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.

TRAX-CHEMxt: Towards the Homogeneous Chemical Stage of Radiation Damage.

The indirect effect of radiation plays an important role in radio-induced biological damages. Monte Carlo codes have been widely used in recent years to study the chemical evolution of particle tracks. However, due to the large computational efforts required, their applicability is typically limited to simulations in pure water targets and to temporal scales up to the µs. In this work, a new extension of TRAX-CHEM is presented, namely TRAX-CHEMxt, able to predict the chemical yields at longer times, with the capability of exploring the homogeneous biochemical stage. Based on the species coordinates produced around one track, the set of reaction-diffusion equations is solved numerically with a computationally light approach based on concentration distributions. In the overlapping time scale (500 ns-1 µs), a very good agreement to standard TRAX-CHEM is found, with deviations below 6% for different beam qualities and oxygenations. Moreover, an improvement in the computational speed by more than three orders of magnitude is achieved. The results of this work are also compared with those from another Monte Carlo-based algorithm and a fully homogeneous code (Kinetiscope). TRAX-CHEMxt will allow for studying the variation in chemical endpoints at longer timescales with the introduction, as the next step, of biomolecules, for more realistic assessments of biological response under different radiation and environmental conditions.

An artificial intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model.

The present work develops ANAKIN: anArtificial iNtelligence bAsed model for (radiation-induced) cell KIlliNg prediction. ANAKIN is trained and tested over 513 cell survival experiments with different types of radiation contained in the publicly available PIDE database. We show how ANAKIN accurately predicts several relevant biological endpoints over a wide broad range on ion beams and for a high number of cell-lines. We compare the prediction of ANAKIN to the only two radiobiological models forRelative Biological Effectivenessprediction used in clinics, that is theMicrodosimetric Kinetic Modeland theLocal Effect Model(LEM version III), showing how ANAKIN has higher accuracy over the all considered cell survival fractions. At last, via modern techniques ofExplainable Artificial Intelligence(XAI), we show how ANAKIN predictions can be understood and explained, highlighting how ANAKIN is in fact able to reproduce relevant well-known biological patterns, such as the overkilling effect.

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, yD 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.

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.

Modelling the HPRT-gene mutation induction of particle beams: systematicin vitrodata collection, analysis and microdosimetric kinetic model implementation.

Objective. Since the early years, particle therapy treatments have been associated with concerns for late toxicities, especially secondary cancer risk (SCR). Nowadays, this concern is related to patients for whom long-term survival is expected (e.g. breast cancer, lymphoma, paediatrics). In the aim to contribute to this research, we present a dedicated statistical and modelling analysis aiming at improving our understanding of the RBE for mutation induction (RBEM˜) for different particle species.Approach. We built a new database based on a systematic collection of RBE data for mutation assays of the gene encoding for the purine salvage enzyme hypoxanthine-guanine phosphoribosyltransferase from literature (105 entries, distributed among 3 cell lines and 16 particle species). The data were employed to perform statistical and modelling analysis. For the latter, we adapted the microdosimetric kinetic model (MKM) to describe the mutagenesis in analogy to lethal lesion induction.Main results. Correlation analysis between RBE for survival (RBES) andRBEM˜reveals significant correlation between these two quantities (ρ= 0.86,p< 0.05). The correlation gets stronger when looking at subsets of data based on cell line and particle species. We also show that the MKM can be successfully employed to describeRBEM˜,obtaining comparably good agreement with the experimental data. Remarkably, to improve the agreement with experimental data the MKM requires, consistently in all the analysed cases, a reduced domain size for the description of mutation induction compared to that adopted for survival.Significance. We were able to show that RBESandRBEM˜are strongly related quantities. We also showed for the first time that the MKM could be successfully applied to the description of mutation induction, representing an endpoint different from the more traditional cell killing. In analogy to the RBES,RBEM˜can be implemented into treatment planning system evaluations.

Including Volume Effects in Biological Treatment Plan Optimization for Carbon Ion Therapy: Generalized Equivalent Uniform Dose-Based Objective in TRiP98.

We describe a way to include biologically based objectives in plan optimization specific for carbon ion therapy, beyond the standard voxel-dose-based criteria already implemented in TRiP98, research planning software for ion beams. The aim is to account for volume effects-tissue architecture-dependent response to damage-in the optimization procedure, using the concept of generalized equivalent uniform dose (gEUD), which is an expression to convert a heterogeneous dose distribution (e.g., in an organ at risk (OAR)) into a uniform dose associated with the same biological effect. Moreover, gEUD is closely related to normal tissue complication probability (NTCP). The multi-field optimization problem here takes also into account the relative biological effectiveness (RBE), which in the case of ion beams is not factorizable and introduces strong non-linearity. We implemented the gEUD-based optimization in TRiP98, allowing us to control the whole dose-volume histogram (DVH) shape of OAR with a single objective by adjusting the prescribed gEUD 0 and the volume effect parameter a, reducing the volume receiving dose levels close to mean dose when a = 1 (large volume effect) while close to maximum dose for a >> 1 (small volume effect), depending on the organ type considered. We studied the role of gEUD 0 and a in the optimization, and we compared voxel-dose-based and gEUD-based optimization in chordoma cases with different anatomies. In particular, for a plan containing multiple OARs, we obtained the same target coverage and similar DVHs for OARs with a small volume effect while decreasing the mean dose received by the proximal parotid, thus reducing its NTCP by a factor of 2.5. Further investigations are done for this plan, considering also the distal parotid gland, obtaining a NTCP reduction by a factor of 1.9 for the proximal and 2.9 for the distal one. In conclusion, this novel optimization method can be applied to different OARs, but it achieves the largest improvement for organs whose volume effect is larger. This allows TRiP98 to perform a double level of biologically driven optimization for ion beams, including at the same time RBE-weighted dose and volume effects in inverse planning. An outlook is presented on the possible extension of this method to the target.

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.

FLASH radiotherapy with carbon ion beams.

FLASH radiotherapy is considered a new potential breakthrough in cancer treatment. Ultra-high dose rates (>40 Gy/s) have been shown to reduce toxicity in the normal tissue without compromising tumor control, resulting in a widened therapeutic window. These high dose rates are more easily achievable in the clinic with charged particles, and clinical trials are, indeed, ongoing using electrons or protons. FLASH could be an attractive solution also for heavier ions such as carbon and could even enhance the therapeutic window. However, it is not yet known whether the FLASH effect will be the same as for sparsely ionizing radiation when densely ionizing carbons ions are used. Here we discuss the technical challenges in beam delivery and present a promising solution using 3D range-modulators in order to apply ultra-high dose rates (UHDR) compatible with FLASH with carbon ions. Furthermore, we will discuss the possible outcome of C-ion therapy at UHDR on the level of the radiobiological and radiation chemical effects.

Cell Survival Computation via the Generalized Stochastic Microdosimetric Model (GSM2); Part I: The Theoretical Framework.

The current article presents the first application of the Generalized Stochastic Microdosimetric Model (GSM2) for computing explicitly a cell survival curve. GSM2 is a general probabilistic model that predicts the kinetic evolution of DNA damages taking full advantage of a microdosimetric description of a radiation energy deposition. We show that, despite the high generality and flexibility of GSM2, an explicit form for the survival fraction curve predicted by the GSM2 is achievable. We illustrate how several correction terms typically added a posteriori in existing radiobiological models to improve the prediction accuracy, are naturally included into GSM2. Among the most relevant features of the survival curve derived from GSM2 and presented in this article, is the linear-quadratic behavior at low doses and a purely linear trend for high doses. The study also identifies and discusses the connections between GSM2 and existing cell survival models, such as the Microdosimetric Kinetic Model (MKM) and the Multi-hit model. Several approximations to predict cell survival in different irradiation regimes are also introduced to include intercellular non-Poissonian behaviors.

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

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/ß 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.

Study of relationship between dose, LET and the risk of brain necrosis after proton therapy for skull base tumors.

PURPOSE: We investigated the relationship between RBE-weighted dose (DRBE) calculated with constant (cRBE) and variable RBE (vRBE), dose-averaged linear energy transfer (LETd) and the risk of radiographic changes in skull base patients treated with protons. METHODS: Clinical treatment plans of 45 patients were recalculated with Monte Carlo tool FRED. Radiographic changes (i.e. edema and/or necrosis) were identified by MRI. Dosimetric parameters for cRBE and vRBE were computed. Biological margin extension and voxel-based analysis were employed looking for association of DRBE(vRBE) and LETd with brain edema and/or necrosis. RESULTS: When using vRBE, Dmax in the brain was above the highest dose limits for 38% of patients, while such limit was never exceeded assuming cRBE. Similar values of Dmax were observed in necrotic regions, brain and temporal lobes. Most of the brain necrosis was in proximity to the PTV. The voxel-based analysis did not show evidence of an association with high LETd values. CONCLUSIONS: When looking at standard dosimetric parameters, the higher dose associated with vRBE seems to be responsible for an enhanced risk of radiographic changes. However, as revealed by a voxel-based analysis, the large inter-patient variability hinders the identification of a clear effect for high LETd.

May oxygen depletion explain the FLASH effect? A chemical track structure analysis.

BACKGROUND AND PURPOSE: Recent observations in animal models show that ultra-high dose rate ("FLASH") radiation treatment significantly reduces normal tissue toxicity maintaining an equivalent tumor control. The dependence of this "FLASH" effect on target oxygenation has led to the assumption that oxygen "depletion" could be its major driving force. MATERIALS AND METHODS: In a bottom-up approach starting from the chemical track evolution of 1 MeV electrons in oxygenated water simulated with the TRAX-CHEM Monte Carlo code, we determine the oxygen consumption and radiolytic reactive oxygen species production following a short radiation pulse. Based on these values, the effective dose weighted by oxygen enhancement ratio (OER) or the in vitro cell survival under dynamic oxygen pressure is calculated and compared to that of conventional exposures, at constant OER. RESULTS: We find an excellent agreement of our Monte Carlo predictions with the experimental value for radiolytic oxygen removal from oxygenated water. However, the application of the present model to published radiobiological experiment conditions shows that oxygen depletion can only have a negligible impact on radiosensitivity through oxygen enhancement, especially at typical experimental oxygenations where a FLASH effect has been observed. CONCLUSION: We show that the magnitude and dependence of the "oxygen depletion" hypothesis are not consistent with the observed biological effects of FLASH irradiation. While oxygenation plays an undoubted role in mediating the FLASH effect, we conclude that state-of-the-art radiation chemistry models do not support oxygen depletion and radiation-induced transient hypoxia as the main mechanism.

Hypoxia Transcriptomic Modifications Induced by Proton Irradiation in U87 Glioblastoma Multiforme Cell Line.

In Glioblastoma Multiforme (GBM), hypoxia is associated with radioresistance and poor prognosis. Since standard GBM treatments are not always effective, new strategies are needed to overcome resistance to therapeutic treatments, including radiotherapy (RT). Our study aims to shed light on the biomarker network involved in a hypoxic (0.2% oxygen) GBM cell line that is radioresistant after proton therapy (PT). For cultivating cells in acute hypoxia, GSI's hypoxic chambers were used. Cells were irradiated in the middle of a spread-out Bragg peak with increasing PT doses to verify the greater radioresistance in hypoxic conditions. Whole-genome cDNA microarray gene expression analyses were performed for samples treated with 2 and 10 Gy to highlight biological processes activated in GBM following PT in the hypoxic condition. We describe cell survival response and significant deregulated pathways responsible for the cell death/survival balance and gene signatures linked to the PT/hypoxia configurations assayed. Highlighting the molecular pathways involved in GBM resistance following hypoxia and ionizing radiation (IR), this work could suggest new molecular targets, allowing the development of targeted drugs to be suggested in association with PT.

Proton pencil beam scanning reduces secondary cancer risk in breast cancer patients with internal mammary chain involvement compared to photon radiotherapy.

PURPOSE: Proton pencil beam scanning (PBS) represents an interesting option for the treatment of breast cancer (BC) patients with nodal involvement. Here we compare tangential 3D-CRT and VMAT to PBS proton therapy (PT) in terms of secondary cancer risk (SCR) for the lungs and for contralateral breast. METHODS: Five BC patients including supraclavicular (SVC) nodes in the target (Group 1) and five including SVC plus internal-mammary-nodes (IMNs, Group 2) were considered. The Group 1 patients were planned by PT versus tangential 3D-CRT in free-breathing (FB). The Group 2 patients were planned by PT versus VMAT considering both FB and deep-inspiration breath hold (DIBH) irradiation. The prescription dose to the target volume was 50 Gy (2 Gy/fraction). A constant RBE = 1.1 was assumed for PT. The SCR was evaluated with the excess absolute risk (EAR) formalism, considering also the age dependence. A cumulative EAR was finally computed. RESULTS: According to the linear, linear-exponential and linear-plateau dose response model, the cumulative EAR for Group 1 patients after PT was equal to 45 ± 10, 17 ± 3 and 15 ± 3, respectively. The corresponding relative increase for tangential 3D-CRT was equal to a factor 2.1 ± 0.5, 2.1 ± 0.4 and 2.3 ± 0.4. Group 2 patients showed a cumulative EAR after PT in FB equal to 65 ± 3, 21 ± 1 and 20 ± 1, according to the different models; the relative risk obtained with VMAT increased by a factor 3.5 ± 0.2, 5.2 ± 0.3 and 5.1 ± 0.3. Similar values emerge from DIBH plans. Contrary to photon radiotherapy, PT appears to be not sensitive to the age dependence due to the very low delivered dose. CONCLUSIONS: PBS PT is associated to significant SCR reduction in BC patients compared to photon radiotherapy. The benefits are maximized for young patients with both SVC and IMNs involvement. When combined with the improved sparing of the heart, this might contribute to the establishment of effective patient-selection criteria for proton BC treatments.

Evaluation of proton beam radiation-induced skin injury in a murine model using a clinical SOBP.

The purpose of this paper is to characterize the skin deterministic damage due to the effect of proton beam irradiation in mice occurred during a long-term observational experiment. This study was initially defined to evaluate the insurgence of myelopathy irradiating spinal cords with the distal part of a Spread-out Bragg peak (SOBP). To the best of our knowledge, no study has been conducted highlighting high grades of skin injury at the dose used in this paper. Nevertheless these effects occurred. In this regard, the experimental evidence of significant insurgence of skin injury induced by protons using a SOBP configuration will be shown. Skin damages were classified into six scores (from 0 to 5) according to the severity of the injuries and correlated to ED50 (i.e. the radiation dose at which 50% of animals show a specific score) at 40 days post-irradiation (d.p.i.). The effects of radiation on the overall animal wellbeing have been also monitored and the severity of radiation-induced skin injuries was observed and quantified up to 40 d.p.i.

Impact of Target Oxygenation on the Chemical Track Evolution of Ion and Electron Radiation.

The radiosensitivity of biological systems is strongly affected by the system oxygenation. On the nanoscopic scale and molecular level, this effect is considered to be strongly related to the indirect damage of radiation. Even though particle track radiolysis has been the object of several studies, still little is known about the nanoscopic impact of target oxygenation on the radical yields. Here we present an extension of the chemical module of the Monte Carlo particle track structure code TRAX, taking into account the presence of dissolved molecular oxygen in the target material. The impact of the target oxygenation level on the chemical track evolution and the yields of all the relevant chemical species are studied in water under different irradiation conditions: different linear energy transfer (LET) values, different oxygenation levels, and different particle types. Especially for low LET radiation, a large production of two highly toxic species ( HO 2 • and O 2 • - ), which is not produced in anoxic conditions, is predicted and quantified in oxygenated solutions. The remarkable correlation between the HO 2 • and O 2 • - production yield and the oxygen enhancement ratio observed in biological systems suggests a direct or indirect involvement of HO 2 • and O 2 • - in the oxygen sensitization effect. The results are in agreement with available experimental data and previous computational approaches. An analysis of the oxygen depletion rate in different radiation conditions is also reported. The radiosensitivity of biological systems is strongly affected by the system oxygenation. On the nanoscopic scale and molecular level, this effect is considered to be strongly related to the indirect damage of radiation. Even though particle track radiolysis has been the object of several studies, still little is known about the nanoscopic impact of target oxygenation on the radical yields. Here we present an extension of the chemical module of the Monte Carlo particle track structure code TRAX, taking into account the presence of dissolved molecular oxygen in the target material. The impact of the target oxygenation level on the chemical track evolution and the yields of all the relevant chemical species are studied in water under different irradiation conditions: different linear energy transfer (LET) values, different oxygenation levels, and different particle types. Especially for low LET radiation, a large production of two highly toxic species ( HO 2 • and O 2 • - ), which is not produced in anoxic conditions, is predicted and quantified in oxygenated solutions. The remarkable correlation between the HO 2 • and O 2 • - production yield and the oxygen enhancement ratio observed in biological systems suggests a direct or indirect involvement of HO 2 • and O 2 • - in the oxygen sensitization effect. The results are in agreement with available experimental data and previous computational approaches. An analysis of the oxygen depletion rate in different radiation conditions is also reported.

Modelling the risk of radiation induced alopecia in brain tumor patients treated with scanned proton beams.

PURPOSE: To develop normal tissue complication probability (NTCP) models for radiation-induced alopecia (RIA) in brain tumor patients treated with proton therapy (PT). METHODS AND MATERIALS: We analyzed 116 brain tumor adult patients undergoing scanning beam PT (median dose 54 GyRBE; range 36-72) for CTCAE v.4 grade 2 (G2) acute (≤90 days), late (>90 days) and permanent (>12 months) RIA. The relative dose-surface histogram (DSH) of the scalp was extracted and used for Lyman-Kutcher-Burman (LKB) modelling. Moreover, DSH metrics (Sx: the surface receiving ≥ X Gy, D2%: near maximum dose, Dmean: mean dose) and non-dosimetric variables were included in a multivariable logistic regression NTCP model. Model performances were evaluated by the cross-validated area under the receiver operator curve (ROC-AUC). RESULTS: Acute, late and permanent G2-RIA was observed in 52%, 35% and 19% of the patients, respectively. The LKB models showed a weak dose-surface effect (0.09 ≤ n ≤ 0.19) with relative steepness 0.29 ≤ m ≤ 0.56, and increasing tolerance dose values when moving from acute and late (22 and 24 GyRBE) to permanent RIA (44 GyRBE). Multivariable modelling selected S21Gy for acute and S25Gy, for late G2-RIA as the most predictive DSH factors. Younger age was selected as risk factor for acute G2-RIA while surgery as risk factor for late G2-RIA. D2% was the only variable selected for permanent G2-RIA. Both LKB and logistic models exhibited high predictive performances (ROC-AUCs range 0.86-0.90). CONCLUSION: We derived NTCP models to predict G2-RIA after PT, providing a comprehensive modelling framework for acute, late and permanent occurrences that, once externally validated, could be exploited for individualized scalp sparing treatment planning strategies in brain tumor patients.

Clinical implementation in proton therapy of multi-field optimization by a hybrid method combining conventional PTV with robust optimization.

To implement a robust multi-field optimization (MFO) technique compatible with the application of a Monte Carlo (MC) algorithm and to evaluate its robustness. Nine patients (three brain, five head-and-neck, one spine) underwent proton treatment generated by a novel robust MFO technique. A hybrid (hMFO) approach was implemented, planning dose coverage on isotropic PTV compensating for setup errors, whereas range calibration uncertainties are incorporated into PTV robust optimization process. hMFO was compared with single-field optimization (SFO) and full robust multi-field optimization (fMFO), both on the nominal plan and the worst-case scenarios assessed by robustness analysis. The SFO and the fMFO plans were normalized to hMFO on CTV to obtain iso-D95 coverage, and then the organs at risk (OARs) doses were compared. On the same OARs, in the normalized nominal plans the potential impact of variable relative biological effectiveness (RBE) was investigated. hMFO reduces the number of scenarios computed for robust optimization (from twenty-one in fMFO to three), making it practicable with the application of a MC algorithm. After normalizing on D95 CTV coverage, nominal hMFO plans were superior compared to SFO in terms of OARs sparing (p  < 0.01), without significant differences compared to fMFO. The improvement in OAR sparing with hMFO with respect to SFO was preserved in worst-case scenarios (p  < 0.01), confirming that hMFO is as robust as SFO to physical uncertainties, with no significant differences when compared to the worst case scenarios obtained by fMFO. The dose increase on OARs due to variable RBE was comparable to the increase due to physical uncertainties (i.e. 4-5 Gy(RBE)), but without significant differences between these techniques. hMFO allows improving plan quality with respect to SFO, with no significant differences with fMFO and without affecting robustness to setup, range and RBE uncertainties, making clinically feasible the application of MC-based robust optimization.