Microdosimetric measurements as a tool to assess potential in-field and out-of-field toxicity regions in proton therapy

Integrating microdosimetricin vitroRBE models for particle therapy into TOPAS MC using the MicrOdosimetry-based modeliNg for RBE ASsessment (MONAS) tool

Objective.In this paper, we present MONAS (MicrOdosimetry-based modelliNg for relative biological effectiveness (RBE) ASsessment) toolkit. MONAS is a TOPAS Monte Carlo extension, that combines simulations of microdosimetric distributions with radiobiological microdosimetry-based models for predicting cell survival curves and dose-dependent RBE.Approach.MONAS expands TOPAS microdosimetric extension, by including novel specific energy scorers to calculate the single- and multi-event specific energy microdosimetric distributions at different micrometer scales. These spectra are used as physical input to three different formulations of themicrodosimetric kinetic model, and to thegeneralized stochastic microdosimetric model(GSM2), to predict dose-dependent cell survival fraction and RBE. MONAS predictions are then validated against experimental microdosimetric spectra andin vitrosurvival fraction data. To show the MONAS features, we present two different applications of the code: (i) the depth-RBE curve calculation from a passively scattered proton SOBP and monoenergetic12C-ion beam by using experimentally validated spectra as physical input, and (ii) the calculation of the 3D RBE distribution on a real head and neck patient geometry treated with protons.Main results.MONAS can estimate dose-dependent RBE and cell survival curves from experimentally validated microdosimetric spectra with four clinically relevant radiobiological models. From the radiobiological characterization of a proton SOBP and12C fields, we observe the well-known trend of increasing RBE values at the distal edge of the radiation field. The 3D RBE map calculated confirmed the trend observed in the analysis of the SOBP, with the highest RBE values found in the distal edge of the target.Significance.MONAS extension offers a comprehensive microdosimetry-based framework for assessing the biological effects of particle radiation in both research and clinical environments, pushing closer the experimental physics-based description to the biological damage assessment, contributing to bridging the gap between a microdosimetric description of the radiation field and its application in proton therapy treatment with variable RBE.

A spatial measure-valued model for radiation-induced DNA damage kinetics and repair under protracted irradiation condition

In the present work, we develop a general spatial stochastic model to describe the formation and repair of radiation-induced DNA damage. The model is described mathematically as a measure-valued particle-based stochastic system and extends in several directions the model developed in Cordoni et al. (Phys Rev E 103:012412, 2021; Int J Radiat Biol 1-16, 2022a; Radiat Res 197:218-232, 2022b). In this new spatial formulation, radiation-induced DNA damage in the cell nucleus can undergo different pathways to either repair or lead to cell inactivation. The main novelty of the work is to rigorously define a spatial model that considers the pairwise interaction of lesions and continuous protracted irradiation. The former is relevant from a biological point of view as clustered lesions are less likely to be repaired, leading to cell inactivation. The latter instead describes the effects of a continuous radiation field on biological tissue. We prove the existence and uniqueness of a solution to the above stochastic systems, characterizing its probabilistic properties. We further couple the model describing the biological system to a set of reaction-diffusion equations with random discontinuity that model the chemical environment. At last, we study the large system limit of the process. The developed model can be applied to different contexts, with radiotherapy and space radioprotection being the most relevant. Further, the biochemical system derived can play a crucial role in understanding an extremely promising novel radiotherapy treatment modality, named in the community FLASH radiotherapy, whose mechanism is today largely unknown.

Investigation of In-Field and Out-of-Field Radiation Quality With Microdosimetry and Its Impact on Relative Biological Effectiveness in Proton Therapy

PURPOSE

Using microdosimetry, this study investigated the relative biological effectiveness (RBE) and quality factor (Q¯) variations in field and out of field as a function of radiation quality for clinical protons.

METHODS AND MATERIALS

A water phantom with a spread-out Bragg peak (SOBP) was irradiated to acquire microdosimetric spectra at several distal and lateral depths with a tissue equivalent proportional counter. The measurements were used as inputs to microdosimetric kinetic and Loncol models to determine the RBE spatial distribution and compare it with predictions from the dose-averaged linear energy transfer-based McNamara model. Q¯ values and biological and dose equivalent values were also calculated.

RESULTS

The data demonstrated that radiation quality changed more rapidly with depth than lateral distance from the SOBP. In beam, y_D ranged from approximately 4 keV/μm at the entrance to 8 keV/μm at the SOBP far end, reaching approximately 15 keV/μm at the penumbra. Out of field, the overall highest value of 23 ± 2 keV/μm was observed at the beam-edge penumbra. Radiation quality changes caused RBE deviations from the clinical value of 1.1, whose extent depends on the approach used for assessing radiation quality as well as on the radiobiological model. For RBE₁₀, microdosimetry-based models appeared to better reproduce the radiobiological data than the dose-averaged linear energy transfer model. Out of field, both the RBE and Q¯ values appeared to have limitations in describing the radiation biological effectiveness. This research also presents a first comprehensive benchmark of TOPAS code against in-field and out-of-field microdosimetric spectra of therapeutic protons.

CONCLUSIONS

Further investigation will be necessary to evaluate the quantitative effects of RBE variations on treatment planning and assess the clinical consequences in terms of both tumor control and normal-tissue toxicity. The achievement of this goal calls for accurate radiobiological data to validate the RBE models.

An exploratory study of machine learning techniques applied to therapeutic energies particle tracking in microdosimetry using the novel hybrid detector for microdosimetry (HDM)

In this work we present an advanced random forest-based machine learning (ML) model, trained and tested on Geant4 simulations. The developed ML model is designed to improve the performance of the hybrid detector for microdosimetry (HDM), a novel hybrid detector recently introduced to augment the microdosimetric information with the track length of particles traversing the microdosimeter. The present work leads to the following improvements of HDM: (i) the detection efficiency is increased up to 100%, filling not detected particles due to scattering within the tracker or non-active regions, (ii) the track reconstruction algorithm precision. Thanks to the ML models, we were able to reconstruct the microdosimetric spectra of both protons and carbon ions at therapeutic energies, predicting the real track length for every particle detected by the microdosimeter. The ML model results have been extensively studied, focusing on non-accurate predictions of the real track lengths. Such analysis has been used to identify HDM limitations and to understand possible future improvements of both the detector and the ML models.

A systematic study of the contribution of counting statistics to the final lineal energy uncertainty in microdosimetry

Objectives. Microdosimetry is proving to be a reliable and powerful tool to be applied in different fields such as radiobiology, radiation protection and hadron therapy. However, accepted standard protocols and codes of practice are still missing. With this regard, a systematic and methodical uncertainty analysis is fundamental to build an accredited uncertainty budget of practical use. This work studied the contribution of counting statistics (i.e. number of events collected) to the final frequency-mean and dose-mean lineal energy uncertainties, aiming at providing guidelines for good experimental and simulation practice. The practical limitation of current technologies and the non-negligible probability of nuclear reactions require careful considerations and nonlinear approaches. Approach. Microdosimetric data were obtained by means of the particle tracking Monte Carlo code Geant4. The uncertainty analysis was carried out relying on a Monte Carlo based numerical analysis, as suggested by the BIPM's ‘Guide to the expression of uncertainty in measurement’. Final uncertainties were systematically investigated for proton, helium and carbon ions at an increasing number of detected events, for a range of different clinical-relevant beam energies. Main results. Rare events generated by nuclear interactions in the detector sensitive volume were found to massively degrade microdosimetric uncertainties unless a very high statistics is collected. The study showed an increasing impact of such events for increasing beam energy and lighter ions. For instance, in the entrance region of a 250 MeV proton beam, about 5 ∗ 107 events need to be collected to obtain a dose-mean lineal energy uncertainty below 10%. Significance. The results of this study help define the necessary conditions to achieve appropriate statistics in computational microdosimetry, pointing out the importance of properly taking into account nuclear interaction events. Their impact on microdosimetric quantities and on their uncertainty is significant and cannot be overlooked, particularly when characterising clinical beams and radiobiological response. This work prepared the ground for deeper investigations involving dedicated experiments and for the development of a method to properly evaluate the counting statistics uncertainty contribution in the uncertainty budget, whose accuracy is fundamental for the clinical transition of microdosimetry.

A multi-detector experimental setup for the study of space radiation shielding materials: Measurement of secondary radiation behind thick shielding and assessment of its radiobiological effect

Space agencies have recognized the risks of astronauts’ exposure to space radiation and are developing complex model-based risk mitigation strategies. In the foundation of these models, there are still significant gaps of knowledge concerning nuclear fragmentation reactions which need to be addressed by ground-based experiments. There is a lack of data on neutron and light ion production by heavy ions, which are an important component of galactic cosmic radiation (GCR). A research collaboration has been set up to characterize the secondary radiation field produced by GCR-like radiation provided by a particle accelerator in thick shielding. The aim is to develop a novel method for producing high-quality experimental data on neutron and light ion production in shielding materials relevant for space radiation protection. Four complementary detector systems are used to determine the energy and angular distributions of high-energy secondary neutrons and light ions. In addition to the physical measurement approach, the biological effectiveness of the secondary radiation field is determined by measuring chromosome aberrations in human peripheral lymphocytes placed behind the shielding. The experiments are performed at the heavy ion

Quantification of biological range uncertainties in patients treated at the Krakow proton therapy centre

Background

Variable relative biological effectiveness (vRBE) in proton therapy might significantly modify the prediction of RBE-weighted dose delivered to a patient during proton therapy. In this study we will present a method to quantify the biological range extension of the proton beam, which results from the application of vRBE approach in RBE-weighted dose calculation.

Methods and materials

The treatment plans of 95 patients (brain and skull base patients) were used for RBE-weighted dose calculation with constant and the McNamara RBE model. For this purpose the Monte Carlo tool FRED was used. The RBE-weighted dose distributions were analysed using indices from dose-volume histograms. We used the volumes receiving at least 95% of the prescribed dose (V95) to estimate the biological range extension resulting from vRBE approach.

Results

The vRBE model shows higher median value of relative deposited dose and D95 in the planning target volume by around 1% for brain patients and 4% for skull base patients. The maximum doses in organs at risk calculated with vRBE was up to 14 Gy above dose limit. The mean biological range extension was greater than 0.4 cm.

Discussion

Our method of estimation of biological range extension is insensitive for dose inhomogeneities and can be easily used for different proton plans with intensity-modulated proton therapy (IMPT) optimization. Using volumes instead of dose profiles, which is the common method, is more universal. However it was tested only for IMPT plans on fields arranged around the tumor area.

Conclusions

Adopting a vRBE model results in an increase in dose and an extension of the beam range, which is especially disadvantageous in cancers close to organs at risk. Our results support the need to re-optimization of proton treatment plans when considering vRBE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-02022-5.

Generalized stochastic microdosimetric model: The main formulation

The present work introduces a rigorous stochastic model, called the generalized stochastic microdosimetric model (GSM^{2}), to describe biological damage induced by ionizing radiation. Starting from the microdosimetric spectra of energy deposition in tissue, we derive a master equation describing the time evolution of the probability density function of lethal and potentially lethal DNA damage induced by a given radiation to a cell nucleus. The resulting probability distribution is not required to satisfy any a priori conditions. After the initial assumption of instantaneous irradiation, we generalized the master equation to consider damage induced by a continuous dose delivery. In addition, spatial features and damage movement inside the nucleus have been taken into account. In doing so, we provide a general mathematical setting to fully describe the spatiotemporal damage formation and evolution in a cell nucleus. Finally, we provide numerical solutions of the master equation exploiting Monte Carlo simulations to validate the accuracy of GSM^{2}. Development of GSM^{2} can lead to improved modeling of radiation damage to both tumor and normal tissues, and thereby impact treatment regimens for better tumor control and reduced normal tissue toxicities.