Commercial Treatment Planning System Research Articles

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

PurposeTo investigate the accuracy of the treatment planning system (TPS) TRiP4D in reproducing doses computed by the clinically used TPS SyngoRT. MethodsProton 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 (ΔDmean,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. ResultsThe carbon and proton absorbed dose gamma passing rates (γ-PRs) were ≥99.93% and ΔDmean,T smaller than −0.22%. On average, the RBE-weighted dose Dmean,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 ΔDmean, 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. ΔDmean,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%). ConclusionThe 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).

Automated treatment planning for proton pencil beam scanning using deep learning dose prediction and dose-mimicking optimization.

The purpose of this study is to investigate the use of a deep learning architecture for automated treatment planning for proton pencil beam scanning (PBS). A 3-dimensional (3D) U-Net model has been implemented in a commercial treatment planning system (TPS) that uses contoured regions of interest (ROI) binary masks as model inputs with a predicted dose distribution as the model output. Predicted dose distributions were converted to deliverable PBS treatment plans using a voxel-wise robust dose mimicking optimization algorithm. This model was leveraged to generate machine learning (ML) optimized plans for patients receiving proton PBS irradiation of the chest wall. Model training was carried out on a retrospective set of 48 previously-treated chest wall patient treatment plans. Model evaluation was carried out by generating ML-optimized plans on a hold-out set of 12 contoured chest wall patient CT datasets from previously treated patients. Clinical goal criteria and gamma analysis were used to compare dose distributions of the ML-optimized plans against the clinically approved plans across the test patients. Statistical analysis of mean clinical goal criteria indicates that compared to the clinical plans, the ML optimization workflow generated robust plans with similar dose to the heart, lungs, and esophagus while achieving superior dosimetric coverage to the PTV chest wall (clinical mean V95=97.6% vs. ML mean V95=99.1%, p<0.001) across the 12 test patients. ML-based automated treatment plan optimization using the 3D U-Net model can generate treatment plans of similar clinical quality compared to human-driven optimization.

A PIN photodiode ionizing radiation detector with small angular dependence and low buildup

PurposeTo develop an improved PIN photodiode detector that has low angular dependence and small buildup. Show that the measured dose compares well with the dose calculated by a treatment planning system for static fields at oblique angles and rotationally delivered fields. MethodsCommercially available PIN photodiodes, VEMD1060X01, (Vishay Semiconductor, Shelton, CT), are fabricated into detectors that can be used for measurement of ionizing radiation. PIN photodiodes are soldered onto coaxial cables in an edge-on orientation and are mounted in a symmetrical package. Detector signals are measured with an electrometer with zero input bias voltage. Dose is measured from a Varian Trilogy (Varian Medical Systems, Milpitas, CA) linear accelerators 6 MV photon and 6 MeV electron beams. An Eclipse (Varian Medical Systems, Milpitas, CA) commercial treatment planning system Acuros and electron Monte Carlo algorithms are used for calculating dose expected in various irradiation geometries. ResultsThe single diode detector has an angular dependence between −1.1% and +1.2% and a buildup of 0.6 mm water equivalence. Measurements of dose at 0.3, 0.5, 1.0 and 1.5 cm depth from oblique static beams and from intensity modulated radiation therapy (IMRT) delivered beams agree with planning system calculations within ±1.5%. ConclusionsA clinically useable dose detector can be fabricated from a PIN type photodiode. This diode detector can be used to measure dose at the surface of a patient under a bolus if one is used. Measurements agree with calculations for static fields at oblique angles as well as rotational IMRT delivered beams.

Validation of the relative biological effectiveness of active-energy scanning carbon-ion radiotherapy on a commercial treatment planning system with a microdosimetic kinetic model

BackgroundThe study objective was to validate the relative biological effectiveness (RBE) calculated by the modified microdosimetric kinetic model in RayStation (Ray-MKM) for active-energy scanning carbon-ion radiotherapy.MethodsThe Ray-MKM was benchmarked using a spread-out Bragg-peak (SOBP) plan, which was suggested in literature from the National Institute of Radiobiological Science (NIRS) in Japan. The residual RBE differences from the MKM at NIRS (NIRS-MKM) were derived using several SOBP plans with different ranges, SOBP widths, and prescriptions. To investigate the origins of the differences, we compared the saturation-corrected dose-mean specific energy Z_{1D}^{*} of the aforementioned SOBPs. Furthermore, we converted the RBE-weighted doses with the Ray-MKM to those with local effect model I (LEM doses). The purpose was to investigate whether the Ray-MKM could reproduce the RBE-weighted conversion study.ResultsThe benchmark determined the value of the clinical dose scaling factor, F_{clin}, as 2.40. The target mean RBE deviations between the Ray-MKM and NIRS-MKM were median: 0.6 (minimum: 0.0 to maximum: 1.69) %. The Z_{1D}^{*} difference in-depth led to the RBE difference in-depth and was remarkable at the distal end. The converted LEM doses from the Ray-MKM doses were comparable (the deviation being − 1.8–0.7%) to existing literature.ConclusionThis study validated the Ray-MKM based on our active-energy scanning carbon-ion beam via phantom studies. The Ray-MKM could generate similar RBEs as the NIRS-MKM after benchmarking. Analysis based on Z_{1D}^{*} indicated that the different beam qualities and fragment spectra caused the RBE differences. Since the absolute dose differences at the distal end were small, we neglected them. Furthermore, each centre may determine its centre-specific F_{clin} based on this approach.

Accuracy and reliability of a commercial treatment planning system in nontarget regions in modern prostate radiotherapy.

The currently available treatment planning systems (TPSs) are neither designed nor intended for accurate dose calculations in nontarget regions. The aim of this work is to quantify the accuracy and reliability of nontarget doses calculated by a commercially available TPS. Nontarget doses calculated by the collapsed cone (CC) (v5.2) algorithm implemented in the RayStation (v6) TPS were compared to measured values. Different scenarios were investigated, from simple static fields to intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) treatment plans. Deviations and confidence limits (CLs) were calculated between results of calculations and measurements-applying both local (δ) and global (Δ) normalization-for various points of interest (POIs). Results were based on a single-institution experience for one clinical test case (prostate) and evaluated against internationally accepted criteria. Overall, the TPS underestimated the nontarget dose by an average of -17.7%±25.3% for IMRT. Quantitatively similar results were obtained for VMAT (-17.6%±21.2%). POIs receiving < 5% of the prescription dose were significantly underestimated by the TPS (p-value<0.05 for both IMRT and VMAT). Dose calculation accuracy was also determined by the contribution of secondary radiation, with measured doses for out-of-field POIs being significantly different from calculated values (p-value<0.01 for both IMRT and VMAT). Although the CLδ in nontarget regions failed the proposed tolerance criteria (40%) for both IMRT (68.8%) and VMAT (52.6%), the CLΔ was within the tolerance limit (4%) for both treatment techniques (1.9% for IMRT and 1.3% for VMAT). No action levels (7%) were exceeded. Based on the currently available benchmarks our TPS is considered acceptable for clinical use, although the dose in some POIs was poorly predicted by the CC algorithm. Some areas were pointed out where TPSs and linear accelerator control systems can be improved.

Commissioning and dosimetric validation of a novel compensator-based Co-60 IMRT system for evaluating suitability to automated treatment planning.

A novel compensator-based system has been proposed which delivers intensity-modulated radiation therapy (IMRT) with cobalt-60 beams. This could improve access to advanced radiotherapy in low- and middle-income countries. For this system to be clinically viable and to be adapted into the Radiation Planning Assistant (RPA), being developed to offer automated planning services in low- and middle-income countries, it is necessary to commission and validate it in a commercial treatment planning system (TPS). The novel treatment device considered here employs a cobalt-60 source and nine compensators. Each compensator is produced by 3-D printing a thin plastic mold which is then filled on-demand within the machine with reusable 2-mm-diameter spherical tungsten balls. This system was commissioned in the Eclipse TPS and validation tests were conducted with Monte Carlo using Geant4 Application for Tomographic Emission for percentage depth dose, in-plane profiles, penumbra, and IMRT dose validation. And the American Association of Physicists in Medicine Task Group 119 benchmarking testing was performed. Additionally, compensator-based cobalt-60 IMRT plans were created for 46 head-and-neck cancer cases and compared to the linac-based volumetric modulated arc therapy (VMAT) plans used clinically, then dosimetric parameters were evaluated. Beam-on time for each field was calculated. In addition, the measurement was also performed in a limited environment and compared with the Monte Carlo simulations. The differences in percent depth doses and in-plane profiles between the Eclipse and Monte Carlo simulations were 0.65%±0.41% and 1.02%±0.99%, respectively, and the 80%-20% penumbra agreed within 0.46±0.27mm. For the Task Group 119 validation plans, all treatment planning goals were met and gamma passing rates were>95% (3%/3mm criteria). In 46 clinical head-and-neck cases, the cobalt-60 compensator-based IMRT plans had planning target volume (PTV) coverages similar to linac-based VMAT plans: all dosimetric values for PTV were within 1.5%. The organs at risk dose parameters were somewhat higher in cobalt-60 compensator-based IMRT plans versus linac-based VMAT plans. The mean dose differences for the spinal cord, brain, and brainstem were 4.43±1.92, 3.39±4.67, and 2.40±3.71Gy, while those for the rest of the organs were<1Gy. The average beam-on time per field was 0.42±0.10min for the 6 MV multi-leaf-collimator plans while those for the cobalt-60 compensator plans were 0.17±0.01 and 0.31±0.01min at the dose rates of 350 and 175 cGy/min. There was a good agreement between in-plane profiles from measurements and Monte Carlo simulations, which differences are 1.34±1.90% and 0.13±2.16% for two different fields. A novel compensator-based IMRT system using cobalt-60 beams was commissioned and validated in a commercial TPS. Plan quality with this system was comparable to that of linac-based plans in all test cases with shorter estimated beam-on times. This system enables reliable, high-quality plans with reduced cost and complexity and may have benefits for underserved regions of the world. This system is being integrated into the RPA, a web-based platform for auto-contouring and auto-planning.

Machine learning-based automatic proton therapy planning: impact of post-processing and dose-mimicking in plan robustness.

Automated treatment planning strategies are being widely implemented in clinical routine to reduce inter-planner variability, speed up the optimization process, and improve plan quality. This study aims to evaluate the feasibility and quality of intensity-modulated proton therapy (IMPT) plans generated with four different knowledge-based planning (KBP) pipelines fully integrated into a commercial treatment planning system (TPS). A data set containing 60 oropharyngeal cancer patients was split into 11 folds, each containing 47 patients for training, 5 patients for validation and 5 patients for testing. A dose prediction model was trained on each of the folds, resulting in a total of 11 models. Three patients were left out in order to assess if the differences introduced between models were significant. From voxel-based dose predictions, we analyze the two steps that follow the dose prediction: post-processing of the predicted dose and dose mimicking (DM). We focused on the effect of post-processing (PP) or no post-processing (NPP) combined with two different DM algorithms for optimization: the one available in the commercial TPS RayStation (RSM) and a simpler isodose-based mimicking (IBM). Using 55 test patients (5 test patients for each model), we evaluated the quality and robustness of the plans generated by the four proposed KBP pipelines (PP-RSM, PP-IBM, NPP-RSM, NPP-IBM). After robust evaluation, dose-volume histogram (DVH) metrics in nominal and worst-case scenarios were compared to those of the manually generated plans. Nominal doses from the four KBP pipelines showed promising results achieving comparable target coverage and improved dose to organs at risk (OARs) compared to the manual plans. However, too optimistic post-processing applied to the dose prediction (i.e. important decrease of the dose to the organs) compromised the robustness of the plans. Even though RSM seemed to partially compensate for the lack of robustness in the PP plans, still 65% of the patients did not achieve the expected robustness levels. NPP-RSM plans seemed to achieve the best trade-off between robustness and OAR sparing. PP and DM strategies are crucial steps to generate acceptable robust and deliverable IMPT plans from ML-predicted doses. Before the clinical implementation of any KBP pipeline, the PP and DM parameters predefined by the commercial TPS need to be modified accordingly with a comprehensive feedback loop in which the robustness of the final dose calculations is evaluated. With the right choice of PP and DM parameters, KBP strategies have the potential to generate IMPT plans within clinically acceptable levels comparable to plans manually generated by dosimetrists. This article is protected by copyright. All rights reserved.

Evaluation of a dedicated software for semi-automated VMAT planning of spine Stereotactic Body Radiotherapy (SBRT)

PurposeTo determine whether SBRT of spinal metastasis using a dedicated treatment planning system (TPS) and delivered with a gantry-based LINAC could provide plans of similar quality to the Cyberknife technology. Additional comparison was also done with other commercial TPS used for volumetric modulated arc therapy (VMAT) planning. Materials and MethodsThirty Spine SBRT patients, previously treated in our institution with CyberKnife (Accuray, Sunnyvale) using Multiplan TPS, were replanned in VMAT with an dedicated TPS (Elements Spine SRS, Brainlab, Munich) and our clinical TPS (Monaco, Elekta LTD, Stockholm), using exactly the same arc geometry. The comparison was done by assessing differences in dose delivered to PTV, CTV and spinal cord, calculating modulation complexity scores (MCS) and performing quality control (QA) of the plans. ResultsRegardless of the vertebra level, in general, no statistical difference was found in PTV coverage between all TPS. Conversely, PTV and CTV D50% were found significantly higher for the dedicated TPS compared to others. In addition, the dedicated TPS also resulted in better gradient index (GI) than clinical VMAT TPS, whatever the vertebral level, and better GI than Cyberknife TPS for the thoracic level only. The D2% to the spinal cord was generally significantly lower with the dedicated TPS compared with others. No significant difference was found in the MCS between both VMAT TPS. All QA were clinically acceptable. ConclusionThe Elements Spine SRS TPS offers very effective and user-friendly semi-automated planning tools and is secure and promising for gantry-based LINAC spinal SBRT.

Built-in wavelet-induced smoothness to reduce plan complexity in intensity modulated radiation therapy (IMRT)

Objective. Reducing plan complexity in intensity modulated radiation therapy (IMRT) to ensure dosimetric accuracy and delivery efficiency of the radiation treatment plans. We propose a novel approach by representing the beamlet intensities using an incomplete wavelet basis that explicitly excludes fluctuating intensity maps from the decision space (explicit hard constraint). This technique provides a built-in wavelet-induced smoothness that improves both dosimetric plan quality and delivery efficiency. Approach. The beamlet intensity maps need to be especially smooth in the leaf travel direction (referred to as the X-direction). We treat the intensity map of each beam as a 2D image and represent it using the wavelets corresponding to low-frequency changes in the X-direction (i.e. approximation and horizontal). The absence of wavelets corresponding to high-frequency changes (i.e. vertical and diagonal) induces built-in smoothness. We still utilize a regularization term in the objective function to promote smoothness in the Y-direction (perpendicular to the X-direction) and further possible smoothness in the X-direction. This technique has been tested on three patient cases of different disease sites (paraspinal, lung, prostate) and all final evaluations and comparisons have been performed on an FDA-approved commercial treatment planning system (Varian EclipseTM). Main results. Wavelet-induced smoothness reduced monitor units by about 10%, 45%, and 14% for paraspinal, lung, and prostate cases, respectively. It also improved organ at risk sparing, especially on the complex paraspinal case where it resulted in about 7%, 13%, and 14% less mean dose to esophagus, lung, and cord, respectively. Moreover, built-in wavelet-induced smoothness desensitizes the results to changing the weight associated to the regularization term, and thereby mitigates the weight fine-tuning difficulty. Significance. Fluence smoothness is often achieved by smoothing the beamlet intensity maps using a proper regularization term in the objective function aiming at disincentivizing fluctuation in the beamlet intensities (implicit soft constraint). This work reports a novel application of wavelets in imposing an explicit smoothness hard constraint in the search space using an incomplete wavelet basis. This idea has been successfully applied to exclude complex and clinically irrelevant radiation plans from the search space. The code and pertained models along with a sample dataset are released on our LowDimRT GitHub (https://github.com/PortPy-Project/LowDimRT).

Intensity modulated proton arc therapy via geometry-based energy selection for ependymoma.

We developed and tested a novel method of creating intensity modulated proton arc therapy (IMPAT) plans that uses computing resources similar to those for regular intensity-modulated proton therapy (IMPT) plans and may offer a dosimetric benefit for patients with ependymoma or similar tumor geometries. Our IMPAT planning method consists of a geometry-based energy selection step with major scanning spot contributions as inputs computed using ray-tracing and single-Gaussian approximation of lateral spot profiles. Based on the geometric relation of scanning spots and dose voxels, our energy selection module selects a minimum set of energy layers at each gantry angle such that each target voxel is covered by sufficient scanning spots as specified by the planner, with dose contributions above the specified threshold. Finally, IMPAT plans are generated by robustly optimizing scanning spots of the selected energy layers using a commercial proton treatment planning system (TPS). The IMPAT plan quality was assessed for four ependymoma patients. Reference three-field IMPT plans were created with similar planning objective functions and compared with the IMPAT plans. In all plans, the prescribed dose covered 95% of the clinical target volume (CTV) while maintaining similar maximum doses for the brainstem. While IMPAT and IMPT achieved comparable plan robustness, the IMPAT plans achieved better homogeneity and conformity than the IMPT plans. The IMPAT plans also exhibited higher relative biological effectiveness (RBE) enhancement than did the corresponding reference IMPT plans for the CTV in all four patients and brainstem in three of them. The proposed method demonstrated potential as an efficient technique for IMPAT planning and may offer a dosimetric benefit for patients with ependymoma or tumors in close proximity to critical organs. IMPAT plans created using this method had elevated RBE enhancement associated with increased linear energy transfer (LET) in both targets and abutting critical organs.

Treatment plan optimisation for reirradiation

BackgroundThe STRIDeR (Support Tool for Re-Irradiation Decisions guided by Radiobiology) project aims to create a clinically viable re-irradiation planning pathway within a commercial treatment planning system (TPS). Such a pathway should account for previously delivered dose, voxel-by-voxel, taking fractionation effects, tissue recovery and anatomical changes into account. This work presents the workflow and technical solutions in the STRIDeR pathway. MethodsThe pathway was implemented in RayStation (version 9B DTK) to allow an original dose distribution to be used as background dose to guide optimisation of re-irradiation plans. Organ at risk (OAR) planning objectives in equivalent dose in 2 Gy fractions (EQD2) were applied cumulatively across the original and re-irradiation treatments, with optimisation of the re-irradiation plan performed voxel-by-voxel in EQD2. Different approaches to image registration were employed to account for anatomical change. Data from 21 patients who received pelvic Stereotactic Ablative Radiotherapy (SABR) re-irradiation were used to illustrate the use of the STRIDeR workflow. STRIDeR plans were compared to those produced using a standard manual method. ResultsThe STRIDeR pathway resulted in clinically acceptable plans in 20/21 cases. Compared to plans produced using the laborious manual method, less constraint relaxation was required or higher re-irradiation doses could be prescribed in 3/21. ConclusionThe STRIDeR pathway used background dose to guide radiobiologically meaningful, anatomically-appropriate re-irradiation treatment planning within a commercial TPS. This provides a standardised and transparent approach, offering more informed re-irradiation and improved cumulative OAR dose evaluation.

An integrated solution of deep reinforcement learning for automatic IMRT treatment planning in non-small-cell lung cancer.

To develop and evaluate an integrated solution for automatic intensity-modulated radiation therapy (IMRT) planning in non-small-cell lung cancer (NSCLC) cases. A novel algorithm named as multi-objectives adjustment policy network (MOAPN) was proposed and trained to learn how to adjust multiple optimization objectives in commercial Eclipse treatment planning system (TPS), based on the multi-agent deep reinforcement learning (DRL) scheme. Furthermore, a three-dimensional (3D) dose prediction module was developed to generate the patient-specific initial optimization objectives to reduce the overall exploration space during MOAPN training. 114 previously treated NSCLC cases suitable for stereotactic body radiotherapy (SBRT) were selected from the clinical database. 87 cases were used for the model training, and the remaining 27 cases for evaluating the feasibility and effectiveness of MOAPN in automatic treatment planning. For all tested cases, the average number of adjustment steps was 21 ± 5.9 (mean ± 1 standard deviation). Compared with the MOAPN initial plans, the actual dose of chest wall, spinal cord, heart, lung (affected side), esophagus and bronchus in the MOAPN final plans reduced by 14.5%, 11.6%, 4.7%, 16.7%, 1.6% and 7.7%, respectively. The dose result of OARs in the MOAPN final plans was similar to those in the clinical plans. The complete automatic treatment plan for a new case was generated based on the integrated solution, with about 5-6min. We successfully developed an integrated solution for automatic treatment planning. Using the 3D dose prediction module to obtain the patient-specific optimization objectives, MOAPN formed action-value policy can simultaneously adjust multiple objectives to obtain a high-quality plan in a shorter time. This integrated solution contributes to improving the efficiency of the overall planning workflow and reducing the variation of plan quality in different regions and treatment centers. Although improvement is warranted, this proof-of-concept study has demonstrated the feasibility of this integrated solution in automatic treatment planning based on the Eclipse TPS.

The accuracy of out of field dose calculations in commercial treatment planning system using GATE/GEANT4 Monte Carlo simulation

The out-of-field dose has particular importance because of the increased risk of radiation-induced second cancer and late effects due to low doses for out-of-field organs at risk (OARs). Although the out-of-field doses are clinically related to the doses to OARs, they receive less attention and are not well documented because of the limitations in the treatment planning system (TPS) in calculating them. Today, Monte Carlo (MC) is considered the most accurate and alternate method in calculation of the dose distribution. This study is evaluated the accuracy of the out-of-field dose calculated by the commercial PROWESS©V.5.01 TPS. A comparison for out-of-field dose profiles (DPs) between TPS and MC for field sizes from 10 × 10 cm 2 up to 20 × 20 cm 2 with an increment of 5 cm at depths 1.5, 5, and 10 cm. Out-of-field DPs were calculated for each field size and each depth from the center of the treatment field with an increment of 2 mm along the in-plane direction to a certain distance at which the TPS reported zero doses. Each curve was normalized to the calculated dose at CAX. The relation for the dependence of the out-of-field doses on the field size and depth in the phantom was also evaluated. The comparison between TPS and MC showed that the PROWESS©V.5.01 TPS underestimated the out-of-field dose compared with MC simulation. The degree of the underestimation of dose increases as the distance from the field edge increases. The out-of-field dose depends on field size and depth.Limitations of PROWESS TPS to detect the out-of-field doses could be lead to inaccuracies of underestimation of secondary cancer induction risk associated with these doses. The quantifying of the accuracy of the out-of-field dose calculations by Prowess TPS resulted in underestimated for the out-of-field doses compared with Gate MC simulation in the range from 34% to 65% at different field sizes and depths. The underestimation of the out-of-field dose increased to 65% as the field size and depth increased.

Towards artificial intelligence-based automated treatment planning in clinical practice: A prospective study of the first clinical experiences in high-dose-rate prostate brachytherapy

This prospective study evaluates our first clinical experiences with the novel ``BRachytherapy via artificial Intelligent GOMEA-Heuristic based Treatment planning''(BRIGHT) applied to high-dose-rate prostate brachytherapy. Between March 2020 and October 2021, 14 prostate cancer patients were treated in our center with a 15Gy HDR-brachytherapy boost. BRIGHT was used for bi-objective treatment plan optimization and selection of the most desirable plans from a coverage-sparing trade-off curve. Selected BRIGHT plans were imported into the commercial treatment planning system Oncentra Brachy . In Oncentra Brachy a dose distribution comparison was performed for clinical plan choice, followed by manual fine-tuning of the preferred BRIGHT plan when deemed necessary. The reasons for plan selection, clinical plan choice, and fine-tuning, as well as process speed were monitored. For each patient, the dose-volume parameters of the (fine-tuned) clinical plan were evaluated. In all patients, BRIGHT provided solutions satisfying all protocol values for coverage and sparing. In four patients not all dose-volume criteria of the clinical plan were satisfied after manual fine-tuning. Detailed information on tumour coverage, dose-distribution, dwell time pattern, and insight provided by the patient-specific trade-off curve, were used for clinical plan choice. Median time spent on treatment planning was 42 min, consisting of 16 min plan optimization and selection, and 26 min undesirable process steps. BRIGHT is implemented in our clinic and provides automated prostate high-dose-rate brachytherapy planning with trade-off based plan selection. Based on our experience, additional optimization aims need to be implemented to further improve direct clinical applicability of treatment plans and process efficiency.

Technical note: In silico benchmarking of the linear energy transfer-based functionalities for carbon ion beams in a commercial treatment planning system.

The increasing number of studies dealing with linear energy transfer (LET)-based evaluation and optimization in the field of carbon ion radiotherapy (CIRT) indicates the rising demand for LET implementation in commercial treatment planning systems (TPS). Benchmarking studies could play a key role in detecting (and thus preventing) computation errors prior implementing such functionalities in aTPS. This in silico study was conducted to benchmark the following two LET-related functionalities in a commercial TPS against Monte Carlo simulations: (1) dose averaged LET (LETd ) scoring and (2) physical dose filtration based on LET for future LET-based treatment plan evaluation and optimization studies. The LETd scoring and LET-based dose filtering (in which the deposited dose can be separated into the dose below and above the user specified LET threshold) functionalities for carbon ions in the research version RayStation (RS) 9A-IonPG TPS (RaySearch Laboratories, Sweden) were benchmarked against GATE/Geant4 simulations. Pristine Bragg peaks (BPs) and cuboid targets, positioned at different depths in a homogeneous water phantom and a setup with heterogeneity were used for thisstudy. For all setups (homogeneous and heterogeneous), the mean absolute (and relative) LETd difference was less than 1 keV/ m (3.5%) in the plateau and target and less than 2 keV/ m (8.3%) in the fragmentation tail. The maximum local differences were 4 and 6 keV/ m, respectively. The mean absolute (and relative) physical dose differences for both low- and high-LET doses were less than 1 cGy (1.5%) in the plateau, target and tail with a maximum absolute difference of 2cGy. No computation error was found in the tested functionalities except for LETd in lateral direction outside the target, showing the limitation of the implemented monochrome model in the tested TPSversion.

Cobalt compensator-based IMRT device: A treatment planning study of head and neck cases

PurposeOur goal is to develop a novel cobalt-compensator-based IMRT device for low- and middle-income countries that is reliable and cost-effective while delivering treatment plans of equal quality to those from linac-MLC devices. The present study examines the quality of treatment plans using this device. MethodsA commercial treatment planning system (TPS; RayStation v.8B) was commissioned for this device using Monte Carlo simulations from the Geant4 toolkit. Patient-specific compensators were created as regions-of-interest. Thirty clinical head & neck cases were planned and compared to clinical plans with a 6MV linac using IMRT. The mock head and neck plan from TG-119 was used for further validation. ResultsPTV objectives were achieved in all 30 plans with PTV V95% >95 %. OAR sparing was similar to clinical plans. There were 14 cases where OAR dose limits exceeded the recommended QUANTEC limits in the clinical plan in order to achieve target coverage. OAR sparing was better in the cobalt compensator plan in 8 cases and worse in 3 cases, in the latter cases exceeding the clinical plan doses by an average of 8.22 % (0.0 %–13.5 %). Average field-by-field gamma pass-rate were 93.7 % (2 %/2mm). Estimated treatment times using the Co-60 compensator device were 1 min 27 s vs 1 min 2 s for the clinical system. ConclusionThis system is the first of its kind to allow for IMRT with a Co-60 device. Data here suggests that the delivery meets plan quality criteria while maintaining short treatment times which may offer a sustainable and cost-low option for IMRT on the global scale.

Evaluation of treatment planning system accuracy in estimating the stopping‐power ratio of immobilization devices for proton therapy

To assess treatment planning system (TPS) accuracy in estimating the stopping-power ratio (SPR) of immobilization devices commonly used in proton therapy and to evaluate the dosimetric effect of SPR estimation error for a set of clinical treatment plans. Computed tomography scans of selected clinical immobilization devices were acquired. Then, the water-equivalent thickness (WET) and SPR values of these devices based on the scans were estimated in a commercial TPS. The reference SPR of each device was measured using a multilayer ion chamber (MLIC), and the differences between measured and TPS-estimated SPRs were calculated. These findings were utilized to calculate corrected dose distributions of 15 clinical proton plans for three treatment sites: extremity, abdomen, and head-and-neck. The original and corrected dose distributions were compared using a set of target and organs-at-risk (OARs) dose-volume histogram (DVH) parameters. On average, the TPS-estimated SPR was 19.5% lower (range, -35.1% to 0.2%) than the MLIC-measured SPR. Due to the relatively low density of most immobilization devices used, the WET error was typically <1mm, but up to 2.2mm in certain devices. Overriding the SPR of the immobilization devices to the measured values did not result in significant changes in the DVH metrics of targets and most OARs. However, some critical OARs showed noticeable changes of up to 6.7% in maximum dose. The TPS tends to underestimate the SPR of selected proton immobilization devices by an average of about 20%, but this does not induce major WET errors because of the low density of the devices. The dosimetric effect of this SPR error was negligible for most treatment sites, although the maximum dose of a few OARs exhibited noticeable variations.

Applying mixed-integer linear programming to the non-coplanar beam angle optimization of intensity-modulated radiotherapy for liver cancer.

Currently, intensity-modulated radiation therapy (IMRT) is commonly used in radiotherapy clinics. However, designing a treatment plan with multiple beam angles depends on the experience of human planners, and is mostly achieved using a trial-and-error approach. It is preferrable but challenging to solve this issue automatically and mathematically using an optimization approach. The goal of this study is to develop a mixed-integer linear programming (MILP) approach for the beam angle optimization (BAO) of non-coplanar IMRT for liver cancer. MILP models for the BAO of both coplanar and non-coplanar IMRT treatment plans were developed. The beam angles of the IMRT plans were first selected by the MILP model built using mathematical optimization software. Next, the IMRT plans with the selected beam angles was created in a commercial treatment planning system. Finally, the fluence map and dose distribution of the IMRT plans were generated under pre-defined dose-volume constraints. The IMRT plans of 10 liver cancer patients previously treated at our institute were used to assessed the proposed MILP models. For each patient, both coplanar and non-coplanar IMRT plans with beam angles optimized by the MILP models were compared with the IMRT plan clinically approved by physicians. The MILP model-guided IMRT plans showed reduced doses for most of the organs at risk (OARs). Compared with the IMRT plans clinically approved by physicians, the doses for the spinal cord (28.5 vs. 36.1, P=0.001<0.05) and liver (27.6 vs. 29.1, P=0.005<0.05) decreased significantly in the IMRT plans with non-coplanar beams selected by the MILP models. The MILP model is an effective tool for the BAO in coplanar and non-coplanar IMRT treatment planning. It facilitates the automation of IMRT treatment planning for current high-precision radiotherapy.

A method for adjusting MLC leaf positions outside a reference region for VMAT plans in a commercial treatment planning system.

Multileaf collimators (MLC) leaf positions in a volumetric modulated arc therapy (VMAT) plan is determined from inverse treatment planning process. On the isocenter plane, leaf projections should not be set too far out of projected PTV boundary. In this study we developed an automatic method of adjusting leaf positions outside a reference region of interest (ROI) for VMAT plans generated in Pinnacle treatment planning system (TPS). The proposed method consisted of a Pinnacle script and a Python program. It checked each MLC leaf position for all control points in a VMAT arc relative to a reference ROI created by adding a small margin to PTV. For leaves opened outside the reference ROI, the method adjusted their positions to reduce dose to normal tissue while maintaining PTV coverage and satisfying leaf position constraints. The deliverability and dose accuracy of the method was verified by applying it to 15 VMAT plans which were delivered in five different linacs in our department. Dosimetric improvement of the proposed method was analyzed for another group of 16 randomly selected VMAT plans. The average gamma passing rate using a 3%/3 mm criteria for the verification group of VMAT plans was 98.3% and all passing rates were above our internal passing threshold. Dosimetric improvement was observed for the evaluation group of VMAT plans. The method can improve normal tissue protection for VMAT plans. It can be safely applied in routine clinic work.

Evaluation of dose calculation accuracy of a commercial radiotherapy treatment planning system for adjacent radiation fields

Abstract Background: Adjacent radiation fields are applied in some radiotherapeutic cases. When using these radiation fields, considerable dose errors across the junction of radiation fields are possible. Therefore, it is necessary to evaluate the accuracy of the dose calculated by treatment planning system (TPS) when using the adjacent radiation fields. The present study aimed to quantify the dose calculation accuracy of ISOgray TPS for the photon-photon adjacent fields. Materials and methods: To assess the accuracy of dose calculations, the dose profiles were first measured by a Semiflex ionization chamber at 1, 1·5, 5 and 10 cm depths for different field sizes (6 × 6, 10 × 10 and 20 × 20 cm2), source to surface distances (SSDs) (90, 100 and 110 cm) and beam angles (0º, 15º, 30º and 45º). In the second step, the data at corresponding depths were extracted from the ISOgray TPS. Finally, the dosimetric performance of TPS was evaluated using a gamma index analysis. Results: The overall dose calculation accuracy of ISOgray TPS was within the acceptable range for the build-up region (with acceptance criteria of dose difference (DD) = 15% and distance to agreement (DTA) = 3 mm) and the depths after the build-up region (with acceptance criteria of DD = 5% and DTA = 3 mm). Moreover, the overall accuracy of dose calculations was not affected by the field size and the SSD. It was also shown that the accuracy of dose calculations was similar for the adjacent radiation fields with beam angles of 0º, 15 º and 30 º, while a considerable decrease in the pass rate values is obtained for the adjacent radiation field with 45 º beam angle. A more detailed analysis of the findings revealed that the accuracy of dose calculations in the match line regions of the adjacent radiation fields for 1 cm beam profiles was within the acceptable range; however, it declined for other depths. Conclusions: The findings showed that the overall dose calculation accuracy of ISOgray TPS was acceptable for evaluated adjacent radiation fields. However, the accuracy of dose calculations in the match line regions of the adjacent radiation fields for the depth after build-up was not within the acceptable range.