Sensitivity analysis of the relative biological effectiveness predicted by the local effect model

Investigation of the Impact of Temporal Dose Delivery Patterns of Ion Irradiation with the Local Effect Model

We present an extension of the Local Effect Model (LEM) to include time-dose relationships for predicting effects of protracted and split-dose ion irradiation at arbitrary LET. With this kinetic extension, the spatial and temporal induction and processing of DNA double strand breaks (DSB) in cellular nuclei can be simulated for a wide range of ion radiation qualities, doses and dose rates. The key concept of the extension is based on the joint spatial and temporal coexistence of initial DSB, leading to the formation of clustered DNA damage on the µm scale (as defined e.g., by the size scale of Mbp chromatin loops), which is considered to have an increased cellular lethality as compared to isolated, single DSB. By simulating the time dependent induction and repair of DSB and scoring of isolated and clustered DSB upon irradiation, the impact of dose rate and split dose on the cell survival probability can be computed. In a first part of this work, we systematically analyze the predicted impact of protraction in dependence of factors like dose, LET, ion species and radiosensitivity as characterized by the photon LQ-parameters. We establish links to common concepts that describe dose rate effects for low LET radiation. We also compare the model predictions to experimental data and find agreement with the general trends observed in the experiments. The relevant concepts of our approach are compared to other models suitable for predicting time effects. We investigate an apparent analogy between spatial and temporal concentration of radiation delivery, both leading to increased effectiveness, and discuss similarities and differences between the general dependencies of these clustering effects on their impacting factors. Finally, we conclude that the findings give additional support for the general concept of the LEM, i.e. the characterization of high LET radiation effects based on the distinction of just two classes of DSB (isolated DSB and clustered DSB).

Dosimetric and radiobiological analyses of a de-escalation strategy for elective nodal regions in human papillomavirus-associated oropharyngeal cancer

Introduction

In this simulation study, we examined the effects of a de-escalation strategy with a reduced dose to subclinical nodal regions in patients with human papillomavirus (HPV)-associated oropharyngeal carcinoma (OPC).

Methods

We created two patterns of intensity-modulated radiotherapy for 16 patients with HPV-associated OPC. In the standard and de-escalation plans, the initial field including elective nodal regions received 46 and 30 Gy, followed by 20 and 36 Gy to the cutdown field, respectively. Comparison metrics were set for each organ at risk (OAR). We compared these metric values and the probability of adverse effects based on the normal tissue complication probability (NTCP) model between the two plans.

Results

Both plans generally met the dose constraints for the targets and all OAR. Among the comparison metrics, the mean doses to the brain, pharyngeal constrictor muscle, thyroid, and skin and the dose to a 1 % volume of the skin were higher in the standard plan than in the de-escalation plan (P = 0.031, 0.007, < 0.001, < 0.001, and 0.006, respectively). NTCP analyses revealed that the probability of adverse effects in the ipsilateral parotid gland and thyroid was higher in the standard plan than in the de-escalation plan (standard vs. de-escalation plans: ipsilateral parotid gland, 6.4 % vs. 5.0 %, P = 0.016; thyroid, 3.3 % vs. 0.5 %, P < 0.001).

Conclusions

A de-escalation strategy with elective nodal regions is a promising treatment to prevent a decline in the quality of life in patients with HPV-associated OPC, particularly xerostomia, dysphagia, and hypothyroidism.

The Mayo Clinic Florida Microdosimetric Kinetic Model of Clonogenic Survival: Application to Various Repair-Competent Rodent and Human Cell Lines

The relative biological effectiveness (RBE) calculations used during the planning of ion therapy treatments are generally based on the microdosimetric kinetic model (MKM) and the local effect model (LEM). The Mayo Clinic Florida MKM (MCF MKM) was recently developed to overcome the limitations of previous MKMs in reproducing the biological data and to eliminate the need for ion-exposed in vitro data as input for the model calculations. Since we are considering to implement the MCF MKM in clinic, this article presents (a) an extensive benchmark of the MCF MKM predictions against corresponding in vitro clonogenic survival data for 4 rodent and 10 cell lines exposed to ions from 1H to 238U, and (b) a systematic comparison with published results of the latest version of the LEM (LEM IV). Additionally, we introduce a novel approach to derive an approximate value of the MCF MKM model parameters by knowing only the animal species and the mean number of chromosomes. The overall good agreement between MCF MKM predictions and in vitro data suggests the MCF MKM can be reliably used for the RBE calculations. In most cases, a reasonable agreement was found between the MCF MKM and the LEM IV.

The Mayo Clinic Florida microdosimetric kinetic model of clonogenic survival: formalism and first benchmark against in vitro and in silico data

Objective. To develop a new model (Mayo Clinic Florida microdosimetric kinetic model, MCF MKM) capable of accurately describing the in vitro clonogenic survival at low and high linear energy transfer (LET) using single-event microdosimetric spectra in a single target. Methodology. The MCF MKM is based on the ‘post-processing average’ implementation of the non-Poisson microdosimetric kinetic model and includes a novel expression to compute the particle-specific quadratic-dependence of the cell survival with respect to dose (β of the linear-quadratic model). A new methodology to a priori calculate the mean radius of the MCF MKM subnuclear domains is also introduced. Lineal energy spectra were simulated with the Particle and Heavy Ion Transport code System (PHITS) for 1H, 4He, 12C, 20Ne, 40Ar, 56Fe, and 132Xe ions and used in combination with the MCF MKM to calculate the ion-specific LET-dependence of the relative biological effectiveness (RBE) for Chinese hamster lung fibroblasts (V79 cell line) and human salivary gland tumor cells (HSG cell line). The results were compared with in vitro data from the Particle Irradiation Data Ensemble (PIDE) and in silico results of different models. The possibility of performing experiment-specific predictions to explain the scatter in the in vitro RBE data was also investigated. Finally, a sensitivity analysis on the model parameters is also included. Main results. The RBE values predicted with the MCF MKM were found to be in good agreement with the in vitro data for all tested conditions. Though all MCF MKM model parameters were determined a priori, the accuracy of the MCF MKM was found to be comparable or superior to that of other models. The model parameters determined a priori were in good agreement with the ones obtained by fitting all available in vitro data. Significance. The MCF MKM will be considered for implementation in cancer radiotherapy treatment planning with accelerated ions.

Comprehensive comparison of local effect model IV predictions with the particle irradiation data ensemble

PURPOSE

The increased relative biological effectiveness (RBE) of ions is one of the key benefits of ion radiotherapy compared to conventional radiotherapy with photons. To account for the increased RBE of ions during the process of ion radiotherapy treatment planning, a robust model for RBE predictions is indispensable. Currently, at several ion therapy centers the local effect model I (LEM I) is applied to predict the RBE, which varies with biological and physical impacting factors. After the introduction of LEM I, several model improvements were implemented, leading to the current version, LEM IV, which is systematically tested in this study.

METHODS

As a comprehensive RBE model should give consistent results for a large variety of ion species and energies, the particle irradiation data ensemble (PIDE) is used to systematically validate the LEM IV. The database covers over 1100 photon and ion survival experiments in form of their linear-quadratic parameters for a wide range of ion types and energies. This makes the database an optimal tool to challenge the systematic dependencies of the RBE model. After appropriate filtering of the database, 571 experiments were identified and used as test data.

RESULTS

The study confirms that the LEM IV reflects the RBE systematics observed in measurements well. It is able to reproduce the dependence of RBE on the linear energy transfer (LET) as well as on the α_γ /β_γ ratio for several ion species in a wide energy range. Additionally, the systematic quantitative analysis revealed precision capabilities and limits of the model. At lower LET values, the LEM IV tends to underestimate the RBE with an increasing underestimation with increasing atomic number of the ion. At higher LET values, the LEM IV overestimates the RBE for protons or helium ions, whereas the predictions for heavier ions match experimental data well.

CONCLUSIONS

The LEM IV is able to predict general RBE characteristics for several ion species in a broad energy range. The accuracy of the predictions is reasonable considering the small number of input parameters needed by the model. The detailed quantification of possible systematic deviations, however, enables to identify not only strengths but also limitations of the model. The gained knowledge can be used to develop model adjustments to further improve the model accuracy, which is on the way.

A systematic review on the usage of averaged LET in radiation biology for particle therapy

Linear Energy Transfer (LET) is widely used to express the radiation quality of ion beams, when characterizing the biological effectiveness. However, averaged LET may be defined in multiple ways, and the chosen definition may impact the resulting reported value. We review averaged LET definitions found in the literature, and quantify which impact using these various definitions have for different reference setups. We recorded the averaged LET definitions used in 354 publications quantifying the relative biological effectiveness (RBE) of hadronic beams, and investigated how these various definitions impact the reported averaged LET using a Monte Carlo particle transport code. We find that the kind of averaged LET being applied is, generally, poorly defined. Some definitions of averaged LET may influence the reported averaged LET values up to an order of magnitude. For publications involving protons, most applied dose averaged LET when reporting RBE. The absence of what target medium is used and what secondary particles are included further contributes to an ill-defined averaged LET. We also found evidence of inconsistent usage of averaged LET definitions when deriving LET-based RBE models. To conclude, due to commonly ill-defined averaged LET and to the inherent problems of LET-based RBE models, averaged LET may only be used as a coarse indicator of radiation quality. We propose a more rigorous way of reporting LET values, and suggest that ideally the entire particle fluence spectra should be recorded and provided for future RBE studies, from which any type of averaged LET (or other quantities) may be inferred.

Rapid effective dose calculation for raster-scanning 4He ion therapy with the modified microdosimetric kinetic model (mMKM)

PURPOSE

To develop and verify effective dose (D_RBE) calculation in ⁴He ion beam therapy based on the modified microdosimetric kinetic model (mMKM) and evaluate the bio-sensitivity of mMKM-based plans to clinical parameters using a fast analytical dose engine.

METHODS

Mixed radiation field particle spectra (MRFS) databases have been generated with Monte-Carlo (MC) simulations for ⁴He-ion beams. Relative biological effectiveness (RBE) and D_RBE calculation using MRFS were established within a fast analytical engine. Spread-out Bragg-Peaks (SOBPs) in water were optimized for two dose levels and two tissue types with photon linear-quadratic model parameters α_ph, β_ph, and (α/β)_ph to verify MRFS-derived database implementation against computations with MC-generated mixed-field α and β databases. Bio-sensitivity of the SOBPs was investigated by varying absolute values of β_ph, while keeping (α/β)_ph constant. Additionally, dose, dose-averaged linear energy transfer, and bio-sensitivity were investigated for two patient cases.

RESULTS

Using MRFS-derived databases, dose differences ≲2% in the plateau and SOBP are observed compared to computations with MC-generated databases. Bio-sensitivity studies show larger deviations when altering the absolute β_ph value, with maximum D_50% changes of ~5%, with similar results for patient cases. Bio-sensitivity analysis indicates a greater impact on D_RBE varying (α/β)_ph than β_ph in mMKM.

CONCLUSIONS

The MRSF approach yielded negligible differences in the target and small differences in the plateau compared to MC-generated databases. The presented analyses provide guidance for proper implementation of RBE-weighted ⁴He ion dose prescription and planning with mMKM. The MRFS-D_RBE calculation approach using mMKM will be implemented in a clinical treatment planning system.

Assessment of RBE-Weighted Dose Models for Carbon Ion Therapy Toward Modernization of Clinical Practice at HIT: In Vitro, in Vivo, and in Patients

PURPOSE

Present-day treatment planning in carbon ion therapy is conducted with assumptions for a limited number of tissue types and models for effective dose. Here, we comprehensively assess relative biological effectiveness (RBE) in carbon ion therapy and associated models toward the modernization of current clinical practice in effective dose calculation.

METHODS

Using 2 human (A549, H460) and 2 mouse (B16, Renca) tumor cell lines, clonogenic cell survival assay was performed for examination of changes in RBE along the full range of clinical-like spread-out Bragg peak (SOBP) fields. Prediction power of the local effect model (LEM1 and LEM4) and the modified microdosimetric kinetic model (mMKM) was assessed. Experimentation and analysis were carried out in the frame of a multidimensional end point study for clinically relevant ranges of physical dose (D), dose-averaged linear energy transfer (LET_d), and base-line photon radio-sensitivity (α/β)_x. Additionally, predictions were compared against previously reported RBE measurements in vivo and surveyed in patient cases.

RESULTS

RBE model prediction performance varied among the investigated perspectives, with mMKM prediction exhibiting superior agreement with measurements both in vitro and in vivo across the 3 investigated end points. LEM1 and LEM4 performed their best in the highest LET conditions but yielded overestimations and underestimations in low/midrange LET conditions, respectively, as demonstrated by comparison with measurements. Additionally, the analysis of patient treatment plans revealed substantial variability across the investigated models (±20%-30% uncertainty), largely dependent on the selected model and absolute values for input tissue parameters α_x and β_x.

CONCLUSION

RBE dependencies in vitro, in vivo, and in silico were investigated with respect to various clinically relevant end points in the context of tumor-specific tissue radio-sensitivity assignment and accurate RBE modeling. Discovered model trends and performances advocate upgrading current treatment planning schemes in carbon ion therapy and call for verification via clinical outcome analysis with large patient cohorts.

Radiation biology considerations of proton therapy for gastrointestinal cancers

Clinical enthusiasm for proton therapy (PT) is high, with an exponential increase in the number of centers offering treatment. Attraction for this charged particle therapy modality stems from the favorable proton dose distribution, with low radiation dose absorption on entry and maximum radiation deposition at the Bragg peak. The current clinical convention is to use a fixed relative biological effectiveness (RBE) value of 1.1 in order to correct the physical dose relative to photon therapy (i.e., proton radiation is 10% more biologically effective then photon radiation). In recent years, concerns about the potential side effects of PT have emerged. Various studies and review articles have sought to better quantify the RBE of PT and shine some light on the complexity of this problem. Reduction in biologic hot spots of non-target tissue is paramount in proton radiation therapy (RT) planning as the primary benefit of proton RT is a reduction in organ at risk (OAR) irradiation. New and emerging clinical data is in support of variable proton biological effectiveness and demonstrate late toxicity, presumably associated with high biological dose, to OAR. Overall, PT has promise to treat many cancer sites with similar efficacy as conventional RT but with fewer acute and late toxicities. However, further knowledge of biologic effective dose and its impact on both cancer and adjacent OAR is paramount for effective and safe treatment of patients with PT.

Biophysical modeling and experimental validation of relative biological effectiveness (RBE) for 4He ion beam therapy

BACKGROUND

Helium (4He) ion beam therapy provides favorable biophysical characteristics compared to currently administered particle therapies, i.e., reduced lateral scattering and enhanced biological damage to deep-seated tumors like heavier ions, while simultaneously lessened particle fragmentation in distal healthy tissues as observed with lighter protons. Despite these biophysical advantages, raster-scanning 4He ion therapy remains poorly explored e.g., clinical translational is hampered by the lack of reliable and robust estimation of physical and radiobiological uncertainties. Therefore, prior to the upcoming 4He ion therapy program at the Heidelberg Ion-beam Therapy Center (HIT), we aimed to characterize the biophysical phenomena of 4He ion beams and various aspects of the associated models for clinical integration.

METHODS

Characterization of biological effect for 4He ion beams was performed in both homogenous and patient-like treatment scenarios using innovative models for estimation of relative biological effectiveness (RBE) in silico and their experimental validation using clonogenic cell survival as the gold-standard surrogate. Towards translation of RBE models in patients, the first GPU-based treatment planning system (non-commercial) for raster-scanning 4He ion beams was devised in-house (FRoG).

RESULTS

Our data indicate clinically relevant uncertainty of ±5-10% across different model simulations, highlighting their distinct biological and computational methodologies. The in vitro surrogate for highly radio-resistant tissues presented large RBE variability and uncertainty within the clinical dose range.

CONCLUSIONS

Existing phenomenological and mechanistic/biophysical models were successfully integrated and validated in both Monte Carlo and GPU-accelerated analytical platforms against in vitro experiments, and tested using pristine peaks and clinical fields in highly radio-resistant tissues where models exhibit the greatest RBE uncertainty. Together, these efforts mark an important step towards clinical translation of raster-scanning 4He ion beam therapy to the clinic.

Report of the AAPM TG-256 on the relative biological effectiveness of proton beams in radiation therapy

The biological effectiveness of proton beams relative to photon beams in radiation therapy has been taken to be 1.1 throughout the history of proton therapy. While potentially appropriate as an average value, actual relative biological effectiveness (RBE) values may differ. This Task Group report outlines the basic concepts of RBE as well as the biophysical interpretation and mathematical concepts. The current knowledge on RBE variations is reviewed and discussed in the context of the current clinical use of RBE and the clinical relevance of RBE variations (with respect to physical as well as biological parameters). The following task group aims were designed to guide the current clinical practice: Assess whether the current clinical practice of using a constant RBE for protons should be revised or maintained. Identifying sites and treatment strategies where variable RBE might be utilized for a clinical benefit. Assess the potential clinical consequences of delivering biologically weighted proton doses based on variable RBE and/or LET models implemented in treatment planning systems. Recommend experiments needed to improve our current understanding of the relationships among in vitro, in vivo, and clinical RBE, and the research required to develop models. Develop recommendations to minimize the effects of uncertainties associated with proton RBE for well-defined tumor types and critical structures.

A comparison of mechanism-inspired models for particle relative biological effectiveness (RBE)

BACKGROUND AND SIGNIFICANCE

The application of heavy ion beams in cancer therapy must account for the increasing relative biological effectiveness (RBE) with increasing penetration depth when determining dose prescriptions and organ at risk (OAR) constraints in treatment planning. Because RBE depends in a complex manner on factors such as the ion type, energy, cell and tissue radiosensitivity, physical dose, biological endpoint, and position within and outside treatment fields, biophysical models reflecting these dependencies are required for the personalization and optimization of treatment plans.

AIM

To review and compare three mechanism-inspired models which predict the complexities of particle RBE for various ion types, energies, linear energy transfer (LET) values and tissue radiation sensitivities.

METHODS

The review of models and mechanisms focuses on the Local Effect Model (LEM), the Microdosimetric-Kinetic (MK) model, and the Repair-Misrepair-Fixation (RMF) model in combination with the Monte Carlo Damage Simulation (MCDS). These models relate the induction of potentially lethal double strand breaks (DSBs) to the subsequent interactions and biological processing of DSB into more lethal forms of damage. A key element to explain the increased biological effectiveness of high LET ions compared to MV x rays is the characterization of the number and local complexity (clustering) of the initial DSB produced within a cell. For high LET ions, the spatial density of DSB induction along an ion's trajectory is much greater than along the path of a low LET electron, such as the secondary electrons produced by the megavoltage (MV) x rays used in conventional radiation therapy. The main aspects of the three models are introduced and the conceptual similarities and differences are critiqued and highlighted. Model predictions are compared in terms of the RBE for DSB induction and for reproductive cell survival.

RESULTS AND CONCLUSIONS

Comparisons of the RBE for DSB induction and for cell survival are presented for proton (¹ H), helium (⁴ He), and carbon (¹² C) ions for the therapeutically most relevant range of ion beam energies. The reviewed models embody mechanisms of action acting over the spatial scales underlying the biological processing of potentially lethal DSB into more lethal forms of damage. Differences among the number and types of input parameters, relevant biological targets, and the computational approaches among the LEM, MK and RMF models are summarized and critiqued. Potential experiments to test some of the seemingly contradictory aspects of the models are discussed.

Sensitivity study of the microdosimetric kinetic model parameters for carbon ion radiotherapy

In carbon ion therapy treatment planning, the relative biological effectiveness (RBE) is accounted for by optimization of the RBE-weighted dose (biological dose). The RBE calculation methods currently applied clinically in carbon ion therapy are derived from the microdosimetric kinetic model (MKM) in Japan and the local effect model (LEM) in Europe. The input parameters of these models are based on fit to experimental data subjected to uncertainties. We therefore performed a sensitivity study of the MKM input parameters, i.e. the domain radius (r _d ), the nucleus radius (R _n ) and the parameters of the linear quadratic (LQ) model (α _x and β). The study was performed with the FLUKA Monte Carlo code, using spread out Bragg peak (SOBP) scenarios in water and a biological dose distribution in a clinical patient case. Comparisons were done between biological doses estimated applying the MKM with parameters based on HSG cells, and with HSG parameters varied separately by  ±{5, 25, 50}%. Comparisons were also done between parameter sets from different cell lines (HSG, V79, CHO and T1), as well as versions of the LEM. Of the parameters, r _d had the largest impact on the biological dose distribution, especially on the absolute dose values. Increasing this parameter by 25% decreased the biological dose level at the center of a 3 Gy(RBE) SOBP by 14%. Variations in R _n only influenced the biological dose distribution towards the particle range, and variations in α _x resulted in minor changes in the biological dose, with an increasing impact towards the particle range. β had the overall smallest influence on the SOBPs, but the impact could become more pronounced if alternative (LET dependent) implementations are used. The resulting percentage change in the SOBPs was generally less than the percentage change in the parameters. The patient case showed similar effects as with the SOBPs in water, and parameter variations had similar impact on the biological dose when using the clinical MKM and the general MKM. The clinical LEM calculated the highest biological doses to both tumor and surrounding healthy tissues, with a median target dose (D _50%) of 40.5 Gy(RBE), while the MKM with HSG and V79 parameters resulted in a D _50% of 34.2 and 36.9 Gy(RBE), respectively. In all, the observed change in biological dose distribution due to parameter variations demonstrates the importance of accurate input parameters when applying the MKM in treatment planning.

Investigating Dependencies of Relative Biological Effectiveness for Proton Therapy in Cancer Cells

Purpose

Relative biological effectiveness (RBE) accounts for the differences in biological effect from different radiation types. The RBE for proton therapy remains uncertain, as it has been shown to vary from the clinically used value of 1.1. In this work we investigated the RBE of protons and correlated the biological differences with the underlying physical quantities.

Materials and Methods

Three cell lines were irradiated (CHO, Chinese hamster ovary; A549, human lung adenocarcinoma; and T98, human glioma) and assessed for cell survival by using clonogenic assay. Cells were irradiated with 71- and 160-MeV protons at depths along the Bragg curve and 6-MV photons to various doses. The dose-averaged lineal energy ( y‒D ) was measured under similar conditions as the cells by using a microdosimeter. Dose-averaged linear energy transfer (LET_d) was also calculated by using Monte Carlo (MC) simulations. Survival data were fit by using the linear quadratic model. The RBE values were calculated by comparing the physical dose (D_6MV/D_p) that results in 50% (RBE_0.5) and 10% (RBE_0.1) cell survival, and survival after 2 Gy (RBE_2Gy).

Results

Proton RBE values ranged from 0.89 to 2.40. The RBE for all 3 cell lines increased with decreasing proton energy and was higher at 50% survival than at 10% survival. Additionally, both A549 and T98 cells generally had higher RBE values relative to the CHO cells, indicating a greater biological response to protons. An increase in RBE corresponded with an increase in y‒D and LET_d.

Conclusion

Proton RBE was found to depend on mean proton energy, survival end point, and cell type. Changes in both y‒D and LET_d were also found to impact proton RBE values, but consideration of the energy spectrum may provide additional information. The RBE values in this study vary greatly, indicating the clinical value of 1.1 may not be suitable in all cases.

First proof of bismuth oxide nanoparticles as efficient radiosensitisers on highly radioresistant cancer cells

This study provides the first proof of the novel application of bismuth oxide as a radiosensitiser. It was shown that on the highly radioresistant 9L gliosarcoma cell line, bismuth oxide nanoparticles sensitise to both kilovoltage (kVp) or megavoltage (MV) X-rays radiation. 9L cells were exposed to a concentration of 50μg.mL^-1 of nanoparticle before irradiation at 125kVp and 10MV. Sensitisation enhancement ratios of 1.48 and 1.25 for 125kVp and 10MV were obtained in vitro, respectively. The radiation enhancement of the nanoparticles is postulated to be a combination of the high Z nature of the bismuth (Z=83), and the surface chemistry. Monte Carlo simulations were performed to elucidate the physical interactions between the incident radiation and the nanoparticle. The results of this work show that Bi₂O₃ nanoparticles increase the radiosensitivity of 9L gliosarcoma tumour cells for both kVp and MV energies. Monte Carlo simulations demonstrate the advantage of a platelet morphology.

Effects of Charged Particles on Human Tumor Cells

The use of charged particle therapy in cancer treatment is growing rapidly, in large part because the exquisite dose localization of charged particles allows for higher radiation doses to be given to tumor tissue while normal tissues are exposed to lower doses and decreased volumes of normal tissues are irradiated. In addition, charged particles heavier than protons have substantial potential clinical advantages because of their additional biological effects, including greater cell killing effectiveness, decreased radiation resistance of hypoxic cells in tumors, and reduced cell cycle dependence of radiation response. These biological advantages depend on many factors, such as endpoint, cell or tissue type, dose, dose rate or fractionation, charged particle type and energy, and oxygen concentration. This review summarizes the unique biological advantages of charged particle therapy and highlights recent research and areas of particular research needs, such as quantification of relative biological effectiveness (RBE) for various tumor types and radiation qualities, role of genetic background of tumor cells in determining response to charged particles, sensitivity of cancer stem-like cells to charged particles, role of charged particles in tumors with hypoxic fractions, and importance of fractionation, including use of hypofractionation, with charged particles.

Analytical linear energy transfer model including secondary particles: calculations along the central axis of the proton pencil beam

In proton therapy, the relative biological effectiveness (RBE) depends on various types of parameters such as linear energy transfer (LET). An analytical model for LET calculation exists (Wilkens' model), but secondary particles are not included in this model. In the present study, we propose a correction factor, L sec, for Wilkens' model in order to take into account the LET contributions of certain secondary particles. This study includes secondary protons and deuterons, since the effects of these two types of particles can be described by the same RBE-LET relationship. L sec was evaluated by Monte Carlo (MC) simulations using the GATE/GEANT4 platform and was defined by the ratio of the LET d distributions of all protons and deuterons and only primary protons. This method was applied to the innovative Pencil Beam Scanning (PBS) delivery systems and L sec was evaluated along the beam axis. This correction factor indicates the high contribution of secondary particles in the entrance region, with L sec values higher than 1.6 for a 220 MeV clinical pencil beam. MC simulations showed the impact of pencil beam parameters, such as mean initial energy, spot size, and depth in water, on L sec. The variation of L sec with these different parameters was integrated in a polynomial function of the L sec factor in order to obtain a model universally applicable to all PBS delivery systems. The validity of this correction factor applied to Wilkens' model was verified along the beam axis of various pencil beams in comparison with MC simulations. A good agreement was obtained between the corrected analytical model and the MC calculations, with mean-LET deviations along the beam axis less than 0.05 keV μm(-1). These results demonstrate the efficacy of our new correction of the existing LET model in order to take into account secondary protons and deuterons along the pencil beam axis.

Simulation of DSB yield for high LET radiation

A simulation approach for the calculation of the LET-dependent yield of double-strand breaks (DSB) is presented. The model considers DSB formed as two close-lying single-strand breaks (SSB), whose formation is mediated by both intra-track processes (single electrons) or at local doses larger than about 1000 Gy in particle tracks also by electron inter-track processes (two independent electron tracks). A Monte Carlo algorithm and an analytical formula for the DSB yield are presented. The approach predicts that the DSB yield is enhanced after charged particle irradiation of high LET compared with X-ray or gamma radiation. It is used as an inherent part of the local effect model, which is applied to estimate the relative biological effectiveness of high LET radiation.

Variation in RBE for Survival of V79-4 Cells as a Function of Alpha-Particle (Helium Ion) Energy

High linear energy transfer (LET) α particles are important with respect to the carcinogenic risk associated with human exposure to ionizing radiation, most notably to radon and its progeny. Additionally, the potential use of alpha-particle-emitting radionuclides in radiotherapy is increasingly being explored. Within the body the emitted alpha particles slow down, traversing a number of cells with a range of energies and therefore with varying efficiencies at inducing biological response. The LET of the particle typically rises from between ~70-90 keV μm(-1) at the start of the track (depending on initial energy) to a peak of ~237 keV μm(-1) towards the end of the track, before falling again at the very end of its range. To investigate the variation in biological response with incident energy, a plutonium-238 alpha-particle irradiator was calibrated to enable studies with incident energies ranging from 4.0 MeV down to 1.1 MeV. The variation in clonogenic survival of V79-4 cells was determined as a function of incident energy, along with the relative variation in the initial yields of DNA double-strand breaks (DSB) measured using the FAR assay. The clonogenic survival data also extends previously published data obtained at the Medical Research Council (MRC), Harwell using the same cells irradiated with helium ions, with energies ranging from 34.9 MeV to 5.85 MeV. These studies were performed in conjunction with cell morphology measurements on live cells enabling the determination of absorbed dose and calculation of the average LET in the cell. The results show an increase in relative biological effectiveness (RBE) for cell inactivation with decreasing helium ion energy (increasing LET), reaching a maximum for incident energies of ~3.2 MeV and corresponding average LET of 131 keV μm(-1), above which the RBE is observed to fall at lower energies (higher LETs). The effectiveness of single alpha-particle traversals (relevant to low-dose exposure) at inducing cell inactivation was observed to increase with decreasing energy to a peak of ~68% survival probability for incident energies of ~1.8 MeV (average LET of 190 keV μm(-1)) producing ~0.39 lethal lesions per track. However, the efficiency of a single traversal will also vary significantly with cell morphology and angle of incidence, as well as cell type.

Reirradiation Using Carbon Ions in Patients with Locally Recurrent Rectal Cancer at HIT: First Results

BACKGROUND

Locally recurrent rectal cancer remains a dreaded event because curative resection is unlikely to be performed in a large number of cases. Carbon ion radiotherapy offers physical and biologic advantages. A high precise local dose deposition and sparing of normal tissue is possible. This work summarizes our experience on feasibility and early toxicity of carbon ion radiotherapy in previously irradiated and operated patients.

METHODS

Between 2010 and 2013, a total of 19 patients with a median age of 62 years (range 14-76 years) received carbon ion irradiation to treat locally recurrent rectal cancer at the Heidelberg Ion Beam Therapy Center (HIT). All patients had a history of surgery and pelvic radiotherapy of at least 50.4 Gy. Median dose was 36 Gy [relative biologic efficacy (RBE)] [range 36-51 Gy(RBE)], and median planning target volume was 456 ml (range 75-1,597 ml). Some patients were treated in the recruiting phase I/II of the PANDORA study (NCT01528683).

RESULTS

Median follow-up was 7.8 months. Four patients were diagnosed with local relapse after carbon ion radiotherapy, and three patients developed distant metastases. Estimated mean local progression-free survival was 20.6 months by the Kaplan-Meier estimator. Two patients had preexisting rectovaginal fistula, and another patient had a preexisting presacral localized abscess formation in which the local relapse took place. No grade III or higher toxicities were observed.

CONCLUSIONS

Our first experiences in a pretreated patient group with a dismal prognosis are encouraging, and therapy-related side effects are mild. Longer follow-up is required to determine possible late effects and long-term disease control.

Direct evaluation of radiobiological parameters from clinical data in the case of ion beam therapy: an alternative approach to the relative biological effectiveness

The relative biological effectiveness (RBE) concept is commonly used in treatment planning for ion beam therapy. Whether models based on in vitro/in vivo RBE data can be used to predict human response to treatments is an open issue. In this work an alternative method, based on an effective radiobiological parameterization directly derived from clinical data, is presented. The method has been applied to the analysis of prostate cancer trials with protons and carbon ions.Prostate cancer trials with proton and carbon ion beams reporting 5 year-local control (LC5) and grade 2 (G2) or higher genitourinary toxicity rates (TOX) were selected from literature to test the method. Treatment simulations were performed on a representative subset of patients to produce dose and linear energy transfer distribution, which were used as explicative physical variables for the radiobiological modelling. Two models were taken into consideration: the microdosimetric kinetic model (MKM) and a linear model (LM). The radiobiological parameters of the LM and MKM were obtained by coupling them with the tumor control probability and normal tissue complication probability models to fit the LC5 and TOX data through likelihood maximization. The model ranking was based on the Akaike information criterion.Results showed large confidence intervals due to the limited variety of available treatment schedules. RBE values, such as RBE = 1.1 for protons in the treated volume, were derived as a by-product of the method, showing a consistency with current approaches. Carbon ion RBE values were also derived, showing lower values than those assumed for the original treatment planning in the target region, whereas higher values were found in the bladder. Most importantly, this work shows the possibility to infer the radiobiological parametrization for proton and carbon ion treatment directly from clinical data.

New challenges in high-energy particle radiobiology

Densely ionizing radiation has always been a main topic in radiobiology. In fact, α-particles and neutrons are sources of radiation exposure for the general population and workers in nuclear power plants. More recently, high-energy protons and heavy ions attracted a large interest for two applications: hadrontherapy in oncology and space radiation protection in manned space missions. For many years, studies concentrated on measurements of the relative biological effectiveness (RBE) of the energetic particles for different end points, especially cell killing (for radiotherapy) and carcinogenesis (for late effects). Although more recently, it has been shown that densely ionizing radiation elicits signalling pathways quite distinct from those involved in the cell and tissue response to photons. The response of the microenvironment to charged particles is therefore under scrutiny, and both the damage in the target and non-target tissues are relevant. The role of individual susceptibility in therapy and risk is obviously a major topic in radiation research in general, and for ion radiobiology as well. Particle radiobiology is therefore now entering into a new phase, where beyond RBE, the tissue response is considered. These results may open new applications for both cancer therapy and protection in deep space.