Rapid calculation of biological effects in ion radiotherapy

Deep learning-based voxel sampling for particle therapy treatment planning

Objective.Scanned particle therapy often requires complex treatment plans, robust optimization, as well as treatment adaptation. Plan optimization is especially complicated for heavy ions due to the variable relative biological effectiveness. We present a novel deep-learning model to select a subset of voxels in the planning process thus reducing the planning problem size for improved computational efficiency.Approach.Using only a subset of the voxels in target and organs at risk (OARs) we produced high-quality treatment plans, but heuristic selection strategies require manual input. We designed a deep-learning model based onP-Net to obtain an optimal voxel sampling without relying on patient-specific user input. A cohort of 70 head and neck patients that received carbon ion therapy was used for model training (50), validation (10) and testing (10). For training, a total of 12 500 carbon ion plans were optimized, using a highly efficient artificial intelligence (AI) infrastructure implemented into a research treatment planning platform. A custom loss function increased sampling density in underdosed regions, while aiming to reduce the total number of voxels.Main results.On the test dataset, the number of voxels in the optimization could be reduced by 84.8% (median) at <1% median loss in plan quality. When the model was trained to reduce sampling in the target only while keeping all voxels in OARs, a median reduction up to 71.6% was achieved, with 0.5% loss in the plan quality. The optimization time was reduced by a factor of 7.5 for the total AI selection model and a factor of 3.7 for the model with only target selection.Significance.The novel deep-learning voxel sampling technique achieves a significant reduction in computational time with a negligible loss in the plan quality. The reduction in optimization time can be especially useful for future real-time adaptation strategies.

Biological dose optimization incorporating intra-tumoural cellular radiosensitivity heterogeneity in ion-beam therapy treatment planning

Objective.Treatment plans of ion-beam therapy have been made under an assumption that all cancer cells within a tumour equally respond to a given radiation dose. However, an intra-tumoural cellular radiosensitivity heterogeneity clearly exists, and it may lead to an overestimation of therapeutic effects of the radiation. The purpose of this study is to develop a biological model that can incorporate the radiosensitivity heterogeneity into biological optimization for ion-beam therapy treatment planning.Approach.The radiosensitivity heterogeneity was modeled as the variability of a cell-line specific parameter in the microdosimetric kinetic model following the gamma distribution. To validate the developed intra-tumoural-radiosensitivity-heterogeneity-incorporated microdosimetric kinetic (HMK) model, a treatment plan with H-ion beams was made for a chordoma case, assuming a radiosensitivity heterogeneous region within the tumour. To investigate the effects of the radiosensitivity heterogeneity on the biological effectiveness of H-, He-, C-, O-, and Ne-ion beams, the relative biological effectiveness (RBE)-weighted dose distributions were planned for a cuboid target with the stated ion beams without considering the heterogeneity. The planned dose distributions were then recalculated by taking the heterogeneity into account.Main results. The cell survival fraction and corresponding RBE-weighted dose were formulated based on the HMK model. The first derivative of the RBE-weighted dose distribution was also derived, which is needed for fast biological optimization. For the patient plan, the biological optimization increased the dose to the radiosensitivity heterogeneous region to compensate for the heterogeneity-induced reduction in biological effectiveness of the H-ion beams. The reduction in biological effectiveness due to the heterogeneity was pronounced for low linear energy transfer (LET) beams but moderate for high-LET beams. The RBE-weighted dose in the cuboid target decreased by 7.6% for the H-ion beam, while it decreased by just 1.4% for the Ne-ion beam.Significance.Optimal treatment plans that consider intra-tumoural cellular radiosensitivity heterogeneity can be devised using the HMK model.

Modeling of the resensitization effect on carbon-ion radiotherapy for stage I non-small cell lung cancer

Objective. To investigate the effect of redistribution and reoxygenation on the 3-year tumor control probability (TCP) of patients with stage I non-small cell lung cancer (NSCLC) treated with carbon-ion radiotherapy.Approach. A meta-analysis of published clinical data of 233 NSCLC patients treated by carbon-ion radiotherapy under 18-, 9-, 4-, and single-fraction schedules was conducted. The linear-quadratic (LQ)-based cell-survival model incorporating the radiobiological 5Rs, radiosensitivity, repopulation, repair, redistribution, and reoxygenation, was developed to reproduce the clinical TCP data. Redistribution and reoxygenation were regarded together as a single phenomenon and termed 'resensitization' in the model. The optimum interval time between fractions was investigated for each fraction schedule using the determined model parameters.Main results.The clinical TCP data for 18-, 9-, and 4-fraction schedules were reasonably reproduced by the model without the resensitization effect, whereas its incorporation was essential to reproduce the TCP data for all fraction schedules including the single fraction. The curative dose for the single-fraction schedule was estimated to be 49.0 Gy (RBE), which corresponds to the clinically adopted dose prescription of 50.0 Gy (RBE). For 18-, 9-, and 4-fraction schedules, a 2-to-3-day interval is required to maximize the resensitization effect during the time interval. In contrast, the single-fraction schedule cannot benefit from the resensitization effect, and the shorter treatment time is preferable to reduce the effect of sub-lethal damage repair during the treatment.Significance.The LQ-based cell-survival model incorporating the radiobiological 5Rs was developed and used to evaluate the effect of the resensitization on clinical results of NSCLC patients treated with hypo-fractionated carbon-ion radiotherapy. The incorporation of the resensitization into the cell-survival model improves the reproducibility to the clinical TCP data. A shorter treatment time is preferable in the single-fraction schedule, while a 2-to-3-day interval between fractions is preferable in the multi-fraction schedules for effective treatments.

Effects of nuclear interaction corrections and trichrome fragment spectra modelling on dose and linear energy transfer distributions in carbon ion radiotherapy

Background and Purpose

Nuclear interaction correction (NIC) and trichrome fragment spectra modelling improve relative biological effectiveness-weighted dose (DRBE) and dose-averaged linear energy transfer (LETd) calculation for carbon ions. The effect of those novel approaches on the clinical dose and LET distributions was investigated.

Materials and Methods

The effect of the NIC and trichrome algorithm was assessed, creating single beam plans for a virtual water phantom with standard settings and NIC + trichrome corrections. Reference DRBE and LETd distributions were simulated using FLUKA version 2021.2.9. Thirty clinically applied scanned carbon ion treatment plans were recalculated applying NIC, trichrome and NIC + trichrome corrections, using the LEM low dose approximation and compared to clinical plans (base RS). Four treatment sites were analysed: six prostate adenocarcinoma, ten head and neck, nine locally advanced pancreatic adenocarcinoma and five sacral chordoma. The FLUKA and clinical plans were compared in terms of DRBE deviations for D98%, D50%, D2% for the clinical target volume (CTV) and D50% in ring-like dose regions retrieved from isodose curves in base RS plans. Additionally, region-based median LETd deviations and global gamma parameters were evaluated.

Results

Dose deviations comparing base RS and evaluation plans were within ± 1% supported by γ-pass rates over 97% for all cases. No significant LETd deviations were reported in the CTV, but significant median LETd deviations were up to 80% for very low dose regions.

Conclusion

Our results showed improved accuracy of the predicted DRBE and LETd. Considering clinically relevant constraints, no significant modifications of clinical protocols are expected with the introduction of NIC + trichrome.

Computational approaches in the estimation of radiobiological damage for human-malignant cells irradiated with clinical proton and carbon beams

PURPOSE

The use of Monte Carlo (MC) simulations capable of reproducing radiobiological effects of ionising radiation on human cell lines is of great importance, especially for cases involving protons and heavier ion beams. In the latter, huge uncertainties can arise mainly related to the effects of the secondary particles produced in the beam-tissue interaction. This paper reports on a detailed MC study performed using Geant4-based approach on three cancer cell lines, the HTB-177, CRL-5876 and MCF-7, that were previously irradiated with therapeutic proton and carbon ion beams.

METHODS

A Geant4-based approach used jointly with analytical calculations has been developed to provide a more realistic estimation of the radiobiological damage produced by proton and carbon beams in tissues, reproducing available data obtained from in vitro cell irradiations. The MC "Hadrontherapy" Geant4 application and the Local Effect Model: LEM I, LEM II and LEM III coupled with the different numerical approaches: RapidRusso (RR) and RapidScholz (RS) were used in the study.

RESULTS

Experimental survival curves are compared with those evaluated using the highlighted Geant4 MC-based approach via chi-square statistical analysis, for the combinations of radiobiological models and numerical approaches, as outlined above.

CONCLUSION

This study has presented a comparison of the survival data from MC simulations to experimental survival data for three cancer cell lines. An overall best level of agreement was obtained for the HTB-177 cells.

Evaluation of proton and carbon ion beam models in TReatment Planning for Particles 4D (TRiP4D) referring to a commercial treatment planning system

PURPOSE

To investigate the accuracy of the treatment planning system (TPS) TRiP4D in reproducing doses computed by the clinically used TPS SyngoRT.

METHODS

Proton and carbon ion beam models in TRiP4D were converted from SyngoRT. Cubic plans with different depths in a water-tank phantom (WP) and previously treated and experimentally verified patient plans from SyngoRT were recalculated in TRiP4D. The target mean dose deviation (ΔD_mean,T) and global gamma index (2%-2 mm for the absorbed dose and 3%-3mm for the RBE-weighted dose with 10% threshold) were evaluated.

RESULTS

The carbon and proton absorbed dose gamma passing rates (γ-PRs) were ≥99.93% and ΔD_mean,T smaller than -0.22%. On average, the RBE-weighted dose D_mean,T was -1.26% lower for TRiP4D than SyngoRT for cubic plans. In TRiP4D, the faster analytical 'low dose approximation' (Krämer, 2006) was used, while SyngoRT used a stochastic implementation (Krämer, 2000). The average ΔD_{mean, T} could be reduced to -0.59% when applying the same biological effect calculation algorithm. However, the dose recalculation time increased by a factor of 79-477. ΔD_mean,T variation up to -2.27% and -2.79% was observed for carbon absorbed and RBE-weighted doses in patient plans. The γ-PRs were ≥93.92% and ≥91.83% for patient plans, except for one proton beam with a range shifter (γ-PR of 64.19%).

CONCLUSION

The absorbed dose between TRiP4D and SyngoRT were identical for both proton and carbon ion plans in the WP. Compared to SyngoRT, TRiP4D underestimated the target RBE-weighted dose; however more efficient in RBE-weighted dose calculation. Large variation for proton beam with range shifter was observed. TRiP4D will be used to evaluate doses delivered to moving targets. Uncertainties inherent to the 4D-dose reconstruction calculation are expected to be significantly larger than the dose errors reported here. For this reason, the residual differences between TRiP4D and SyngoRT observed in this study are considered acceptable. The study was approved by the Institutional Research Board of Shanghai Proton and Heavy Ion Center (approval number SPHIC-MP-2020-04, RS).

Creation, evolution, and future challenges of ion beam therapy from a medical physicist's viewpoint (Part 2). Chapter 2. Biophysical model, treatment planning system and image guided radiotherapy

When an ion beam penetrates deeply into the body, its kinetic energy decreases, and its biological effect increases due to the change of the beam quality. To give a uniform biological effect to the target, it is necessary to reduce the absorbed dose with the depth. A bio-physical model estimating the relationship between ion beam quality and biological effect is necessary to determine the relative biological effectiveness (RBE) of the ion beam that changes with depth. For this reason, Lawrence Berkeley Laboratory, National Institute of Radiological Sciences (NIRS) and GSI have each developed their own model at the starting of the ion beam therapy. Also, NIRS developed a new model at the starting of the scanning irradiation. Although the Local Effect Model (LEM) at the GSI and the modified Microdosimetric Kinetic Model (MKM) at the NIRS, the both are currently used, can similarly predict radiation quality-induced changes in surviving fraction of cultured cell, the clinical RBE-weighted doses for the same absorbed dose are different. This is because the LEM uses X-rays as a reference for clinical RBE, whereas the modified MKM uses carbon ion beam as a reference and multiplies it by a clinical factor of 2.41. Therefore, both are converted through the absorbed dose. In PART 2, I will describe the development of such a bio-physical model, as well as the birth and evolution of a treatment planning system and image guided radiotherapy.

Formation of spread-out Bragg peak for helium-ion beam using microdosimetric kinetic model

PURPOSE

To evaluate the applicability of microdosimetric kinetic model (MKM) to helium-ion therapy by forming a spread-out Bragg peak (SOBP) of a helium-ion beam using the MKM developed for carbon-ion radiotherapy and confirming the predictions in biological experiments.

METHODS

Using a ridge filter, a 90-mm wide SOBP for a 210 MeV/u helium-ion beam was created in a broad beam delivery system. The ridge filter was designed such that a uniform biological response was achieved with a cell survival rate of 7% over the SOBP region. Biological experiments were then performed using the SOBP beam in a human salivary gland (HSG) cell line to measure the cell survival rates.

RESULTS

The biological responses were uniform in the SOBP region, as expected by the MKM; however, the mean of the measured cell survival rates was (11.2 ± 0.6) % in the SOBP region, which was 60% higher than the designed rate. When investigating the biological parameters of the HSG cell line used in the experiments, we found that they were altered slightly from the MKM parameters used for carbon-ion radiotherapy. The new β parameter reproduced the measured survival rates within 6.5% in the SOBP region.

CONCLUSION

We produced biologically uniform SOBP using MKM for carbon-ion radiotherapy. The measured survival rates in the SOBP region were higher than expected, and the survival rates were reproduced by modifying the MKM parameter. This study was limited to one SOBP, and further investigations are required to prove that MKM is generally applicable to helium-ion radiotherapy.

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.

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 gEUD₀ 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 gEUD₀ 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.

Fixed Beamline Optimization for Intensity Modulated Carbon-Ion Therapy

A major obstacle for the adoption of heavy ion therapy is the cost and technical difficulties to construct and maintain a rotational gantry. Many heavy ion treatment facilities instead choose to construct fixed beamlines as a compromise, which we propose to mitigate with optimized treatment couch angle. We formulate the integrated beam orientation and scanning spot optimization problem as a quadratic cost function with a group sparsity regularization term. The optimization problem is efficiently solved using fast iterative shrinkage-thresholding algorithm (FISTA). To test the method, we created the fixed beamline plans with couch rotation (FBCR) and without couch rotation (FB) for intensity modulated carbon-ion therapy (IMCT) and compared with the ideal scenario where both the couch and gantry have 360 degrees of freedom (GCR). FB, FBCR, and GCR IMCT plans were compared for ten pancreas cases. The FBCR plans show comparable PTV coverage and OAR doses for each pancreas case. In conclusion, the dosimetric limitation of fixed beams in heavy ion radiotherapy may be largely mitigated with integrated beam orientation optimization of the couch rotation.

Therapeutic Efficacy of Variable Biological Effectiveness of Proton Therapy in U-CH2 and MUG-Chor1 Human Chordoma Cell Death

Simple Summary

Chordoma is a rare, slow-growing cancer of the spinal cord. Photon radiation therapy and surgery are the standard of care for chordoma. Proton radiation therapy has become an increasingly common treatment in comparison to photon radiation therapy due to the ability to reduce off-target radiation dose. However, there is still an increased risk of toxicity to the surrounding critical structures that lead to poor treatment outcomes. Moreover, the biologic effectiveness of protons to sterilize chordoma cells remains uncertain and likely varies according to the proton energy spectrum throughout the proton field. We aim to investigate the tumoricidal properties of proton radiation therapy at the middle and end of the proton radiation field and elucidate variations in the relative biological effectiveness for chordoma cells. Our study helps quantify the therapeutic value of treating chordoma near the end of the proton field, where linear energy transfer is relatively high.

Abstract

Background: Chordoma is a cancer of spinal cord, skull base, and sacral area. Currently, the standard of care to treat chordoma is resection followed by radiation therapy. Since, chordoma is present in the spinal cord and these are very sensitive structures and often complete removal by surgery is not possible. As a result, chordoma has a high chance of recurrence and developing resistance to radiation therapy. In addition, treatment of chordoma by conventional radiation therapy can also damage normal tissues surrounding chordoma. Thus, current therapeutic options to treat chordoma are insufficient and novel therapies are desperately needed to treat locally advanced and metastatic chordoma. (2) Methods: In the present investigation, human chordoma cell lines of sacral origin MUG-Chor1 and U-CH2 were cultured and irradiated with Proton Beam Radiation using the clinical superconducting cyclotron and pencil-beam (active) scanning at Middle and End of the Spread-Out Bragg Peak (SOBP). Proton radiation was given at the following doses: Mug-Chor1 at 0, 1, 2, 4, and 8 Gy and U-CH2 at 0, 4, 8, 12, and 16 Gy. These doses were selected based on a pilot study in our lab and attempted to produce approximate survival fractions in the range of 1, 0.9, 0.5, 0.1, and 0.01, respectively, chosen for linear quadratic model fitting of the dose response. (3) Results: In this study, we investigated relative biological effectiveness (RBE) of proton radiation at the end of Spread Out Bragg Peak assuming that the reference radiation is a proton radiation in the middle of the SOBP. We observed differences in the survival of both Human chordoma cell lines, U-CH2 and MUG-Chor1. The data showed that there was a significantly higher cell death at the end of the Bragg peak as compared to middle of the Bragg peak. Based on the linear quadratic (LQ) fit for cell survival we calculated the RBE between M-SOBP and E-SOBP at 95% CI level and it was observed that RBE was higher than 1 at E-SOBP and caused significantly higher cell killing. Proton field at E-SOBP caused complex DNA damage in comparison to M-EOBP and the genes such as DNA topoisomerase 1, GTSE1, RAD51B were downregulated in E-SOBP treated cells. Thus, we conclude that there seems to be substantial variation in RBE (1.3–1.7) at the E-SOBP compared with the M-SOBP.

Extension of RBE-weighted 4D particle dose calculation for non-periodic motion

PURPOSE

Highly conformal scanned Carbon Ion Radiotherapy (CIRT) might permit dose escalation and improved local control in advanced stage thoracic tumors, but is challenged by target motion. Dose calculation algorithms typically assume a periodically repeating, regular motion. To assess the effect of realistic, irregular motion, new algorithms of validated accuracy are needed.

METHODS

We extended an in-house treatment planning system to calculate RBE-weighted dose distributions in CIRT on non-periodic CT image sequences. Dosimetric accuracy was validated experimentally on a moving, time-resolved ionization chamber array. Log-file based dose reconstructions were compared by gamma analysis and correlation to measurements at every intermediate detector frame during delivery. The impact of irregular motion on treatment quality was simulated on a virtual 4DCT thorax phantom. Periodic motion was compared to motion with varying amplitude and period ± baseline drift. Rescanning as a mitigation strategy was assessed on all scenarios.

RESULTS

In experimental validation, average gamma pass rates were 99.89+-0.30% for 3%/3 mm and 88.2+-2.2% for 2%/2 mm criteria. Average correlation for integral dose distributions was 0.990±0.002. Median correlation for single 200 ms frames was 0.947±0.006. In the simulations, irregular motion deteriorated V95 target coverage to 81.2%, 76.6% and 79.0% for regular, irregular motion and irregular motion with base-line drift, respectively. Rescanning restored V95 to >98% for both scenarios without baseline drift, but not with additional baseline drift at 83.7%.

CONCLUSIONS

The validated algorithm permits to study the effects of irregular motion and to develop and adapt appropriate motion mitigation techniques.

Compensating for beam modulation due to microscopic lung heterogeneities in carbon ion therapy treatment planning

PURPOSE

To predict and mitigate for the degradation in physical and biologically effective dose distributions of particle beams caused by microscopic heterogeneities in lung tissue.

MATERIALS AND METHODS

The TRiP98 treatment planning system was adapted to account for the beam-modulating effect of heterogeneous lung tissue in physical and biological inverse treatment planning. The implementation employs an analytical model that derives the degradation from the established "modulation power" parameter P mod and the total water-equivalent thickness of lung parenchyma traversed by the beam. Beam modulation was reproduced through an on-the-fly convolution of the reference Bragg curve with Gaussian kernels depending on the modulation power of lung tissue (upstream). For biological doses, the degradation was determined by modulating dose-averaged α , β , and LET distributions. Carbon SOBP measurements behind lung substitute material were performed to validate the code. The implementation was then applied to a phantom and patient case.

RESULTS

Experimental results show the passage through a 20-cm Gammex LN300 slab led to a decrease in target coverage and broadening of the SOBP distal fall-off. However, dose coverage was regained through optimization. A good agreement between calculated and measured SOBPs was also found. In addition, a patient case study revealed a 3.2% decrease in D 95 from degradation ( P mod = 450 μ m), which was reduced to a 0.4% difference after optimization. Furthermore, widening of the RBE distribution beyond the target distal edge was observed. This implies an increased degradation in the biological dose, which could be harmful to healthy tissues distal to the target.

CONCLUSIONS

This is the first implementation capable of compensating for lung dose perturbations, which is more effective than margin extensions. A larger patient study is needed to examine the observed modulation in the RBE distribution and judge the clinical relevance also in IMPT, where margins might prove insufficient to recover target coverage.

Adaptation of stochastic microdosimetric kinetic model to hypoxia for hypo-fractionated multi-ion therapy treatment planning

For hypo-fractionated multi-ion therapy (HFMIT), the stochastic microdosimetric kinetic (SMK) model had been developed to estimate the biological effectiveness of radiation beams with wide linear energy transfer (LET) and dose ranges. The HFMIT will be applied to radioresistant tumors with oxygen-deficient regions. The response of cells to radiation is strongly dependent on the oxygen condition in addition to radiation type, LET and absorbed dose. This study presents an adaptation of the SMK model to account for oxygen-pressure dependent cell responses, and develops the oxygen-effect-incorporated stochastic microdosimetric kinetic (OSMK) model. In the model, following assumptions were made: the numbers of radiation-induced sublethal lesions (double-strand breaks) are reduced due to lack of oxygen, and the numbers of oxygen-mediated lesions are reduced for radiation with high LET. The model parameters were determined by fitting survival data under aerobic and anoxic conditions for human salivary gland tumor cells and V79 cells exposed to helium-, carbon-, and neon-ion beams over the LET range of 18.5-654.0 keVμm^-1. The OSMK model provided good agreement with the experimental survival data of the cells with determination coefficients >0.9. In terms of oxygen enhancement ratio, the OSMK model reproduced the experimental data behavior, including slight dependence on particle type at the same LET. The OSMK model was then implemented into the in-house treatment planning software for the HFMIT to validate its applicability in clinical practice. A treatment plan with helium- and neon-ion beams was made for a pancreatic cancer case assuming an oxygen-deficient region within the tumor. The biological optimization based on the OSMK model preferentially placed the neon-ion beam to the hypoxic region, while it placed both helium- and neon-ion beams to the surrounding normoxic region. The OSMK model offered the accuracy and usability required for hypoxia-based biological optimization in HFMIT treatment planning.

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

Simple Summary

Proton therapy is now an established external radiotherapy modality for cancer treatment. Clinical routine currently neglects the radiobiological impact of nuclear target fragments even if experimental evidences show a significant enhancement in cell-killing effect due to secondary particles. This paper quantifies the contribution of proton target fragments of different charge in different irradiation scenarios and compares the computationally predicted corrections to the overall biological dose with experimental data.

Abstract

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

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.

FLUKA simulation of target fragmentation in proton therapy

In proton therapy, secondary fragments are created in nuclear interactions of the beam with the target nuclei. The secondary fragments have low kinetic energies and high atomic numbers as compared to primary protons. Fragments have a high LET and deposit all their energy close to the generation point. For their characteristics, secondary fragments can alter the dose distribution and lead to an increase of RBE for the same delivered physical dose. Moreover, the radiobiological impact of target fragmentation is significant mostly in the region before the Bragg peak, where generally healthy tissues are present, and immediately after Bragg peak. Considering the high biological impact of those particles, especially in the case of healthy tissues or organs at risk, the inclusion of target fragmentation processes in the dose calculation of a treatment planning system can be relevant to improve the treatment accuracy and for this reason it is one of the major tasks of the MoVe IT project. In this study, Monte Carlo simulations were employed to fully characterize the mixed radiation field generated by target fragmentation in proton therapy. The dose averaged LET has been evaluated in case of a Spread Out Bragg Peak (SOBP). Starting from LET distribution, RBE has been evaluated with two different phenomenological models. In order to characterize the mixed radiation field, the production cross section has been evaluated by means of the FLUKA code. The future development of present work is to generate a MC database of fragments fluence to be included in TPS.

Robust treatment planning with 4D intensity modulated carbon ion therapy for multiple targets in stage IV non-small cell lung cancer

Intensity modulated particle therapy (IMPT) with carbon ions can generate highly conformal treatment plans; however, IMPT is limited in robustness against range and positioning uncertainty. This is particularly true for moving targets, even though all motion states of a 4DCT are considered in 4D-IMPT. Here, we expand 4D-IMPT to include robust non-linear RBE-weighted optimization to explore its potential in improving plan robustness and sparing critical organs. In this study, robust 4D-optimization-based on worst-case optimization on 9 scenarios-was compared to conventional 4D-optimization with PTV margins using 4D dose calculation and robustness analysis for 21 uncertainty scenarios. Slice-by-slice rescanning was used for motion mitigation. Both 4D-optimization strategies were tested on a cohort of 8 multi-lesion lung cancer patients with the goal of prioritizing OAR sparing in a hypofractionated treatment plan. Planning objectives were to keep the OAR volume doses below corresponding limits while simultaneously achieve CTV coverage with D95% ≥ 95 %. For the conventional plans, average D95% was at 98.7% which fulfilled the target objective in 83.2% of scenarios. For the robust plans, average D95% was reduced to 97.6% which still fulfilled the target objective in 80.7% of cases, but led to significantly improved overall OAR sparing: Volume doses were below the limits in 96.2% of cases for the conventional and 99.5% for the robust plans. When considering the particularly critical smaller airways only, fulfillment rates could be increased from 76.2% to 96% for the robust plans. This study has shown that plan robustness of 4D-IMPT could be improved by using robust 4D-optimization, offering greater control over uncertainties in the actual delivered dose. In some cases, this required sacrificing target coverage for the benefit of better OAR sparing.

Evaluating dosimetric constraints for carbon ion radiotherapy in the treatment of locally advanced pancreatic cancer

OBJECTIVE

To identify a safe carbon ion radiotherapy (CIRT) regimen for patients with locally advanced pancreatic cancer (LAPC).

METHODS

We generated treatment plans for 13 consecutive, unselected patients who were treated for LAPC with CIRT at our center using three dose and fractionation schedules: 4.6 GyRBE × 12, 4.0 GyRBE × 14, and 3.0 GyRBE × 17. We tested the ability to meet published dose constraints for the duodenum, stomach, and small bowel as a function of dose schedule and distance between the tumor and organs at risk.

RESULTS

Using 4.6 GyRBE × 12 and 4.0 GyRBE × 14, critical (high-dose) constraints could only reliably be achieved when target volumes were not immediately adjacent to organs at risk. Critical constraints could be met in all cases using 3.0 GyRBE × 17. Low-dose constraints could not uniformly be achieved using any dose schedule.

CONCLUSION

While selected patients with LAPC may be treated safely with a CIRT regimen of 4.6 GyRBE × 12, our dosimetric analyses indicate that a more conservative schedule of 3.0 GyRBE × 17 may be required to safely treat a broader population of LAPC patients, including those with large tumors and tumors that approach gastrointestinal organs at risk. The result of this work was used to guide an ongoing clinical trial.

Comparative photon and proton dosimetry for patients with mediastinal lymphoma in the era of Monte Carlo treatment planning and variable relative biological effectiveness

Background

Existing pencil beam analytical (PBA) algorithms for proton therapy treatment planning are not ideal for sites with heterogeneous tissue density and do not account for the spatial variations in proton relative biological effectiveness (vRBE). Using a commercially available Monte Carlo (MC) treatment planning system, we compared various dosimetric endpoints between proton PBA, proton MC, and photon treatment plans among patients with mediastinal lymphoma.

Methods

Eight mediastinal lymphoma patients with both free breathing (FB) and deep inspiration breath hold (DIBH) CT simulation scans were analyzed. The original PBA plans were re-calculated with MC. New proton plans that used MC for both optimization and dose calculation with equivalent CTV/ITV coverage were also created. A vRBE model, which uses a published model for DNA double strand break (DSB) induction, was applied on MC plans to study the potential impact of vRBE on cardiac doses. Comparative photon plans were generated on the DIBH scan.

Results

Re-calculation of FB PBA plans with MC demonstrated significant under coverage of the ITV V99 and V95. Target coverage was recovered by re-optimizing the PT plan with MC with minimal change to OAR doses. Compared to photons with DIBH, MC-optimized FB and DIBH proton plans had significantly lower dose to the mean lung, lung V5, breast tissue, and spinal cord for similar target coverage. Even with application of vRBE in the proton plans, the putative increase in RBE at the end of range did not decrease the dosimetric advantages of proton therapy in cardiac substructures.

Conclusions

MC should be used for PT treatment planning of mediastinal lymphoma to ensure adequate coverage of target volumes. Our preliminary data suggests that MC-optimized PT plans have better sparing of the lung and breast tissue compared to photons. Also, the potential for end of range RBE effects are unlikely to be large enough to offset the dosimetric advantages of proton therapy in cardiac substructures for mediastinal targets, although these dosimetric findings require validation with late toxicity data.

Prediction of Cell Survival after Exposure to Mixed Radiation Fields with the Local Effect Model

Mixed radiation fields comprise the most common form of radiation exposure. Given their relevance in radiation protection, cancer radiotherapy and space research, accurate predictions of the corresponding radiation effects are essential. The local effect model (LEM) allows the prediction of cell survival after ion irradiation based on the knowledge of the cells' response to photon radiation. The assumption is made that the same spatial DNA double-strand break (DSB) distributions in the cell nucleus lead to the same effects, independent of the radiation quality that produced the DSBs. This makes the LEM an ideal tool for predictions of cell survival after exposure to any mixed radiation field. In this work, the LEM is applied to calculate cell survival for extreme mixed irradiation scenarios, i.e., high-linear energy transfer (LET) ion radiation combined with low-LET photon radiation, which can be understood as a consistency test for the high-LET model. Available experimental data covering several ion species and energies in combination with photon exposure are predicted with the LEM. Furthermore, the results are compared to the microdosimetric model by Zaider and Rossi and the lesion additivity model by Lam, which allow the prediction of cell survival after exposure to mixed radiation fields based on the knowledge of the survival curves of the two radiation components. Although the LEM uses only photon dose-response data as input, it is able to compete with the empirical radiobiological models that additionally require ion dose-response curves as input. Certain experimental scenarios are presented in which the specific consideration of spatial DSB distributions could be essential for an accurate prediction of the effect of mixed radiation fields.

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.

FLUKA particle therapy tool for Monte Carlo independent calculation of scanned proton and carbon ion beam therapy

While Monte Carlo (MC) codes are considered as the gold standard for dosimetric calculations, the availability of user friendly MC codes suited for particle therapy is limited. Based on the FLUKA MC code and its graphical user interface (GUI) Flair, we developed an easy-to-use tool which enables simple and reliable simulations for particle therapy. In this paper we provide an overview of functionalities of the tool and with the presented clinical, proton and carbon ion therapy examples we demonstrate its reliability and the usability in the clinical environment and show its flexibility for research purposes. The first, easy-to-use FLUKA MC platform for particle therapy with GUI functionalities allows a user with a minimal effort and reduced knowledge about MC details to apply MC at their facility and is expected to enhance the popularity of the MC for both research and clinical quality assurance and commissioning purposes.

Radiobiological quantities in proton-therapy: Estimation and validation using Geant4-based Monte Carlo simulations

PURPOSE

The Geant4 Monte Carlo simulation toolkit was used to reproduce radiobiological parameters measured by irradiating three different cancerous cell lines with monochromatic and clinical proton beams.

METHODS

The experimental set-up adopted for irradiations was fully simulated with a dedicated open-source Geant4 application. Cells survival fractions was calculated coupling the Geant4 simulations with two analytical radiobiological models: one based on the LEM (Local Effect Model) approach and the other on a semi-empirical parameterisation. Results was evaluated and compared with experimental data.

RESULTS AND CONCLUSIONS

The results demonstrated the Geant4 ability to reproduce radiobiological quantities for different cell lines.

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.

Adaptation of stochastic microdosimetric kinetic model for charged-particle therapy treatment planning

The microdosimetric kinetic (MK) model underestimates the cell-survival fractions for high linear energy transfer (LET) and high dose irradiations. To address the issue, some researchers previously extended the MK model to the stochastic microdosimetric kinetic (SMK) model. In the SMK model, the radiation induced cell-survival fractions were estimated from the specific energies z _d and z _n absorbed by a microscopic subnuclear structure domain and a cell nucleus, respectively. By taking the stochastic nature of z _n as well as that of z _d into account, the SMK model could reproduce the measured cell-survival fractions for radiations with wide LET and dose ranges. However, treatment planning based on the SMK model was unrealistic in clinical practice due to its long computation time and huge memory space required for the computation. In this study, we modified the SMK model to shorten the computation time and to reduce the memory space required for the computation. By using the dose-averaged cell-nucleus specific energy per event [Formula: see text] in the SMK formalism, the stochastic nature of z _n was reflected onto the estimated cell-survival fractions. The accuracy of the modified SMK model was examined through the comparison between the estimated and the measured survival fractions of human salivary gland tumor cells and V79 cells. We then implemented the modified SMK model into the in-house treatment planning software for scanned charged-particle therapy to validate its applicability in clinical practice. As examples, treatment plans of helium-, carbon-, and neon-ion beams were made for an orbital tumor case. The modified SMK model could reproduce the measured cell-survival fractions more accurately compared to the MK model especially for high-LET and high-dose irradiations. In summary, the modified SMK model offers the accuracy and simplicity required in treatment planning of scanned charged-particle therapy for wide LET and dose ranges.

'Survival': a simulation toolkit introducing a modular approach for radiobiological evaluations in ion beam therapy

One major rationale for the application of heavy ion beams in tumour therapy is their increased relative biological effectiveness (RBE). The complex dependencies of the RBE on dose, biological endpoint, position in the field etc require the use of biophysical models in treatment planning and clinical analysis. This study aims to introduce a new software, named 'Survival', to facilitate the radiobiological computations needed in ion therapy. The simulation toolkit was written in C++ and it was developed with a modular architecture in order to easily incorporate different radiobiological models. The following models were successfully implemented: the local effect model (LEM, version I, II and III) and variants of the microdosimetric-kinetic model (MKM). Different numerical evaluation approaches were also implemented: Monte Carlo (MC) numerical methods and a set of faster analytical approximations. Among the possible applications, the toolkit was used to reproduce the RBE versus LET for different ions (proton, He, C, O, Ne) and different cell lines (CHO, HSG). Intercomparison between different models (LEM and MKM) and computational approaches (MC and fast approximations) were performed. The developed software could represent an important tool for the evaluation of the biological effectiveness of charged particles in ion beam therapy, in particular when coupled with treatment simulations. Its modular architecture facilitates benchmarking and inter-comparison between different models and evaluation approaches. The code is open source (GPL2 license) and available at https://github.com/batuff/Survival.

Targeting DNA repair with PNKP inhibition sensitizes radioresistant prostate cancer cells to high LET radiation

High linear energy transfer (LET) radiation or heavy ion such as carbon ion radiation is used as a method for advanced radiotherapy in the treatment of cancer. It has many advantages over the conventional photon based radiotherapy using Co-60 gamma or high energy X-rays from a Linear Accelerator. However, charged particle therapy is very costly. One way to reduce the cost as well as irradiation effects on normal cells is to reduce the dose of radiation by enhancing the radiation sensitivity through the use of a radiomodulator. PNKP (polynucleotide kinase/phosphatase) is an enzyme which plays important role in the non-homologous end joining (NHEJ) DNA repair pathway. It is expected that inhibition of PNKP activity may enhance the efficacy of the charged particle irradiation in the radioresistant prostate cancer cell line PC-3. To test this hypothesis, we investigated cellular radiosensitivity by clonogenic cell survival assay in PC-3 cells.12Carbon ion beam of62 MeVenergy (equivalent 5.16 MeV/nucleon) and with an entrance LET of 287 kev/μm was used for the present study. Apoptotic parameters such as nuclear fragmentation and caspase-3 activity were measured by DAPI staining, nuclear ladder assay and colorimetric caspase-3method. Cell cycle arrest was determined by FACS analysis. Cell death was enhanced when carbon ion irradiation is combined with PNKPi (PNKP inhibitor) to treat cells as compared to that seen for PNKPi untreated cells. A low concentration (10μM) of PNKPi effectively radiosensitized the PC-3 cells in terms of reduction of dose in achieving the same survival fraction. PC-3 cells underwent significant apoptosis and cell cycle arrest too was enhanced at G2/M phase when carbon ion irradiation was combined with PNKPi treatment. Our findings suggest that combined treatment of carbon ion irradiation and PNKP inhibition could enhance cellular radiosensitivity in a radioresistant prostate cancer cell line PC-3. The synergistic effect of PNKPi and carbon ion irradiation could be used as a promising method for carbon-ion therapy in radioresistant cells.

RBE and related modeling in carbon-ion therapy

Carbon ion therapy is a promising evolving modality in radiotherapy to treat tumors that are radioresistant against photon treatments. As carbon ions are more effective in normal and tumor tissue, the relative biological effectiveness (RBE) has to be calculated by bio-mathematical models and has to be considered in the dose prescription. This review (i) introduces the concept of the RBE and its most important determinants, (ii) describes the physical and biological causes of the increased RBE for carbon ions, (iii) summarizes available RBE measurements in vitro and in vivo, and (iv) describes the concepts of the clinically applied RBE models (mixed beam model, local effect model, and microdosimetric-kinetic model), and (v) the way they are introduced into clinical application as well as (vi) their status of experimental and clinical validation, and finally (vii) summarizes the current status of the use of the RBE concept in carbon ion therapy and points out clinically relevant conclusions as well as open questions. The RBE concept has proven to be a valuable concept for dose prescription in carbon ion radiotherapy, however, different centers use different RBE models and therefore care has to be taken when transferring results from one center to another. Experimental studies significantly improve the understanding of the dependencies and limitations of RBE models in clinical application. For the future, further studies investigating quantitatively the differential effects between normal tissues and tumors are needed accompanied by clinical studies on effectiveness and toxicity.

Analytical probabilistic modeling of RBE-weighted dose for ion therapy

Particle therapy is especially prone to uncertainties. This issue is usually addressed with uncertainty quantification and minimization techniques based on scenario sampling. For proton therapy, however, it was recently shown that it is also possible to use closed-form computations based on analytical probabilistic modeling (APM) for this purpose. APM yields unique features compared to sampling-based approaches, motivating further research in this context. This paper demonstrates the application of APM for intensity-modulated carbon ion therapy to quantify the influence of setup and range uncertainties on the RBE-weighted dose. In particular, we derive analytical forms for the nonlinear computations of the expectation value and variance of the RBE-weighted dose by propagating linearly correlated Gaussian input uncertainties through a pencil beam dose calculation algorithm. Both exact and approximation formulas are presented for the expectation value and variance of the RBE-weighted dose and are subsequently studied in-depth for a one-dimensional carbon ion spread-out Bragg peak. With V and B being the number of voxels and pencil beams, respectively, the proposed approximations induce only a marginal loss of accuracy while lowering the computational complexity from order [Formula: see text] to [Formula: see text] for the expectation value and from [Formula: see text] to [Formula: see text] for the variance of the RBE-weighted dose. Moreover, we evaluated the approximated calculation of the expectation value and standard deviation of the RBE-weighted dose in combination with a probabilistic effect-based optimization on three patient cases considering carbon ions as radiation modality against sampled references. The resulting global γ-pass rates (2 mm,2%) are [Formula: see text]99.15% for the expectation value and [Formula: see text]94.95% for the standard deviation of the RBE-weighted dose, respectively. We applied the derived analytical model to carbon ion treatment planning, although the concept is in general applicable to other ion species considering a variable RBE.

Systematics of relative biological effectiveness measurements for proton radiation along the spread out Bragg peak: experimental validation of the local effect model

The purpose of this study is to compare the predictions of the local effect model (LEM) in an extensive analysis to proton relative biological effectiveness (RBE) experiments found in the literature, and demonstrate the capabilities of the model as well as to discuss potential limitations. 19 publications with in vitro experiments and 10 publications with in vivo experiments focusing on proton RBE along the spread out Bragg peak (SOBP) were considered. In total the RBE values of over 100 depth positions were compared to LEM predictions. The treatment planning software TRiP98 was used to reconstruct the proton depth dose profile, and, together with the physical dose distribution, the RBE prediction was conducted based on the LEM. Only parameters from photon dose response curves are used as input for the LEM, and no free parameters are introduced, thus allowing us to demonstrate the predictive power of the LEM for protons. The LEM describes the RBE adequately well within the SOBP region with a relative deviation of typically less than 10% up to 10 keV µm^-1. In accordance with previous publications a clear dependence of RBE on the dose-averaged linear energy transfer (LET_D) was observed. The RBE in the experiments tends to increase above 1.1 for LET_D values above 2 keV µm^-1 and above 1.5 for LET_D values higher than 10 keV µm^-1 (distal part of the SOBP). The dose dependence is most pronounced for doses lower than 3 Gy (RBE). However, both the LEM predictions and experimental data show only a weak dependence of RBE on the tissue type, as characterized by the α/β ratio, which is considered insignificant with regard to the general uncertainties of RBE. The RBE predicted by the LEM shows overall very good agreement with the experimental data within the SOBP region and is in better agreement with the experimental data than the constant RBE of 1.1 that is currently applied in the clinics. All RBE trends deduced from the experiments were also reflected by the LEM predictions, which are purely based on input parameters derived from low-LET photon radiation.

Variable RBE in proton therapy: comparison of different model predictions and their influence on clinical-like scenarios

Background

In proton radiation therapy a constant relative biological effectiveness (RBE) of 1.1 is usually assumed. However, biological experiments have evidenced RBE dependencies on dose level, proton linear energy transfer (LET) and tissue type. This work compares the predictions of three of the main radio-biological models proposed in the literature by Carabe-Fernandez, Wedenberg, Scholz and coworkers.

Methods

Using the chosen models, a spread-out Bragg peak (SOBP) as well as two exemplary clinical cases (single field and two fields) for cranial proton irradiation, all delivered with state-of-the-art pencil-beam scanning, have been analyzed in terms of absorbed dose, dose-averaged LET (LET_D), RBE-weighted dose (D_RBE) and biological range shift distributions.

Results

In the systematic comparison of RBE predictions by the three models we could show different levels of agreement depending on (α/β)_x and LET values. The SOBP study emphasizes the variation of LET_D and RBE not only as a function of depth but also of lateral distance from the central beam axis. Application to clinical-like scenario shows consistent discrepancies from the values obtained for a constant RBE of 1.1, when using a variable RBE scheme for proton irradiation in tissues with low (α/β)_x, regardless of the model. Biological range shifts of 0.6– 2.4 mm (for high (α/β)_x) and 3.0 – 5.4 mm (for low (α/β)_x) were found from the fall-off analysis of individual profiles of RBE-weighted fraction dose along the beam penetration depth.

Conclusions

Although more experimental evidence is needed to validate the accuracy of the investigated models and their input parameters, their consistent trend suggests that their main RBE dependencies (dose, LET and (α/β)_x) should be included in treatment planning systems. In particular, our results suggest that simpler models based on the linear-quadratic formalism and LET_D might already be sufficient to reproduce important RBE dependencies for re-evaluation of plans optimized with the current RBE = 1.1 approximation. This approach would be a first step forward to consider RBE variations in proton therapy, thus enabling a more robust choice of biological dose delivery. The latter could in turn impact clinical outcome, especially in terms of reduced toxicities for tumors adjacent to organs at risk.

The FLUKA Code: An Accurate Simulation Tool for Particle Therapy

Monte Carlo (MC) codes are increasingly spreading in the hadrontherapy community due to their detailed description of radiation transport and interaction with matter. The suitability of a MC code for application to hadrontherapy demands accurate and reliable physical models capable of handling all components of the expected radiation field. This becomes extremely important for correctly performing not only physical but also biologically based dose calculations, especially in cases where ions heavier than protons are involved. In addition, accurate prediction of emerging secondary radiation is of utmost importance in innovative areas of research aiming at in vivo treatment verification. This contribution will address the recent developments of the FLUKA MC code and its practical applications in this field. Refinements of the FLUKA nuclear models in the therapeutic energy interval lead to an improved description of the mixed radiation field as shown in the presented benchmarks against experimental data with both (4)He and (12)C ion beams. Accurate description of ionization energy losses and of particle scattering and interactions lead to the excellent agreement of calculated depth-dose profiles with those measured at leading European hadron therapy centers, both with proton and ion beams. In order to support the application of FLUKA in hospital-based environments, Flair, the FLUKA graphical interface, has been enhanced with the capability of translating CT DICOM images into voxel-based computational phantoms in a fast and well-structured way. The interface is capable of importing also radiotherapy treatment data described in DICOM RT standard. In addition, the interface is equipped with an intuitive PET scanner geometry generator and automatic recording of coincidence events. Clinically, similar cases will be presented both in terms of absorbed dose and biological dose calculations describing the various available features.

Comparative Risk Predictions of Second Cancers After Carbon-Ion Therapy Versus Proton Therapy

PURPOSE

This work proposes a theoretical framework that enables comparative risk predictions for second cancer incidence after particle beam therapy for different ion species for individual patients, accounting for differences in relative biological effectiveness (RBE) for the competing processes of tumor initiation and cell inactivation. Our working hypothesis was that use of carbon-ion therapy instead of proton therapy would show a difference in the predicted risk of second cancer incidence in the breast for a sample of Hodgkin lymphoma (HL) patients.

METHODS AND MATERIALS

We generated biologic treatment plans and calculated relative predicted risks of second cancer in the breast by using two proposed methods: a full model derived from the linear quadratic model and a simpler linear-no-threshold model.

RESULTS

For our reference calculation, we found the predicted risk of breast cancer incidence for carbon-ion plans-to-proton plan ratio, <Rc/Rp>, to be 0.75 ± 0.07 but not significantly smaller than 1 (P=.180).

CONCLUSIONS

Our findings suggest that second cancer risks are, on average, comparable between proton therapy and carbon-ion therapy.

Kill-painting of hypoxic tumours in charged particle therapy

Solid tumours often present regions with severe oxygen deprivation (hypoxia), which are resistant to both chemotherapy and radiotherapy. Increased radiosensitivity as a function of the oxygen concentration is well described for X-rays. It has also been demonstrated that radioresistance in anoxia is reduced using high-LET radiation rather than conventional X-rays. However, the dependence of the oxygen enhancement ratio (OER) on radiation quality in the regions of intermediate oxygen concentrations, those normally found in tumours, had never been measured and biophysical models were based on extrapolations. Here we present a complete survival dataset of mammalian cells exposed to different ions in oxygen concentration ranging from normoxia (21%) to anoxia (0%). The data were used to generate a model of the dependence of the OER on oxygen concentration and particle energy. The model was implemented in the ion beam treatment planning system to prescribe uniform cell killing across volumes with heterogeneous radiosensitivity. The adaptive treatment plans have been validated in two different accelerator facilities, using a biological phantom where cells can be irradiated simultaneously at three different oxygen concentrations. We thus realized a hypoxia-adapted treatment plan, which will be used for painting by voxel of hypoxic tumours visualized by functional imaging.

A numerical method to optimise the spatial dose distribution in carbon ion radiotherapy planning

The authors describe a numerical algorithm to optimise the entrance spectra of a composition of pristine carbon ion beams which delivers a pre-assumed dose-depth profile over a given depth range within the spread-out Bragg peak. The physical beam transport model is based on tabularised data generated using the SHIELD-HIT10A Monte-Carlo code. Depth-dose profile optimisation is achieved by minimising the deviation from the pre-assumed profile evaluated on a regular grid of points over a given depth range. This multi-dimensional minimisation problem is solved using the L-BFGS-B algorithm, with parallel processing support. Another multi-dimensional interpolation algorithm is used to calculate at given beam depths the cumulative energy-fluence spectra for primary and secondary ions in the optimised beam composition. Knowledge of such energy-fluence spectra for each ion is required by the mixed-field calculation of Katz's cellular Track Structure Theory (TST) that predicts the resulting depth-survival profile. The optimisation algorithm and the TST mixed-field calculation are essential tools in the development of a one-dimensional kernel of a carbon ion therapy planning system. All codes used in the work are generally accessible within the libamtrack open source platform.