Assessment of the Dosimetric Index from IMRT and Rapid arc Plan for Oropharyngeal Cancer with Simultaneous Integrated Boost (SIB) Technique in Combination with EUD-based NTCP and TCP Radiobiological Models.

PURPOSE: The current research compared radiobiological and dosimetric results for simultaneous integrated boost (SIB) plans employing RapidArc and IMRT planning procedures in oropharyngeal cancer from head-and-neck cancer (HNC) patients. MATERIALS AND METHODS: The indigenously developed Python-based software was used in this study for generation and analysis. Twelve patients with forty-eight total plans with SIB were planned using Rapid arc (2 and 3 arcs) and IMRT (7 and 9 fields) and compared with radiobiological models Lyman, Kutcher, Burman (LKB) and EUD (Equivalent Uniform Dose) along with physical index such as homogeneity index(HI), conformity index(CI) of target volumes. RESULTS: These models' inputs are the dose-volume histograms (DVHs) calculated by the treatment planning system (TPS). The values obtained vary from one model to the other for the same technique and patient. The maximum dose to the brainstem and spinal cord and the mean dose to the parotids were analysed both dosimetrically and radiobiologically, such as the LKB model effective volume, equivalent uniform dose, EUD-based normal tissue complication probability, and normal tissue integral dose. The mean and max dose to target volume with conformity, homogeneity index, tumor control probability compared with treatment times, and monitor units. CONCLUSION: Rapid arc (3 arcs) resulted in significantly better OAR sparing, dose homogeneity, and conformity. The findings indicate that the rapid arc plan has improved dose distribution in the target volume compared with IMRT, but the tumor control probability obtained for the two planning methods, Rapid arc (3 arcs) and IMRT (7 fields), are similar. The treatment time and monitor units for the Rapid arc (3 arcs) were superior to other planning methods and considered to be standard in head & neck radiotherapy.

RadPhysBio: A Radiobiological Database for the Prediction of Cell Survival upon Exposure to Ionizing Radiation.

Based on the need for radiobiological databases, in this work, we mined experimental ionizing radiation data of human cells treated with X-rays, γ-rays, carbon ions, protons and α-particles, by manually searching the relevant literature in PubMed from 1980 until 2024. In order to calculate normal and tumor cell survival α and ß coefficients of the linear quadratic (LQ) established model, as well as the initial values of the double-strand breaks (DSBs) in DNA, we used WebPlotDigitizer and Python programming language. We also produced complex DNA damage results through the fast Monte Carlo code MCDS in order to complete any missing data. The calculated α/ß values are in good agreement with those valued reported in the literature, where α shows a relatively good association with linear energy transfer (LET), but not ß. In general, a positive correlation between DSBs and LET was observed as far as the experimental values are concerned. Furthermore, we developed a biophysical prediction model by using machine learning, which showed a good performance for α, while it underscored LET as the most important feature for its prediction. In this study, we designed and developed the novel radiobiological 'RadPhysBio' database for the prediction of irradiated cell survival (α and ß coefficients of the LQ model). The incorporation of machine learning and repair models increases the applicability of our results and the spectrum of potential users.

Dosimetric validation of SmART-RAD Monte Carlo modelling for x-ray cabinet radiobiology irradiators.

Objective. Accuracy and reproducibility in the measurement of radiation dose and associated reporting are critically important for the validity of basic and preclinical radiobiological studies performed with kilovolt x-ray radiation cabinets. This is essential to enable results of radiobiological studies to be repeated, as well as enable valid comparisons between laboratories. In addition, the commonly used single point dose value hides the 3D dose heterogeneity across the irradiated sample. This is particularly true for preclinical rodent models, and is generally difficult to measure directly. Radiation transport simulations integrated in an easy to use application could help researchers improve quality of dosimetry and reporting.Approach. This paper describes the use and dosimetric validation of a newly-developed Monte Carlo (MC) tool, SmART-RAD, to simulate the x-ray field in a range of standard commercial x-ray cabinet irradiators used for preclinical irradiations. Comparisons are made between simulated and experimentally determined dose distributions for a range of configurations to assess the potential use of this tool in determining dose distributions through samples, based on more readily available air-kerma calibration point measurements.Main results. Simulations gave very good dosimetric agreement with measured depth dose distributions in phantoms containing both water and bone equivalent materials. Good spatial and dosimetric agreement between simulated and measured dose distributions was obtained when using beam-shaping shielding.Significance. The MC simulations provided by SmART-RAD provide a useful tool to go from a limited number of dosimetry measurements to detailed 3D dose distributions through a non-homogeneous irradiated sample. This is particularly important when trying to determine the dose distribution in more complex geometries. The use of such a tool can improve reproducibility and dosimetry reporting in preclinical radiobiological research.

Primary study of the relative and compound biological effectiveness model for boron neutron capture therapy based on nanodosimetry.

BACKGROUND: The current radiobiological model employed for boron neutron capture therapy (BNCT) treatment planning, which relies on microdosimetry, fails to provide an accurate representation the biological effects of BNCT. The precision in calculating the relative biological effectiveness (RBE) and compound biological effectiveness (CBE) plays a pivotal role in determining the therapeutic efficacy of BNCT. Therefore, this study focuses on how to improve the accuracy of the biological effects of BNCT. PURPOSE: The purpose of this study is to propose new radiation biology models based on nanodosimetry to accurately assess RBE and CBE for BNCT. METHODS: Nanodosimetry, rooted in ionization cluster size distributions (ICSD), introduces a novel approach to characterize radiation quality by effectively delineating RBE through the ion track structure at the nanoscale. In the context of prior research, this study presents a computational model for the nanoscale assessment of RBE and CBE. We establish a simplified model of DNA chromatin fiber using the Monte Carlo code TOPAS-nBio to evaluate the applicability of ICSD to BNCT and compute nanodosimetric parameters. RESULTS: Our investigation reveals that both homogeneous and heterogeneous nanodosimetric parameters, as well as the corresponding biological model coefficients α and ß, along with RBE values, exhibit variations in response to varying intracellular 10B concentrations. Notably, the nanodosimetric parameter M 1 C 2 $M_1^{{{\mathrm{C}}}_2}$ effectively captures the fluctuations in model coefficients α and RBE. CONCLUSION: Our model facilitates a nanoscale analysis of BNCT, enabling predictions of nanodosimetric quantities for secondary ions as well as RBE, CBE, and other essential biological metrics related to the distribution of boron. This contribution significantly enhances the precision of RBE calculations and holds substantial promise for future applications in treatment planning.

Comparison of two methods simulating inter-track interactions using the radiobiological Monte Carlo toolkit TOPAS-nBio.

Objective.To compare two independently developed methods that enable modelling inter-track interactions in TOPAS-nBio by examining the yield of radiolytic species in radiobiological Monte Carlo track structure simulations. One method uses a phase space file to assign more than one primary to one event, allowing for inter-track interaction between these primary particles. This method has previously been developed by this working group and published earlier. Using the other method, chemical reactions are simulated based on a new version of the independent reaction time approach to allow inter-track interactions.Approach.G-values were calculated and compared using both methods for different numbers of tracks able to undergo inter-track interactions.Main results.Differences in theG-values simulated with the two methods strongly depend on the molecule type, and deviations can range up to 3.9% (H2O2), although, on average, the deviations are smaller than 1.5%.Significance.Both methods seem to be suitable for simulating inter-track interactions, as they provide comparableG-values even though both techniques were developed independently of each other.

Track Structure of Light Ions: The Link to Radiobiology.

It is generally recognized that the biological response to irradiation by light ions is initiated by complex damages at the DNA level. In turn, the occurrence of complex DNA damages is related to spatial and temporal distribution of ionization and excitation events, i.e., the particle track structure. It is the aim of the present study to investigate the correlation between the distribution of ionizations at the nanometric scale and the probability to induce biological damage. By means of Monte Carlo track structure simulations, the mean ionization yield M1 and the cumulative probabilities F1, F2, and F3 of at least one, two and three ionizations, respectively, were calculated in spherical volumes of water-equivalent diameters equal to 1, 2, 5 and 10 nm. When plotted as a function of M1, the quantities F1, F2 and F3 are distributed along almost unique curves, largely independent of particle type and velocity. However, the shape of the curves depends on the size of the sensitive volume. When the site size is 1 nm, biological cross sections are strongly correlated to combined probabilities of F2 and F3 calculated in the spherical volume, and the proportionality factor is the saturation value of biological cross sections.

Modeling of scavenging systems in water radiolysis with Geant4-DNA.

PURPOSE: This paper presents the capabilities of the Geant4-DNA Monte Carlo toolkit to simulate water radiolysis with scavengers using the step-by-step (SBS) or the independent reaction times (IRT) methods. It features two examples of application areas: (1) computing the escape yield of H2O2 following a 60Co γ-irradiation and (2) computing the oxygen depletion in water irradiated with 1 MeV electrons. METHODS: To ease the implementation of the chemical stage in Geant4-DNA, we developed a user interface that helps define the chemical reactions and set the concentration of scavengers. The first application area example required two computational steps to perform water radiolysis using NO2- and NO3- as scavengers and a 60Co irradiation. The oxygen depletion computation technique for the second application area example consisted of simulating track segments of 1 MeV electrons and determining the radio-induced loss and gain of oxygen molecules. RESULTS: The production of H2O2 under variable scavenging levels is consistent with the literature; the mean relative difference between the SBS and IRT methods is 7.2 % ± 0.5 %. For the oxygen depletion 1 µs post-irradiation, the mean relative difference between both methods is equal to 9.8 % ± 0.3 %. The results in the microsecond scale depend on the initial partial pressure of oxygen in water. In addition, the computed oxygen depletions agree well with the literature. CONCLUSIONS: The Geant4-DNA toolkit makes it possible to simulate water radiolysis in the presence of scavengers. This feature offers perspectives in radiobiology, with the possibility of simulating cell-relevant scavenging mechanisms.

Modeling of dose and linear energy transfer homogeneity in cell nuclei exposed to alpha particles under various setup conditions.

PURPOSE: Different alpha exposure setups are often used to study the relation between biological responses and LET. This study aimed to estimate the dose heterogeneity and uncertainty in four exposure setups using Geant4 and PARTRAC codes. The importance of the irradiation system characteristics was shown in the context of reporting experimental results, especially in radiobiological studies at the molecular level. MATERIALS AND METHODS: Geant4 was used to estimate the dose distributions in cells grown on a disk exposed to alpha particles penetrating from above and below. The latter setup was simulated without and with a collimator. PARTRAC was used for the validation of Geant4 simulations based on distributions of the number of alpha particles penetrating a round nucleus and the deposited energy. RESULTS: The LET distributions obtained for simulated setups excluding the collimator were wide and non-Gaussian. Using a collimator resulted in a Gaussian LET distribution, but strongly reduced dose rate and dose homogeneity. Comparison between PARTRAC and Geant4 calculations for the cell nucleus exposed to alpha radiation showed an excellent agreement. CONCLUSIONS: The interpretation of results from radiobiological experiments with alpha particles should always cover the characteristics of the experimental setup, which can be done precisely with computational methods.

Large-scale in vitro microdosimetry via live cell microscopy imaging: implications for radiosensitivity and RBE evaluations in alpha-emitter radiopharmaceutical therapy.

BACKGROUND: Alpha-emitter radiopharmaceutical therapy (αRPT) has shown promising outcomes in metastatic disease. However, the short range of the alpha particles necessitates dosimetry on a near-cellular spatial scale. Current knowledge on cellular dosimetry is primarily based on in vitro experiments using cell monolayers. The goal of such experiments is to establish cell sensitivity to absorbed dose (AD). However, AD cannot be measured directly and needs to be modeled. Current models, often idealize cells as spheroids in a regular grid (geometric model), simplify binding kinetics and ignore the stochastic nature of radioactive decay. It is unclear what the impact of such simplifications is, but oversimplification results in inaccurate and non-generalizable results, which hampers the rigorous study of the underlying radiobiology. METHODS: We systematically mapped out 3D cell geometries, clustering behavior, agent binding, internalization, and subcellular trafficking kinetics for a large cohort of live cells under representative experimental conditions using confocal microscopy. This allowed for realistic Monte Carlo-based (micro)dosimetry. Experimentally established surviving fractions of the HER2 + breast cancer cell line treated with a 212Pb-labelled anti-HER2 conjugate or external beam radiotherapy, anchored a rigorous statistical approach to cell sensitivity and relative biological effectiveness (RBE) estimation. All outcomes were compared to a reference geometric model, which allowed us to determine which aspects are crucial model components for the proper study of the underlying radiobiology. RESULTS: In total, 567 cells were measured up to 26 h post-incubation. Realistic cell clustering had a large (2x), and cell geometry a small (16.4% difference) impact on AD, compared to the geometric model. Microdosimetry revealed that more than half of the cells do not receive any dose for most of the tested conditions, greatly impacting cell sensitivity estimates. Including these stochastic effects in the model, resulted in significantly more accurate predictions of surviving fraction and RBE (permutation test; p < .01). CONCLUSIONS: This comprehensive integration of the biological and physical aspects resulted in a more accurate method of cell survival modelling in αRPT experiments. Specifically, including realistic stochastic radiation effects and cell clustering behavior is crucial to obtaining generalizable radiobiological parameters.

Multiple levels of stochasticity accounted for in different radiation biophysical models: from physics to biology.

PURPOSE: In the present paper we investigate how some stochastic effects are included in a class of radiobiological models with particular emphasis on how such randomnesses reflect into the predicted cell survival curve. MATERIALS AND METHODS: We consider four different models, namely the Generalized Stochastic Microdosimetric Model GSM2, in its original full form, the Dirac GSM2 the Poisson GSM2 and the Repair-Misrepair Model (RMR). While GSM2 and the RMR models are known in literature, the Dirac and the Poisson GSM2  have been newly introduced in this work. We further numerically investigate via Monte Carlo simulation of four different particle beams, how the proposed stochastic approximations reflect into the predicted survival curves. To achieve these results, we consider different ion species at energies of interest for therapeutic applications, also including a mixed field scenario. RESULTS: We show how the Dirac GSM2, the Poisson GSM2 and the RMR can be obtained from the GSM2 under suitable approximations on the stochasticity considered. We analytically derive the cell survival curve predicted by the four models, characterizing rigorously the high and low dose limits. We further study how the theoretical findings emerge also using Monte Carlo numerical simulations. CONCLUSIONS: We show how different models include different levels of stochasticity in the description of cellular response to radiation. This translates into different cell survival predictions depending on the radiation quality.

2D mapping of radiation dose and clonogenic survival for accurate assessment ofin vitroX-ray GRID irradiation effects.

Spatially fractionated radiation therapy (SFRT or GRID) is an approach to deliver high local radiation doses in an 'on-off' pattern. To better appraise the radiobiological effects from GRID, a framework to link local radiation dose to clonogenic survival needs to be developed. A549 lung cancer cells were irradiated in T25 cm2flasks using 220 kV x-rays with an open field or through a tungsten GRID collimator with periodical 5 mm openings and 10 mm blockings. Delivered nominal doses were 2, 5, and 10 Gy. A novel approach for image segmentation was used to locate the centroid of surviving colonies in scanned images of the cell flasks. GafchromicTMfilm dosimetry (GFD) and FLUKA Monte Carlo (MC) simulations were employed to map the dose at each surviving colony centroid. Fitting the linear-quadratic (LQ) function to clonogenic survival data for open field irradiation, the expected survival level at a given dose level was calculated. The expected survival levels were then mapped together with the observed levels in the GRID-irradiated flasks. GFD and FLUKA MC gave similar dose distributions, with a mean peak-to-valley dose ratio of about 5. LQ-parameters for open field irradiation gaveα=0.24±0.02Gy-1andß=0.019±0.002Gy-2. The mean relative percentage deviation between observed and predicted survival in the (peak; valley) dose regions was (4.6; 3.1) %, (26.6; -1.0) %, and (129.8; -2.3) % for 2, 5 and 10 Gy, respectively. In conclusion, a framework for mapping of surviving colonies following GRID irradiation together with predicted survival levels from homogeneous irradiation was presented. For the given cell line, our findings indicate that GRID irradiation causes reduced survival in the peak regions compared to an open field configuration.

Revealing low-temperature plasma efficacy through a dose-rate assessment by DNA damage detection combined with machine learning models.

Low-temperature plasmas have quickly emerged as alternative and unconventional types of radiation that offer great promise for various clinical modalities. As with other types of radiation, the therapeutic efficacy and safety of low-temperature plasmas are ubiquitous concerns, and assessing their dose rates is crucial in clinical settings. Unfortunately, assessing the dose rates by standard dosimetric techniques has been challenging. To overcome this difficulty, we proposed a dose-rate assessment framework that combined the predictive modeling of plasma-induced damage in DNA by machine learning with existing radiation dose-DNA damage correlations. Our results indicated that low-temperature plasmas have a remarkably high dose rate that can be tuned by various process parameters. This attribute is beneficial for inducing radiobiological effects in a more controllable manner.

Nano-scale simulation of neuronal damage by galactic cosmic rays.

The effects of realistic, deep space radiation environments on neuronal function remain largely unexplored.In silicomodeling studies of radiation-induced neuronal damage provide important quantitative information about physico-chemical processes that are not directly accessible through radiobiological experiments. Here, we present the first nano-scale computational analysis of broad-spectrum galactic cosmic ray irradiation in a realistic neuron geometry. We constructed thousands ofin silicorealizations of a CA1 pyramidal neuron, each with over 3500 stochastically generated dendritic spines. We simulated the entire 33 ion-energy beam spectrum currently in use at the NASA Space Radiation Laboratory galactic cosmic ray simulator (GCRSim) using the TOol for PArticle Simulation (TOPAS) and TOPAS-nBio Monte Carlo-based track structure simulation toolkits. We then assessed the resulting nano-scale dosimetry, physics processes, and fluence patterns. Additional comparisons were made to a simplified 6 ion-energy spectrum (SimGCRSim) also used in NASA experiments. For a neuronal absorbed dose of 0.5 Gy GCRSim, we report an average of 250 ± 10 ionizations per micrometer of dendritic length, and an additional 50 ± 10, 7 ± 2, and 4 ± 2 ionizations per mushroom, thin, and stubby spine, respectively. We show that neuronal energy deposition by proton andα-particle tracks declines approximately hyperbolically with increasing primary particle energy at mission-relevant energies. We demonstrate an inverted exponential relationship between dendritic segment irradiation probability and neuronal absorbed dose for each ion-energy beam. We also find that there are no significant differences in the average physical responses between the GCRSim and SimGCRSim spectra. To our knowledge, this is the first nano-scale simulation study of a realistic neuron geometry using the GCRSim and SimGCRSim spectra. These results may be used as inputs to theoretical models, aid in the interpretation of experimental results, and help guide future study designs.

Dose calculations for preclinical radiobiology experiments conducted with single-field cabinet irradiators.

PURPOSE: To provide percentage depth dose (PDD) data along the central axis for dosimetry calculations in small-animal radiation biology experiments performed in cabinet irradiators. The PDDs are provided as a function of source-to-surface distance (SSD), field size, and animal size. METHODS: The X-ray tube designs for four biological cabinet irradiators, the RS2000, RT250, MultiRad350, and XRAD320, were simulated using the BEAMnrc Monte Carlo code to generate 160, 200, 250, and 320 kVp photon beams, respectively. The 320 kVp beam was simulated with two filtrations: a soft F1 aluminium filter and a hard F2 thoraeus filter made of aluminium, tin, and copper. Beams were collimated into circular fields with diameters of 0.5-10 cm at SSDs of 10-60 cm. Monte Carlo dose calculations in 1-5-cm diameter homogeneous (soft tissue) small-animal phantoms as well as in heterogeneous phantoms with 3-mm diameter cylindrical lung and bone inserts (rib and cortical bone) were performed using DOSXYZnrc. The calculated depth doses in three test-cases were estimated by applying SSD, field size, and animal size correction factors to a reference case (40-cm SSD, 1-cm field, and 5-cm animal size), and these results were compared with the specifically simulated (i.e., expected) doses to assess the accuracy of this method. Dosimetry for two test-case scenarios of 160 and 250 kVp beams (representative of end-user beam qualities) was also performed, whereby the simulated PDDs at two different depths were compared with the results based on the interpolation from reference data. RESULTS: The depth doses for three test-cases calculated at 200, 320 kVp F1, and 320 kVp F2 with half value layers (HVLs) ranging from ∼0.6 to 3.6 mm Cu, agreed well with the expected doses, yielding dose differences of 1.2%, 0.1%, and 1.0%, respectively. The two end-user test-cases for 160 and 250 kVp beams with respective HVLs of ∼0.8 and 1.8 mm Cu yielded dose differences of 1.4% and 3.2% between the simulated and the interpolated PDDs. The dose increase at the bone-tissue proximal interface ranged from 1.2 to 2.5 times the dose in soft tissue for rib and 1.3 to 3.7 times for cortical bone. The dose drop-off at 1-cm depth beyond the bone ranged from 1.3% to 6.0% for rib and 3.2% to 11.7% for cortical bone. No drastic dose perturbations occurred in the presence of lung, with lung-tissue interface dose of >99% of soft tissue dose and <3% dose increase at 1-cm depth beyond lung. CONCLUSIONS: The developed dose estimation method can be used to translate the measured dose at a point to dose at any depth in small-animal phantoms, making it feasible for preclinical calculation of dose distributions in animals irradiated with cabinet-style irradiators. The dosimetric impact of bone must be accurately quantified as dramatic dose perturbations at and beyond the bone interfaces can occur due to the relative importance of the photoelectric effect at kilovoltage energies. These results will help improve dosimetric accuracy in preclinical experiments.

A new platform for ultra-high dose rate radiobiological research using the BELLA PW laser proton beamline.

Radiotherapy is the current standard of care for more than 50% of all cancer patients. Improvements in radiotherapy (RT) technology have increased tumor targeting and normal tissue sparing. Radiations at ultra-high dose rates required for FLASH-RT effects have sparked interest in potentially providing additional differential therapeutic benefits. We present a new experimental platform that is the first one to deliver petawatt laser-driven proton pulses of 2 MeV energy at 0.2 Hz repetition rate by means of a compact, tunable active plasma lens beamline to biological samples. Cell monolayers grown over a 10 mm diameter field were exposed to clinically relevant proton doses ranging from 7 to 35 Gy at ultra-high instantaneous dose rates of 107 Gy/s. Dose-dependent cell survival measurements of human normal and tumor cells exposed to LD protons showed significantly higher cell survival of normal-cells compared to tumor-cells for total doses of 7 Gy and higher, which was not observed to the same extent for X-ray reference irradiations at clinical dose rates. These findings provide preliminary evidence that compact LD proton sources enable a new and promising platform for investigating the physical, chemical and biological mechanisms underlying the FLASH effect.

Technical note: Providing proton fields down to the few-MeV level at clinical pencil beam scanning facilities for radiobiological experiments.

PURPOSE: The adequate performance of radiobiological experiments using clinical proton beams typically requires substantial preparations to provide the appropriate setup for specific experiments. Providing radiobiologically interesting low-energy protons is a particular challenge, due to various physical effects that become more pronounced with larger absorber thickness and smaller proton energy. This work demonstrates the generation of decelerated low-energy protons from a clinical proton beam. METHODS: Monte Carlo simulations of proton energy spectra were performed for energy absorbers with varying thicknesses to reduce the energy of the clinical proton beam down to the few-MeV level corresponding to µ m-ranges. In this way, a setup with an optimum thickness of the absorber with a maximum efficiency of the proton fluence for the provisioning of low-energy protons is supposed to be found. For the specific applications of 2.5-3.3 MeV protons and α -particle range equivalent protons, the relative depth dose was measured and simulated together with the dose-averaged linear energy transfer (LETd) distribution. RESULTS: The resulting energy spectra from Monte Carlo simulations indicate an optimal absorber thickness for providing low-energy protons with maximum efficiency of proton fluence at an user-requested energy range for experiments. For instance, providing energies lower than 5 MeV, an energy spectrum with a relative total efficiency of 38.6 % to the initial spectrum was obtained with the optimal setup. The measurements of the depth dose, compared to the Monte Carlo simulations, showed that the dosimetry of low-energy protons works and protons with high LETd down to the range of α -particles can be produced. CONCLUSIONS: This work provides a method for generating all clinically and radiobiologically relevant energies - especially down to the few-MeV level - at one clinical facility with pencil beam scanning. Thereby, it enables radiobiological experiments under environmentally uniform conditions.

Track-structure modes in particle and heavy ion transport code system (PHITS): application to radiobiological research.

PURPOSE: In radiation physics, Monte Carlo radiation transport simulations are powerful tools to evaluate the cellular responses after irradiation. When investigating such radiation-induced biological effects, it is essential to perform track structure simulations by explicitly considering each atomic interaction in liquid water at the sub-cellular and DNA scales. The Particle and Heavy-Ion Transport code System (PHITS) is a Monte Carlo code which enables to calculate track structure at DNA scale by employing the track-structure modes for electrons, protons and carbon ions. In this paper, we review the recent development status and future prospects of the track-structure modes in the PHITS code. CONCLUSIONS: To date, the physical features of these modes have been verified using the available experimental data and Monte Carlo simulation results reported in literature. These track-structure modes can be used for calculating microdosimetric distributions to estimate cell survival and for estimating initial DNA damage yields. The use of PHITS track-structure mode is expected not only to clarify the underlying mechanisms of radiation effects but also to predict curative effects in radiation therapy. The results of PHITS simulations coupled with biophysical models will contribute to the radiobiological studies by precisely predicting radiation-induced biological effects based on the Monte Carlo approach.

In vitro modified microdosimetric kinetic model-based predictions for B14-150 cells survival in 450 MeV/u carbon ion beam with aluminum ridge filter for biologically optimized spread-out Bragg peak.

The relative biological efficiency of particle irradiation could be predicted with a wide variety of radiobiological models for various end-points. We validate the forecast of modified Microdosimetric Kinetic Modelin vitrousing combined data of reference Co-60 radiation and carbon ion plateau data for specific cell line to optimize the survival function in spread-out Bragg Peak obtained with an especially designed ridge filter. We used Geant4 Monte-Carlo software to simulate the fragment contribution along Bragg curve inside water phantom, open-source toolkit Survival to predict the expected linear-quadratic model parameters for each fragment, and in-house software to form the total survival curve in spread-out Bragg Peak. The irradiation was performed at U-70 synchrotron with an especially designed Aluminum ridge filter under the control of PTW and in-house ionization chambers. The cell clonogenic assay was conducted with the B14-150 cell line. The data analysis was accomplished using scipy and CERN ROOT. The clonogenic assay represents the survival in spread-out Bragg Peak at different points and qualitatively follows the modeled survival curve very well. The quantitative difference is within 3σ, and the deviation might be explained by the uncertainties of physical modeling using Monte-Carlo methods. Overall, the obtained results are promising for further usage in radiobiological studies or carbon ion radiotherapy. Shaping the survival curve in the region of interest (i.e., spread-out Bragg Peak) is a comprehensive task that requires high-performance computing approaches. Nevertheless, the method's potential application is related to the development of next-generation treatment planning systems for ion beams. This can open a wide range of improvements in patient treatment outcome, provide new optimized fractionation regimes or optimized dose delivery schemes, and serve as an entrance point to the translational science approach.

Moving Forward in the Next Decade: Radiation Oncology Sciences for Patient-Centered Cancer Care.

In a time of rapid advances in science and technology, the opportunities for radiation oncology are undergoing transformational change. The linkage between and understanding of the physical dose and induced biological perturbations are opening entirely new areas of application. The ability to define anatomic extent of disease and the elucidation of the biology of metastases has brought a key role for radiation oncology for treating metastatic disease. That radiation can stimulate and suppress subpopulations of the immune response makes radiation a key participant in cancer immunotherapy. Targeted radiopharmaceutical therapy delivers radiation systemically with radionuclides and carrier molecules selected for their physical, chemical, and biochemical properties. Radiation oncology usage of "big data" and machine learning and artificial intelligence adds the opportunity to markedly change the workflow for clinical practice while physically targeting and adapting radiation fields in real time. Future precision targeting requires multidimensional understanding of the imaging, underlying biology, and anatomical relationship among tissues for radiation as spatial and temporal "focused biology." Other means of energy delivery are available as are agents that can be activated by radiation with increasing ability to target treatments. With broad applicability of radiation in cancer treatment, radiation therapy is a necessity for effective cancer care, opening a career path for global health serving the medically underserved in geographically isolated populations as a substantial societal contribution addressing health disparities. Understanding risk and mitigation of radiation injury make it an important discipline for and beyond cancer care including energy policy, space exploration, national security, and global partnerships.

Investigating the feasibility of TOPAS-nBio for Monte Carlo track structure simulations by adapting GEANT4-DNA examples application.

Purpose.The purpose of this work is to investigate the feasibility of TOPAS-nBio for track structure simulations using tuple scoring and ROOT/Python-based post-processing.Materials and methods.There are several example applications implemented in GEANT4-DNA demonstrating track structure simulations. These examples are not implemented by default in TOPAS-nBio. In this study, the tuple scorer was used to re-simulate these examples. The simulations contained investigations of different physics lists, calculation of energy-dependent range, stopping power, mean free path andW-value. Additionally, further applications of the TOPAS-nBio tool were investigated, focusing on physical interactions and deposited energies of electrons with initial energies in the range of 10-60 eV, not covered in the recently published GEANT4-DNA simulations. Low-energetic electrons are currently of great interest in the radiobiology research community due to their high effectiveness towards the induction of biological damage.Results.The quantities calculated with TOPAS-nBio show a good agreement with the simulations of GEANT4-DNA with deviations of 5% at maximum. Thus, we have presented a feasible way to implement the example applications included in GEANT4-DNA in TOPAS-nBio. With the extended simulations, an insight could be given, which further tracking information can be gained with the track structure code and how cross sections and physics models influence a particle's fate.Conclusion.With our results, we could show the potentials of applying the tuple scorer in TOPAS-nBio Monte Carlo track structure simulations. Using this scorer, a large amount of information about the track structure can be accessed, which can be analyzed as preferred after the simulation.