Research Treatment Planning System Research Articles

Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization

Objective. Treatment plan optimization in high dose rate brachytherapy often requires manual fine-tuning of penalty weights for each objective, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI). Approach. The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations. Main results. MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% ± 8.11%, and performance time was 66.6 ± 12.6 s. The initiation took 22.47 ± 7.57 s, and each iteration took 7.35 ± 2.45 s to find one Pareto solution.Significance. MOBO-qNEHVI combined with FMIO can automatically explore the trade-offs between treatment plan objectives in a patient specific manner within a minute. This approach can reduce the dependency of plan quality on planner’s experience and reduce dose to the organs at risk.

Feasibility and constraints of Bragg peak FLASH proton therapy treatment planning.

FLASH proton therapy (FLASH-PT) requires ultra-high dose rate (≥40 Gy/s) protons to be delivered in a short timescale whilst conforming to a patient-specific target. This study investigates the feasibility and constraints of Bragg peak FLASH-PT treatment planning, and compares the in silico results produced to plans for intensity modulated proton therapy (IMPT). Bragg peak FLASH-PT and IMPT treatment plans were generated for bone (n=3), brain (n=3), and lung (n=4) targets using the MIROpt research treatment planning system and the Conformal FLASH library developed by Applications SA from the open-source version of UCLouvain. FLASH-PT beams were simulated using monoenergetic spot-scanned protons traversing through a conformal energy modulator, a range shifter, and an aperture. A dose rate constraint of ≥ 40 Gy/s was included in each FLASH-PT plan optimisation. Space limitations in the FLASH-PT adapted beam nozzle imposed a maximum target width constraint, excluding 4 cases from the study. FLASH-PT plans did not satisfy the imposed target dose constraints (D95% ≥ 95% and D2%≤ 105%) but achieved clinically acceptable doses to organs at risk (OARs). IMPT plans adhered to all target and OAR dose constraints. FLASH-PT plans showed a reduction in both target homogeneity (p < 0.001) and dose conformity (non-significant) compared to IMPT. Without accounting for a sparing effect, IMPT plans were superior in target coverage, dose conformity, target homogeneity, and OAR sparing compared to FLASH-PT. Further research is warranted in treatment planning optimisation and beam delivery for clinical implementation of Bragg peak FLASH-PT.

Very high-energy electron therapy as light-particle alternative to transmission proton FLASH therapy – An evaluation of dosimetric performances

PurposeClinical translation of FLASH-radiotherapy (RT) to deep-seated tumours is still a technological challenge. One proposed solution consists of using ultra-high dose rate transmission proton (TP) beams of about 200–250 MeV to irradiate the tumour with the flat entrance of the proton depth-dose profile. This work evaluates the dosimetric performance of very high-energy electron (VHEE)-based RT (50–250 MeV) as a potential alternative to TP-based RT for the clinical transfer of the FLASH effect. MethodsBasic physics characteristics of VHEE and TP beams were compared utilizing Monte Carlo simulations in water. A VHEE-enabled research treatment planning system was used to evaluate the plan quality achievable with VHEE beams of different energies, compared to 250 MeV TP beams for a glioblastoma, an oesophagus, and a prostate cancer case. ResultsLike TP, VHEE above 100 MeV can treat targets with roughly flat (within ± 20 %) depth-dose distributions. The achievable dosimetric target conformity and adjacent organs-at-risk (OAR) sparing is consequently driven for both modalities by their lateral beam penumbrae. Electron beams of 400[500] MeV match the penumbra of 200[250] MeV TP beams and penumbra is increased for lower electron energies. For the investigated patient cases, VHEE plans with energies of 150 MeV and above achieved a dosimetric plan quality comparable to that of 250 MeV TP plans. For the glioblastoma and the oesophagus case, although having a decreased conformity, even 100 MeV VHEE plans provided a similar target coverage and OAR sparing compared to TP. ConclusionsVHEE-based FLASH-RT using sufficiently high beam energies may provide a lighter-particle alternative to TP-based FLASH-RT with comparable dosimetric plan quality.

Extension of matRad with a modified microdosimetric kinetic model for carbon ion treatment planning: Comparison with Monte Carlo calculation.

Treatment planning is essential for in silico particle therapy studies. matRad is an open-source research treatment planning system (TPS) based on the local effect model, which is a type of relative biological effectiveness (RBE) model. This study aims to implement a microdosimetric kinetic model (MKM) in matRad and develop an automation algorithm for Monte Carlo (MC) dose recalculation using the TOPAS code. In addition, we provide the developed MKM extension as open-source tool for users. Carbon beam data were generated using TOPAS MC pencil beam irradiation. We parameterized the TOPAS MC beam data with a double-Gaussian fit and modeled the integral depth doses and lateral spot profiles in the range of 100-430 MeV/u. To implement the MKM, the specific energy data table for Z=1-6 and integrated depth-specific energy data were acquired based on the Kiefer-Chatterjee track structure and TOPAS MC simulation, respectively. Generic data were integrated into matRad, and treatment planning was performed based on these data. The optimized plan parameters were automatically converted into MC simulation input. Finally, the matRad TPS and TOPAS MC simulations were compared using the RBE-weighted dose calculation results. A comparison was made for three geometries: homogeneous water phantom, inhomogeneous phantom, and patient. The RBE-weighted dose (DRBE ) distribution agreed with TOPAS MC within 1.8% for all target sizes for the homogeneous phantom. For the inhomogeneous phantom, the relative difference in the range of 80% of the prescription dose in the distal fall-off region (R80) between the matRad TPS and TOPAS MC was 0.6% (1.1mm). DRBE between the TPS and the MC was within 4.0%. In the patient case, the difference in the dose-volume histogram parameters for the target volume between the TPS and the MC was less than 2.7%. The relative difference in R80 was 0.7% (1.2mm). The MKM was successfully implemented in matRad TPS, and the RBE-weighted dose was comparable to that of TOPAS MC. The MKM-implemented matRad was released as an open-source tool. Further investigations with MC simulations can be conducted using this tool, providing a good option for carbon ion research.

Robust deep learning-based forward dose calculations for VMAT on the 1.5T MR-linac

In this work we present a framework for robust deep learning-based VMAT forward dose calculations for the 1.5T MR-linac. A convolutional neural network was trained on the dose of individual multi-leaf-collimator VMAT segments and was used to predict the dose per segment for a set of MR-linac-deliverable VMAT test plans. The training set consisted of prostate, rectal, lung and esophageal tumour data. All patients were previously treated in our clinic with VMAT on a conventional linac. The clinical data were converted to an MR-linac environment prior to training. During training time, gantry and collimator angles were randomized for each training sample, while the multi-leaf-collimator shapes were rigidly shifted to ensure robust learning. A Monte Carlo dose engine was used for the generation of the ground truth data at 1% statistical uncertainty per control point. For a set of 17 MR-linac-deliverable VMAT test plans, generated on a research treatment planning system, our method predicted highly accurate dose distributions, reporting 99.7% ± 0.5% for the full plan prediction at the 3%/3 mm gamma criterion. Additional evaluation on previously unseen IMRT patients passed all clinical requirements resulting in 99.0% ± 0.6% for the 3%/3 mm analysis. The overall performance of our method makes it a promising plan validation solution for IMRT and VMAT workflows, robust to tumour anatomies and tissue density variations.

3D high resolution clonogenic survival measurement of xrs-5 cells in low-dose region of carbon ion plans

Purpose In this study, we performed biological verification measurements of cell survival of a 12C ion irradiation plan employing a high-resolution 3D culture setup. This allowed, in particular, to access the cell inactivation in the low-dose regions close to the target area. Materials and methods We established the protocol for a 3D culture setup where xrs-5 cells were grown inside a layered matrigel structure in 384-well plates. Their radiosensitivity to conventional and 12C ion radiation was evaluated by irradiating them either with 250 kV X-rays at GSI or with monoenergetic 12C beams of 110 MeV/u at MIT, and compared with those of monolayers. A treatment plan for a rectangular target was prepared using the GSI research treatment planning system TRiP98. xrs-5 cells were seeded in the matrigel-based setup and irradiated in dose fall-off regions using active scanning 12C ion beams. In addition, film dosimetry utilizing radiochromic EBT3 film has been performed to assess the field homogeneity downstream of 384-well V-bottom plates with or without additional agarose coating of the well plate bottom. Results Dose response curves following X-ray and 12C ion irradiation had linear shape and showed a significant decrease in survival fraction at even moderate doses. Survival measurements in the low-dose regions of the plan for the extended target showed good agreement to the predicted survival fraction. The irradiated film profiles yielded a flat dose distribution without apparent artifacts or inhomogeneities for well plates both with and without agarose coating, confirming the suitability of the experimental setup. Conclusions We conclude that the V-bottom 384-well plates in combination with the radiation-sensitive xrs-5 cell line constitute a suitable radiobiological verification tool which can be used especially for low doses. Furthermore, the measured survival of xrs-5 cells show a good agreement with the expected survival in the low-dose out-of-field regions, both laterally and downstream of the target.

Fast VMAT planning for prostate radiotherapy: dosimetric validation of a deep learning-based initial segment generation method

Objective. To develop and evaluate a deep learning based fast volumetric modulated arc therapy (VMAT) plan generation method for prostate radiotherapy. Approach. A customized 3D U-Net was trained and validated to predict initial segments at 90 evenly distributed control points of an arc, linked to our research treatment planning system (TPS) for segment shape optimization (SSO) and segment weight optimization (SWO). For 27 test patients, the VMAT plans generated based on the deep learning prediction (VMATDL) were compared with VMAT plans generated with a previously validated automated treatment planning method (VMATref). For all test cases, the deep learning prediction accuracy, plan dosimetric quality, and the planning efficiency were quantified and analyzed. Main results. For all 27 test cases, the resulting plans were clinically acceptable. The V 95% for the PTV2 was greater than 99%, and the V 107% was below 0.2%. Statistically significant difference in target coverage was not observed between the VMATref and VMATDL plans (P = 0.3243 > 0.05). The dose sparing effect to the OARs between the two groups of plans was similar. Small differences were only observed for the Dmean of rectum and anus. Compared to the VMATref, the VMATDL reduced 29.3% of the optimization time on average. Significance. A fully automated VMAT plan generation method may result in significant improvement in prostate treatment planning efficiency. Due to the clinically acceptable dosimetric quality and high efficiency, it could potentially be used for clinical planning application and real-time adaptive therapy application after further validation.

Variance-based sensitivity analysis for uncertainties in proton therapy: A framework to assess the effect of simultaneous uncertainties in range, positioning, and RBE model predictions on RBE-weighted dose distributions.

Treatment plans in proton therapy are more sensitive to uncertainties than in conventional photon therapy. In addition to setup uncertainties, proton therapy is affected by uncertainties in proton range and relative biological effectiveness (RBE). While to date a constant RBE of 1.1 is commonly assumed, the actual RBE is known to increase toward the distal end of the spread-out Bragg peak. Several models for variable RBE predictions exist. We present a framework to evaluate the combined impact and interactions of setup, range, and RBE uncertainties in a comprehensive, variance-based sensitivity analysis (SA). The variance-based SA requires a large number (104 -105 ) of RBE-weighted dose (RWD) calculations. Based on a particle therapy extension of the research treatment planning system CERR we implemented a fast, graphics processing unit (GPU) accelerated pencil beam modeling of patient and range shifts. For RBE predictions, two biological models were included: The mechanistic repair-misrepair-fixation (RMF) model and the phenomenological Wedenberg model. All input parameters (patient position, proton range, RBE model parameters) are sampled simultaneously within their assumed probability distributions. Statistical formalisms rank the input parameters according to their influence on the overall uncertainty of RBE-weighted dose-volume histogram (RW-DVH) quantiles and the RWD in every voxel, resulting in relative, normalized sensitivity indices (S=0: noninfluential input, S=1: only influential input). Results are visualized as RW-DVHs with error bars and sensitivity maps. The approach is demonstrated for two representative brain tumor cases and a prostate case. The full SA including RWD calculations took 39, 11, and 55min, respectively. Range uncertainty was an important contribution to overall uncertainty at the distal end of the target, while the relatively smaller uncertainty inside the target was governed by biological uncertainties. Consequently, the uncertainty of the RW-DVH quantile D98 for the target was governed by range uncertainty while the uncertainty of the mean target dose was dominated by the biological parameters. The SA framework is a powerful and flexible tool to evaluate uncertainty in RWD distributions and DVH quantiles, taking into account physical and RBE uncertainties and their interactions. The additional information might help to prioritize research efforts to reduce physical and RBE uncertainties and could also have implications for future approaches to biologically robust planning and optimization.

Implementation of a dose calculation algorithm based on Monte Carlo simulations for treatment planning towards MRI guided ion beam therapy

Magnetic resonance guidance in particle therapy has the potential to improve the current performance of clinical workflows. However, the presence of magnetic fields challenges the current algorithms for treatment planning. To ensure proper dose calculations, compensation methods are required to guarantee that the maximum deposited energy of deflected beams lies in the target volume. In addition, proper modifications of the intrinsic dose calculation engines, accounting for magnetic fields, are needed. In this work, an algorithm for proton treatment planning in magnetic fields was implemented in a research treatment planning system (TPS), matRad. Setup-specific look up tables were generated using a validated MC model for a clinical proton beamline (62.4 – 215.7 MeV) interacting with a dipole magnet (B = 0–1 T). The algorithm was successfully benchmarked against MC simulations in water, showing gamma index (2%/2mm) global pass rates higher than 96% for different plan configurations. Additionally, absorbed depth doses were compared with experimental measurements in water. Differences within 2% and 3.5% in the Bragg peak and entrance regions, respectively, were found. Finally, treatment plans were generated and optimized for magnetic field strengths of 0 and 1 T to assess the performance of the proposed model. Equivalent treatment plans and dose volume histograms were achieved, independently of the magnetic field strength. Differences lower than 1.5% for plan quality indicators (D2%, D50%, D90%, V95% and V105%) in water, a TG119 phantom and an exemplary prostate patient case were obtained. More complex treatment planning studies are foreseen to establish the limits of applicability of the proposed model.

Online Prostate-Specific Membrane Antigen and Positron Emission Tomography–Guided Radiation Therapy for Oligometastatic Prostate Cancer

PurposeStereotactic ablative radiation therapy (SABR) for oligometastatic prostate cancer (OMPC) may improve clinical outcomes, but current challenges in intrafraction tracking of multiple small targets limits treatment accuracy. A biology-guided radiation therapy (BgRT) delivery system incorporating positron emission tomography (PET) detectors is being developed to use radiotracer uptake as a biologic fiducial for intrafraction tumor tracking to improve geometric accuracy. This study simulates prostate-specific membrane antigen (PSMA)-directed BgRT using a cohort from our phase II randomized trial of SABR in men with recurrent hormone sensitive OMPC and compares dose distributions to clinical SABR (CSABR). Methods and MaterialsA research treatment planning system (RTPS) was used to replan 15 patients imaged with PSMA-targeted 18F-DCFPyL PET/computed tomography and previously treated with CSABR using conventional linear accelerators (linacs). The RTPS models a prototype ring-mounted linac incorporating PET and kilo-voltage computed tomography imaging subsystems and can be used to optimize BgRT plans, as well as research SABR (RSABR) plans, which use the prototype linac without radiotracer guidance. CSABR, RSABR, and BgRT plans were compared in terms of maximum planning target volume (PTV) dose (Dmax), mean dose to proximal organs at risk (DOAR), conformity index, as well as voxel-wise correlation of dose with PET specific uptake values to investigate possible dose-painting effects. ResultsRSABR and BgRT plans resulted in mean ± standard deviation increases in Dmax of 4 ± 11% (P = .21) and 18 ± 15% (P < .001) and reductions in DOAR of –20 ± 19% (P <.001) and –10 ± 19% (P = .02) compared with CSABR. Similar target coverage was maintained with conformity indices of 0.81 ± 0.04 (P < .001) and 0.72 ± 0.08 (P = .44) for RSABR and BgRT compared with 0.74 ± 0.08 for CSABR. Dose and log (specific uptake values) had Pearson correlation coefficients of 0.10 (CSABR), 0.16 (RSABR), and 0.31 (BgRT). ConclusionsBgRT plans provided similar PTV coverage and conformity compared with CSABR while incorporating underlying PET activity. These results demonstrate feasibility of BgRT optimization enabling online PSMA-targeted, PET-based tracked dose delivery for OMPC.

Trimmer sequencing time minimization during dynamically collimated proton therapy using a colony of cooperating agents

The dynamic collimation system (DCS) can be combined with pencil beam scanning proton therapy to deliver highly conformal treatment plans with unique collimation at each energy layer. This energy layer-specific collimation is accomplished through the synchronized motion of four trimmer blades that intercept the proton beam near the target boundary in the beam’s eye view. However, the corresponding treatment deliveries come at the cost of additional treatment time since the translational speed of the trimmer is slower than the scanning speed of the proton pencil beam. In an attempt to minimize the additional trimmer sequencing time of each field while still maintaining a high degree of conformity, a novel process utilizing ant colony optimization (ACO) methods was created to determine the most efficient route of trimmer sequencing and beamlet scanning patterns for a collective set of collimated proton beamlets. The ACO process was integrated within an in-house treatment planning system optimizer to determine the beam scanning and DCS trimmer sequencing patterns and compared against an analytical approximation of the trimmer sequencing time should a contour-like scanning approach be assumed instead. Due to the stochastic nature of ACO, parameters where determined so that they could ensure good convergence and an efficient optimization of trimmer sequencing that was faster than an analytical contour-like trimmer sequencing. The optimization process was tested using a set of three intracranial treatment plans which were planned using a custom research treatment planning system and were successfully optimized to reduce the additional trimmer sequencing time to approximately 60 s per treatment field while maintaining a high degree of target conformity. Thus, the novel use of ACO techniques within a treatment planning algorithm has been demonstrated to effectively determine collimation sequencing patterns for a DCS in order to minimize the additional treatment time required for trimmer movement during treatment.

Kill painting of hypoxic tumors with multiple ion beams

We report on a novel method for simultaneous biological optimization of treatment plans for hypoxic tumors using multiple ion species. Our previously introduced kill painting approach, where the overall cell killing is optimized on biologically heterogeneous targets, was expanded with the capability of handling different ion beams simultaneously. The current version (MIBO) of the research treatment planning system TRiP98 has now been augmented to handle 3D (voxel-by-voxel) target oxygenation data. We present a case of idealized geometries where this method can identify optimal combinations leading to an improved peak-to-entrance effective dose ratio. This is achieved by the redistribution of particle fluences, when the heavier ions are preferentially forwarded to hypoxic target areas, while the lighter ions deliver the remaining dose to its normoxic regions. Finally, we present an in silico skull base chordoma patient case study with a combination of 4He and 16O beams, demonstrating specific indications for its potential clinical application. In this particular case, the mean dose, received by the brainstem, was reduced by 3%–5% and by 10%–12% as compared to the pure 4He and 16O plans, respectively. The new method allows a full biological optimization of different ion beams, exploiting the capabilities of actively scanned ion beams of modern particle therapy centers. The possible experimental verification of the present approach at ion beam facilities disposing of fast ion switch is presented and discussed.

Planning strategies for inter-fractional robustness in pancreatic patients treated with scanned carbon therapy

BackgroundManaging inter-fractional anatomy changes is a challenging task in radiotherapy of pancreatic tumors, especially in scanned carbon-ion delivery. This treatment planning study aims to focus on clinically feasible solutions, such as the beam angle selection and margin design to increase the robustness against inter-fractional uncertainties.MethodsThis study included 10 patients with weekly 3D-CT imaging and physician-approved Clinical Target Volume (CTV). The study was directed to keep the CTV-coverage using six beam angle configurations in combination with different Internal Target Volume (ITV) concepts. These were: geometric-margin (symmetric 3 and 5 mm margin); range-equivalent margins with an isotropic HU replacement; and to evaluate the need of asymmetric margins the water-equivalent range path (WEPL) was determined per patient from the set of CTs.Plan optimization and forward dose calculation in each week-CT were performed with the research treatment planning system TRiP98 and the plan quality evaluated in terms of CTV coverage (V95CTV) and homogeneity dose (HCTV = D5-D95).ResultsThe beam geometry had a substantial impact on the target irradiation over the treatment course, with the single posterior or two beams showing the best average coverage of the CTV. The use of geometric margins for the more robust beam geometries showed acceptable results, with a V95CTV of (99.2 ± 1.2)% for the 5 mm-margin. For the non-robust configurations, due to substantial changes in the radiological depth, the use of this margin results in a V95CTV that might be below 80%, only showing improvement when the range changes are included.ConclusionsSelection of adequate beam configurations and treatment margins in ion-beam therapy of pancreatic tumors is of great importance. For a single posterior beam or two beam configurations, application of geometrical margins compensate for dose degradation induced by inter-fractional anatomy changes for the majority of the analyzed treatment fractions.

Rapid implementation of the repair-misrepair-fixation (RMF) model facilitating online adaption of radiosensitivity parameters in ion therapy

Introduction: Treatment planning for ion therapy must account for physical properties of the beam as well as differences in the relative biological effectiveness (RBE) of ions compared to photons. In this work, we present a fast RBE calculation approach, based on the decoupling of physical properties and the ratio commonly used to describe the radiosensitivity of irradiated cells or organs.Material and methods: In the framework of the mechanistic repair-misrepair-fixation (RMF) model, the biological modeling can be decoupled from the physical dose. This was implemented into a research treatment planning system for carbon ion therapy.Results: The presented implementation of the RMF model is very fast, allowing online changes of . For example, a change of including a complete biological modeling and a recalculation of RBE for voxel takes 4 ms on a 4 CPU, 3.2 GHz workstation.Discussion and conclusion: The derived decoupling within the RMF model allows fast changes in , facilitating online adaption by the user. This provides new options for radiation oncologists, facilitating online variations of the radiobiological input parameters during the treatment plan evaluation process as well as uncertainty and sensitivity analyses.

SU‐C‐BRB‐04: Delineation Uncertainty Maps: Proof of Concept Study for Recurrent Gynaecological Cancers

Purpose:To create a novel infrastructure which allows clinicians to record their GTV delineation uncertainty; to generate delineation uncertainty maps from this data and compare them to clinical GTVs.Methods:A contouring concept was developed in which clinicians draw up to two GTV boundaries per CT slice corresponding to the inner and outermost possible boundaries the GTV may have.A new uncertainty contouring tool was implemented in our research treatment planning system (TPS, DynaPlan) to accommodate these contour pairs and the derivation of delineation probability maps. 3D probability maps were generated from the contour pairs based on the minimum Chebyshev distance, at each pixel within the contours, from each boundary. The probability associated with each pixel was defined as the ratio of its distance from the inner boundary to the sum of its distances from each boundary.These tools were used to create probability maps using contours produced by a clinician experienced in treating recurrent gynaecological cancers. The corresponding 50% probability volumes (GTVP50) were compared against the observers corresponding clinical GTV (GTVC) contour, created in accordance with local clinical protocols, using the Jaccard Index.Results:This approach was successfully implemented within the research TPS. Comparisons between GTVC and GTVP50 were made for 10 recurrences from 9 patients. GTVC was larger than GTVP50 (p&lt;0.01), with mean±s.d. volumes of 69.6±55.4cm3 and 61.6±53.7cm3 respectively, and a corresponding Jaccard Index of 0.83±0.11.Conclusion:Individual clinician's delineation uncertainty can be recorded and a corresponding uncertainty map derived. This is a crucial step in improving the handling of delineation uncertainty and moving away from population based margins, using techniques such as probabilistic treatment planning. Contours produced for recurrent gynaecological GTVs show the clinical GTV to be consistently larger than volume corresponding to 50% probability. Future work is to investigate non‐linear methods for generating these probability maps.This report is independent research arising from an NIHR/HEE Healthcare Science Doctoral Research Fellowship, David Bernstein, HCS‐DRF‐2014‐05‐005, supported by National Institute for Health Research (NIHR). The views expressed in this publication are those of the author(s); not necessarily those of the NHS, NIHR, Health Education England or Department of Health.

New Ions for Therapy.

Charged particle therapy (CPT) is currently based on the use of protons or carbon ions for the treatment of deep-seated and/or radioresistant tumors, which are known to return poor prognosis in photon treatments. A renovated interest has recently been observed in the possibility of extending the spectrum of ions used in CPT. The potential and limitations of different particle species will be discussed in this work, with special regard to 1H, 4He, 12C, and 16O, that is, those presently available in the most advanced particle therapy clinical centers. Literature information has been screened, as well as additional analysis has been performed, aimed at the comparison of basic physical and biological properties of several ions. The research treatment planning system TRiP98 is also employed to compare the dose distribution resulting from exposure to the different ions in different configurations, including the irradiation of hypoxic targets. Particles of intermediate charge, such as helium and lithium, offer an increased physical selectivity compared with protons, while having reduced biological effectiveness compared with carbon. The latter aspect translates into a less sensitive biological optimization of CPT treatments, though still more effective than protons in killing cancer cells. At the same time, in view of their increased linear energy transfer, heavier ions, like oxygen, are considered attractive, especially for the treatment of hypoxic tumors. While the higher biological dose released in the entrance dose represents in general a drawback for ions heavier than carbon, for oxygen beam this effect may be balanced by the lower dose increase requested to overcome hypoxia. The possibility of delivering radiation quality-optimized CPT treatments seems to be the new challenge in heavy ion therapy. The potential and limitations of different particle species, according to different sensitivity and morphological scenarios, makes combined treatments of different ions an intriguing option. New ions could open new scenarios in cancer therapy, but would represent as well an opportunity for the treatment of specific non-cancer disease such as atrial fibrillation.

EUD-based biological optimization for carbon ion therapy.

Treatment planning for carbon ion therapy requires an accurate modeling of the biological response of each tissue to estimate the clinical outcome of a treatment. The relative biological effectiveness (RBE) accounts for this biological response on a cellular level but does not refer to the actual impact on the organ as a whole. For photon therapy, the concept of equivalent uniform dose (EUD) represents a simple model to take the organ response into account, yet so far no formulation of EUD has been reported that is suitable to carbon ion therapy. The authors introduce the concept of an equivalent uniform effect (EUE) that is directly applicable to both ion and photon therapies and exemplarily implemented it as a basis for biological treatment plan optimization for carbon ion therapy. In addition to a classical EUD concept, which calculates a generalized mean over the RBE-weighted dose distribution, the authors propose the EUE to simplify the optimization process of carbon ion therapy plans. The EUE is defined as the biologically equivalent uniform effect that yields the same probability of injury as the inhomogeneous effect distribution in an organ. Its mathematical formulation is based on the generalized mean effect using an effect-volume parameter to account for different organ architectures and is thus independent of a reference radiation. For both EUD concepts, quadratic and logistic objective functions are implemented into a research treatment planning system. A flexible implementation allows choosing for each structure between biological effect constraints per voxel and EUD constraints per structure. Exemplary treatment plans are calculated for a head-and-neck patient for multiple combinations of objective functions and optimization parameters. Treatment plans optimized using an EUE-based objective function were comparable to those optimized with an RBE-weighted EUD-based approach. In agreement with previous results from photon therapy, the optimization by biological objective functions resulted in slightly superior treatment plans in terms of final EUD for the organs at risk (OARs) compared to voxel-based optimization approaches. This observation was made independent of the underlying objective function metric. An absolute gain in OAR sparing was observed for quadratic objective functions, whereas intersecting DVHs were found for logistic approaches. Even for considerable under- or overestimations of the used effect- or dose-volume parameters during the optimization, treatment plans were obtained that were of similar quality as the results of a voxel-based optimization. EUD-based optimization with either of the presented concepts can successfully be applied to treatment plan optimization. This makes EUE-based optimization for carbon ion therapy a useful tool to optimize more specifically in the sense of biological outcome while voxel-to-voxel variations of the biological effectiveness are still properly accounted for. This may be advantageous in terms of computational cost during treatment plan optimization but also enables a straight forward comparison of different fractionation schemes or treatment modalities.

SU‐F‐BRD‐10: Improving Plan Delivery Efficiency of Intensity Modulated Proton Plans with Prioritized Optimization

Purpose:To integrate treatment delivery time into plan optimization in spot scanning intensity modulated proton therapy. Utilizing a dedicated research treatment planning system we present an optimization approach to explore the trade‐off between the correlated parameters treatment time and plan quality on an astrocytoma patient case.Methods:The planning system is based on prioritized optimization, a stepwise approach of implementing clinical goals. After each optimization step, dosimetric achievements are turned into hard constraints to maintain the achieved plan quality. Prior achievements can be violated by a so‐called slip‐factor which allows to study possible trade‐offs of conflicting goals. Plan quality is obtained in the first two steps, while the third step optimizes delivery efficiency by working on the spot weight distribution via four alternativeMethods:elimination of low weighted spots (1), elimination of spots hardly contributing to PTV dose, followed by reoptimization of the resulting smaller optimization problem (2), reduction of spot weights variance within each energy layer (3), and reduction of the overall spot weight sum (4). Treatment times were calculated assuming either constant or variable beam current depending on the lowest spot weight.Results:Delivery efficiency can be improved remarkably without influencing the plan quality. Absolute time savings depend on the utilized method and facility properties. By varying slip‐factor and spot reduction limits, a border of worsening quality is detectable for all methods.Deleting low weighted spots by 10% results in a noticeable decrease in minimum target dose. Further reduction results in more heterogeneous dose distributions and insufficient coverage. Option 2 showed constant plan quality for spot reductions of more than 10%.Conclusion:Including treatment time optimization as a final step into prioritized optimization allows for more efficient treatment plans by redistributing the spot patterns without compromising plan quality, which has to remain the main intent of radiotherapy treatment.The authors report grants from German Research Foundation (DFG) during the conduct of the study.

Ion therapy of prostate cancer: daily rectal dose reduction by application of spacer gel.

BackgroundIon beam therapy represents a promising approach to treat prostate cancer, mainly due to its high conformity and radiobiological effectiveness. However, the presence of prostate motion, patient positioning and range uncertainties may deteriorate target dose and increase exposure of organs at risk. Spacer gel injected between prostate and rectum may increase the safety of prostate cancer (PC) radiation therapy by separating the rectum from the target dose field. The dosimetric impact of the application of spacer gel for scanned carbon ion therapy of PC has been analyzed at Heidelberg Ion-Beam Therapy Center (HIT).Materials and methodsThe robustness of ion therapy treatment plans was investigated by comparison of two data sets of patients treated with and without spacer gel. A research treatment planning system for ion therapy was used for treatment plan optimization and calculation of daily dose distributions on 2 to 9 Computed Tomography (CT) studies available for each of the 19 patients. Planning and daily dose distributions were analyzed with respect to target coverage, maximal dose to the rectum (excluding 1 ml of the greatest dose; Dmax-1 ml) and the rectal volume receiving dose greater than 90% of prescribed target dose (V90Rectum), respectively.ResultsThe application of spacer gel did substantially diminish rectum dose. Dmax-1 ml on the treatment planning CT was on average reduced from 100.0 ± 1.0% to 90.2 ± 4.8%, when spacer gel was applied. The robustness analysis performed with daily CT studies demonstrated for all analyzed patient cases that application of spacer gel results in a decrease of the daily V90Rectum index, which calculated over all patient cases and CT studies was 10.2 ± 10.4 [ml] and 1.1 ± 2.1 [ml] for patients without and with spacer gel, respectively.ConclusionsThe dosimetric benefit of increasing the distance between prostate and rectum using spacer gel for PC treatment with carbon ion beams has been quantified. Application of spacer gel substantially reduced rectal exposure to high treatment dose and, therefore, can reduce the hazard of rectal toxicity in ion beam therapy of PC. The results of this study enable modifications of the PC ion therapy protocol such as dose escalation or hypofractionation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13014-015-0348-1) contains supplementary material, which is available to authorized users.

Robustness of target dose coverage to motion uncertainties for scanned carbon ion beam tracking therapy of moving tumors

Beam tracking with scanned carbon ion radiotherapy achieves highly conformal target dose by steering carbon pencil beams to follow moving tumors using real-time magnetic deflection and range modulation. The purpose of this study was to evaluate the robustness of target dose coverage from beam tracking in light of positional uncertainties of moving targets and beams. To accomplish this, we simulated beam tracking for moving targets in both water phantoms and a sample of lung cancer patients using a research treatment planning system. We modeled various deviations from perfect tracking that could arise due to uncertainty in organ motion and limited precision of a scanned ion beam tracking system. We also investigated the effects of interfractional changes in organ motion on target dose coverage by simulating a complete course of treatment using serial (weekly) 4DCTs from six lung cancer patients. For perfect tracking of moving targets, we found that target dose coverage was high ( was 94.8% for phantoms and 94.3% for lung cancer patients, respectively) but sensitive to changes in the phase of respiration at the start of treatment and to the respiratory period. Phase delays in tracking the moving targets led to large degradation of target dose coverage (up to 22% drop for a 15° delay). Sensitivity to technical uncertainties in beam tracking delivery was minimal for a lung cancer case. However, interfractional changes in anatomy and organ motion led to large decreases in target dose coverage (target coverage dropped approximately 8% due to anatomy and motion changes after 1 week). Our findings provide a better understand of the importance of each of these uncertainties for beam tracking with scanned carbon ion therapy and can be used to inform the design of future scanned ion beam tracking systems.