lampe, Sara Zein, Marie Claude Bordage, Susanna Guatelli, Carmen Villagrasa, Dimitris Emfietzoglou and Sébastien Incerti

Cancers 2022, 14(1), 35; https://doi.org/10.3390/cancers14010035 - 22 Dec 2021

Cited by 44 | Viewed by 5694

Abstract

The Geant4-DNA low energy extension of the Geant4 Monte Carlo (MC) toolkit is a continuously evolving MC simulation code permitting mechanistic studies of cellular radiobiological effects. Geant4-DNA considers the physical, chemical, and biological stages of the action of ionizing radiation (in the form of x- and γ-ray photons, electrons and β^±-rays, hadrons, α-particles, and a set of heavier ions) in living cells towards a variety of applications ranging from predicting radiotherapy outcomes to radiation protection both on earth and in space. In this work, we provide a brief, yet concise, overview of the progress that has been achieved so far concerning the different physical, physicochemical, chemical, and biological models implemented into Geant4-DNA, highlighting the latest developments. Specifically, the “dnadamage1” and “molecularDNA” applications which enable, for the first time within an open-source platform, quantitative predictions of early DNA damage in terms of single-strand-breaks (SSBs), double-strand-breaks (DSBs), and more complex clustered lesions for different DNA structures ranging from the nucleotide level to the entire genome. These developments are critically presented and discussed along with key benchmarking results. The Geant4-DNA toolkit, through its different set of models and functionalities, offers unique capabilities for elucidating the problem of radiation quality or the relative biological effectiveness (RBE) of different ionizing radiations which underlines nearly the whole spectrum of radiotherapeutic modalities, from external high-energy hadron beams to internal low-energy gamma and beta emitters that are used in brachytherapy sources and radiopharmaceuticals, respectively. Full article

(This article belongs to the Special Issue Ionizing Radiation in Therapy and Biology of Cancer: Role of Monte Carlo simulations, Biophysical Modeling, and Radiobiological Techniques)

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