Clinical Treatment Planning System Research Articles

Is Constant RBE Accurate Enough for Determination of Biological Effects in Proton Therapy?

The relative biological effectiveness (RBE) of protons currently used in clinical practice is assigned a constant value of 1.1, despite the evidence that the actual RBE of protons varies as a function of proton energy, linear energy transfer (LET), dose per treatment fraction, tissue type, and clinical endpoint. The purpose of this study is to compare the performance of the traditional, constant RBE model, and two variable RBE models in explaining the radiation induced brain necrosis indicated on follow-up imaging studies for a patient treated with postoperative proton radiation therapy for malignant meningioma. In this study, treatment plan information is extracted from a clinical treatment planning system (TPS) and used to recalculate physical proton dose and linear energy transfer (LET) distributions using Monte Carlo simulations. One constant and two variable RBE models which account for tissue specific parameters, LET, biological endpoint, and dose per treatment fraction are applied on a voxel-by-voxel basis to compute three biologically effective dose distributions. These dose distributions are subsequently correlated to follow-up imaging studies which have indicated the presence of radiation induced necrosis in the treated region of the patient being analyzed. Corresponding areas of toxicity and dose/RBE computed with three different models are compared to determine which model performs the best in explaining the radiation induced toxicity. Areas of increased biologically effective dose computed using variable RBE models compared to the original dose distribution calculated by the clinical TPS using a constant RBE model are found to correspond to areas of radiation induced brain necrosis. Variable RBE doses were up to 8% higher in the region of toxicity. Variable RBE models perform better in explaining the radiation induced brain necrosis.

Feasibility of Generating Synthetic ED and CT Images From Conventional MR Images for Brain Radiation Treatment Planning and Delivery

Magnetic resonance imaging (MRI) provides soft tissue and functional contrast that may improve target delineation in radiation treatment planning. However, in order to establish MRI as the primary imaging modality in radiation therapy, limitations of lack of extractable electron density (ED) information and incompatibility with existing CT-based image guided radiation therapy (IGRT) technologies must be overcome. The purpose of this study was to determine the feasibility of generating synthetic ED and synthetic CT images directly from conventional MRI images for use in brain radiation treatment planning and delivery. A retrospective study was performed using conventional MRI images acquired on three brain tumor patients. Pre-contrast T1 and T2 images were segmented into air and skin, scalp, skull, brain, cerebrospinal fluid, and eye meshes using FSL and AFNI software. Relative ED values, obtained from ICRU Report 46, were applied to each segmented structure. The segmented structures were then recombined in to form synthetic ED images. Synthetic CT images were generated from the synthetic ED images by mapping ED to Hounsfield Units using the standard CT-ED conversion table from a clinical radiation treatment planning system. The synthetic CT images were then transferred to a radiation treatment planning system and sample whole brain radiation plans were generated without and with heterogeneity correction. Finally, the synthetic CT images were transferred to a clinical IGRT workstation. In general, the combined use of multiple MR images improved segmentation robustness. However, skull segmentation proved to be difficult in inferior cranial regions. Differences in dose distributions in plans generated without and with heterogeneity correction were apparent, indicating that the planning system was able to successfully convert the synthetic CT images into electron density. Digitally reconstructed radiographs (DRRs) were successfully generated and the synthetic CT images were able to be successfully loaded as “planning CT” images for IGRT registration during radiation treatment. Generation of synthetic ED and CT images directly from conventional MRI images for brain radiation treatment planning and delivery is feasible. However, regions of complex anatomy and/or poor image contrast may hinder segmentation and thus, dose distribution, accuracy. Future work will examine MRI relaxometry-based segmentation methods, which may improve robustness in the presence of image non-uniformities, and compare radiation treatment plans generated using synthetic CT images to those generated using conventional CT images.

Proton therapy dose distribution comparison between Monte Carlo and a treatment planning system for pediatric patients with ependymomaa)

Compare dose distributions for pediatric patients with ependymoma calculated using a Monte Carlo (MC) system and a clinical treatment planning system (TPS). Plans from ten pediatric patients with ependymoma treated using double scatter proton therapy were exported from the TPS and calculated in our MC system. A field by field comparison of the distal edge (80% and 20%), distal fall off (80% to 20%), field width (50% to 50%), and penumbra (80% to 20%) were examined. In addition, the target dose for the full plan was compared. For the 32 fields from the 10 patients, the average differences of distal edge at 80% and 20% on central axis between MC and TPS are -1.9 ± 1.7 mm (p < 0.001) and -0.6 ± 2.3 mm (p = 0.13), respectively. Excluding the fields that ranged out in bone or an air cavity, the 80% difference was -0.9 ± 1.7 mm (p = 0.09). The negative value indicates that MC was on average shallower than TPS. The average difference of the 63 field widths of the 10 patients is -0.7 ± 1.0 mm (p < 0.001), negative indicating on average the MC had a smaller field width. On average, the difference in the penumbra was 2.3 ± 2.1 mm (p < 0.001). The average of the mean clinical target volume dose differences is -1.8% (p = 0.001), negative indicating a lower dose for MC. Overall, the MC system and TPS gave similar results for field width, the 20% distal edge, and the target coverage. For the 80% distal edge and lateral penumbra, there was slight disagreement; however, the difference was less than 2 mm and occurred primarily in highly heterogeneous areas. These differences highlight that the TPS dose calculation cannot be automatically regarded as correct.

First experimental-based characterization of oxygen ion beam depth dose distributions at the Heidelberg Ion-Beam Therapy Center

Over the last decades, the application of proton and heavy-ion beams to external beam radiotherapy has rapidly increased. Due to the favourable lateral and depth dose profile, the superposition of narrow ion pencil beams may enable a highly conformal dose delivery to the tumour, with better sparing of the surrounding healthy tissue in comparison to conventional radiation therapy with photons. To fully exploit the promised clinical advantages of ion beams, an accurate planning of the patient treatments is required. The clinical treatment planning system (TPS) at the Heidelberg Ion-Beam Therapy Center (HIT) is based on a fast performing analytical algorithm for dose calculation, relying, among others, on laterally integrated depth dose distributions (DDDs) simulated with the FLUKA Monte Carlo (MC) code. Important input parameters of these simulations need to be derived from a comparison of the simulated DDDs with measurements. In this work, the first measurements of 16O ion DDDs at HIT are presented with a focus on the determined Bragg peak positions and the understanding of factors influencing the shape of the distributions. The measurements are compared to different simulation approaches aiming to reproduce the acquired data at best. A simplified geometrical model is first used to optimize important input parameters, not known a priori, in the simulations. This method is then compared to a more realistic, but also more time-consuming simulation approach better accounting for the experimental set-up and the measuring process. The results of this work contributed to a pre-clinical oxygen ion beam database, which is currently used by a research TPS for corresponding radio-biological cell experiments. A future extension to a clinical database used by the clinical TPS at HIT is foreseen. As a side effect, the performed investigations showed that the typical water equivalent calibration approach of experimental data acquired with water column systems leads to slight deviations between the experimentally determined and the real Bragg peak positions. For improved accuracy, the energy dependence of the stopping power, and herewith the water equivalent thickness, of the material downstream of the water tank should be considered in the analysis of measured data.

TH‐C‐213AB‐05: Correlating Areas of Radiation Toxicity with Relative Biological Effectiveness‐Weighted Dose Distributions in Proton Radiotherapy

Purpose: To determine if biologically effective proton dose calculated using a relative biological effectiveness (RBE) model with multiple parameters may be used to explain areas of radiation induced brain necrosis indicated on follow‐up imaging studies for a patient treated with postoperative intensity modulated proton radiotherapy for malignant meningioma. Methods: In this study, treatment plan information is extracted from a clinical treatment planning system (TPS) and used to recalculate physical proton dose and linear energy transfer (LET) distributions using Monte Carlo simulations. An RBE model which accounts for tissue specific parameters, LET, biological endpoint, and dose per treatment fraction is applied on a voxel‐by‐voxel basis to compute a biological effective dose distribution. This dose distribution is subsequently compared to follow‐up imaging studies which have indicated the presence of radiation induced necrosis in the treated region of the patient being analyzed. Corresponding areas of toxicity and dose/RBE are compared to determine the governing relationship. Results: Areas of increased biological effective dose compared to the original dose distribution calculated by the clinical TPS are found to correspond to areas of radiation induced necrosis identified on follow‐up imaging. Biologically effective dose is found to be increased over TPS dose by as much as 8% of the prescribed dose for the patient. Conclusions: Dose distributions which account for the variable nature of proton RBE may provide insight into toxicity risk for patients treated with proton radiotherapy.

SU-E-T-495: Monte Carlo Dose Verification of Passive Scattering Proton Therapy for Prostate Cancer.

To verify the clinical pencil beam dose calculation algorithm for passive scattering proton therapy using field with large range in tissue, i.e. in prostate cancer, using a Monte Carlo (MC) simulation system. Previously treated prostate cancer cases were randomly selected from our patient database. All patients received the same dose prescription of 50Gy (25 fractions) to planning treatment volume including the seminal vesicles (PTV1), followed by 28Gy (14 fractions) boost to the prostate gland only (PTV2). Patient and beam geometry were imported to our MC simulation platform (TOPAS - TOol for PArticle Simulation) and the dose of each individual beam, as well as their weighted sum, were calculated and compared to pencil beam algorithm-based calculation from the clinical treatment planning system (XiO). Preliminary results from four patient cases show overall good agreement between the pencil beam and MC calculations. However, a small but systematic overestimation of the dose, as calculated by the pencil beam calculation algorithm, was noticed for the target structures (<2% difference in D95 for PTV1 and PTV2), compared to the MC calculation. The inverse was observed for the OARs (rectum and bladder) for which the dose seems to be somewhat underestimated by the pencil beam calculation algorithm (up to 3.75% difference in the volume covered by the 70Gy and 75Gy isodose lines). Furthermore, systematic difference in the range calculation was noticed: the pencil beam calculation algorithm results in larger proton range, in the order of 3-4mm, compared to the MC calculations for all beams and patients studied. This can be attributed to the bone anatomy in the path of the beams (femoral heads). Routine MC dose calculation has the potential to improve delivery accuracy in proton therapy of prostate cancer and influence the analysis of currently ongoing clinical trials of protons versus IMRT. Funded by NIH/NCI R01 CA140735.

SU‐E‐T‐484: Potential Clinical Impact of Dosimetric Uncertainties in Proton Therapy

The analytical algorithms which are used widely for proton treatment planning in the current clinical practice may have significant dosimetric uncertainties in highly heterogeneous regions. The aim of this study is the assessment of the potential clinical impact of these uncertainties. A cohort of 8 patients with local (in field) recurrences, originally treated for lung cancer at our institution, was selected for this study. CT scans and treatment plans were used to assemble the input files for the Monte Carlo (MC) code MCNPX. The total energy deposition inside the patient volume was computed with statistical uncertainty of less than 2% and converted to dose-in-water. The results were compared to the dose computed by the clinical treatment planning system (TPS). The area of the recurrence was contoured on the follow-up PET/CT study for each of the patients and registered to the planning CT. While there is acceptable agreement between the TPS and the Monte Carlo dose in homogeneous regions, there are noticeable differences caused by heterogeneities. The regions of largest differences are the points around and beyond the distal edge and points around the lateral penumbra and in several patients the target has received lower then the prescribed dose. The area of the recurrence corelates well with the MC predicted underdosed area of the target. The uncertanties in analytical dose algorithms used in clinical TPSs may result in suboptimal target coverage increasing the possibility of tumor recurrence. Accurate Monte Carlo simulations can be used to predict and avoid the situations in which the analytical algorithms have high uncertanties providing for more robust treatment planning.

SU-E-T-52: Independent Dose Verification System for Spot Scanning Proton Therapy

Purpose: Similar to IMRT, patient specific quality assurance (QA) is an essential part of intensity modulated proton therapy (IMPT). Patient‐specific QA based on measurements only is a time‐consuming process. In this work, we developed and validated an independent dose verification system which will be used as a part of the patient specific QA process to improve the QA efficiency for IMPT patients. Methods: A recently developed pencil beam algorithm base on measured in‐water single‐spot profiles was implemented. The algorithm added a modified Cauchy‐Lorentz function to accurately model individual scanning spot. For the purpose of independent dose calculation, it is sufficiently different from the in‐air fluence‐based algorithm of our clinical treatment planning system (TPS). QA tasks can be seamlessly communicated between TPS and the verification system through DICOM network streams. Verification results were generated without human intervention and analyzed using data extracting and analyzing tools. Patient specific measured data of ten patients were selected to validate the system, among which five were treated with single‐field optimization (SFO), and five with multiple‐field optimization (MFO). The measured and calculated dose distributions were compared at points, and 2D planes at various depths. Results: AT least 98% of dose pixels passed 2% relative dose/2 mm distance to agreement (DTA) gamma criteria for all 2D dose planes of SFO cases. At least 95% of dose pixels passed the same gamma criteria for all MFO cases. Calculated values are within 3% or 2 mm DTA of measurements for 97% of validated point doses. Conclusions: We havedeveloped and validated an independent dose verification system for IMPT. The proposed system may reduce the amount of measurement and beam time required for patient specific QA processes, while maintaining a reasonable confidence to deliver a treatment safely and effectively.

SU-E-T-41: 2D Vs 3D Gamma Analysis: Establishment of Comparable Clinical Action Limits

Purpose: As clinics begin to use 3D quality assurance (QA) metrics, an investigation into appropriate action limits is required. This can be accomplished by comparing results of the 3D metric to the existing 2D metric. To investigate this, we compared 2D and 3D gamma analysis results to determine 3D gamma action limits that would Result in comparable 2D QA outcomes. Methods: 2D and 3D gamma QA analysis results for ten clinical IMRT patients using a variety of distance‐to‐agreements (DTA), dose‐thresholds, and interpolated slice thicknesses were chosen for this study. Clinical treatment planning system (TPS)‐calculated dose (evaluated) distribution and Monte Carlo‐calculated (reference) dose distribution were compared using DoseLab (Mobius Medical Systems) gamma analysis software that calculates 2‐ and 3‐dimensional gamma index for a given DTA. Each comparison evaluated the 2‐ and 3‐D average gamma and percent of pixels passing for the following acceptance criteria: ±5%/5mm, 3%/3mm, 2%/2mm, and 1%/1mm using 1 mm slice thicknesses (no dose threshold used). The effect of low‐dose thresholds and slice thickness was also evaluated. Results: The percent of pixels passing for each DTA was better for 3D as compared to the 2D analysis (2D vs 3D): 99.8% vs. 99.9% for 5%/5mm, 99.0% vs. 99.7% for 3%/3mm, 96.4% vs. 98.4% for 2%/2mm, and 79.3% vs. 83.4% for 1%/1mm. Using the low‐dose threshold resulted in worse gamma for both 2D and 3D gamma. However, similar to the no‐threshold results, use of dose threshold resulted in worse agreement for 2D than 3D for all DTA (4.1% decrease compared to 1.7% at 15% low‐dose threshold). No appreciable differences in gamma were observed as a function of slice thickness. Conclusions: Clinical use of 3D gamma analysis requires use of action limits (% of pixel passing and γ values) that are more stringent than 2D for comparable QA results.

Variation in external beam treatment plan quality: An inter-institutional study of planners and planning systems

PurposeThis study quantifies variation in radiation treatment plan quality for plans generated by a population of treatment planners given very specific plan objectives. Methods and MaterialsA “Plan Quality Metric” (PQM) with 14 submetrics, each with a unique value function, was defined for a prostate treatment plan, serving as specific goals of a hypothetical “virtual physician.” The exact PQM logic was distributed to a population of treatment planners (to remove ambiguity of plan goals or plan assessment methodology) as was a predefined computed tomographic image set and anatomic structure set (to remove anatomy delineation as a variable). Treatment planners used their clinical treatment planning system (TPS) to generate their best plan based on the specified goals and submitted their results for analysis. ResultsOne hundred forty datasets were received and 125 plans accepted and analyzed. There was wide variability in treatment plan quality (defined as the ability of the planners and plans to meet the specified goals) quantified by the PQM. Despite the variability, the resulting PQM distributions showed no statistically significant difference between TPS employed, modality (intensity modulated radiation therapy versus arc), or education and certification status of the planner. The PQM results showed negligible correlation to number of beam angles, total monitor units, years of experience of the planner, or planner confidence. ConclusionsThe ability of the treatment planners to meet the specified plan objectives (as quantified by the PQM) exhibited no statistical dependence on technologic parameters (TPS, modality, plan complexity), nor was the plan quality statistically different based on planner demographics (years of experience, confidence, certification, and education). Therefore, the wide variation in plan quality could be attributed to a general “planner skill” category that would lend itself to processes of continual improvement where best practices could be derived and disseminated to improve the mean quality and minimize the variation in any population of treatment planners.

SU‐E‐I‐32: Experimental Evaluation of a Reconstruction Algorithm for Metal Artifact Reduction

Purpose: To evaluate a metal artifact reduction algorithm (MAR) developed by Philips Medical Systems Inc. for accurately reconstructing the density of a material obscured by metallic streaking artifacts due to a prosthetic hip implant. Methods: Two inserts with known densities of 1.84 and 1.15g/cm3 were placed in a water tank at distances of 0 and 20cm from a titanium hip prosthesis. CT images were acquired with the inserts at both positions, and each set of images was reconstructed twice: once with and once without MAR. The physical densities of the inserts in these CT images were determined by a clinical treatment planning system (Pinnacle 8.0) and were recorded and compared to the known values. Results: With MAR reconstruction, for inserts proximal to the titanium prosthesis, the measured physics densities were closer to the known values. The 1.84 g/cm3 insert had a mean density of 1.444 g/cm3 without MAR and 1.546g/cm3 with MAR, an improvement of 5.5%. The standard deviation of the measured density also decreased from 0.298 to 0.213. The 1.15 g/cm3 insert had a mean density of 0.744g/cm3 without MAR and 0.890g/cm3 with MAR, an improvement of 12.7%. Standard deviation decreased from 0.33 to 0.178. for inserts distal to the prosthesis, the measured densities were minimally changed by the MAR reconstruction, as the artifact did not affect the inserts. For the 1.84g/cm3 insert: 1.801 without MAR, 1.816 with MAR. for the 1.15 g/cm3 insert: 1.138 without MAR, 1.138 with MAR. Conclusions: Our experiments show that the MAR algorithm improves accuracy of reconstructed densities proximal to hip prosthesis, although up to 22% error in the physical densities may still be present depending on the specific materials. For tissues distal to the prosthesis, the MAR reconstruction algorithm does not adversely affect the reconstructed physical densities by the treatment planning system.

SU‐E‐T‐664: Accuracy of Patient Dose Calculation in Proton Therapy of Lung Cancer: A Comparison of Monte Carlo and Pencil Beam Computations

Purpose: The analytical, semi‐empirical algorithms are used widely for proton treatment planning in the current clinical practice. The aim of this study is the assessment of possible dosimetric uncertainties in these algorithms in highly heterogeneous areas of the thorax. Methods: In this study we use a detailed Monte Carlo model of the passive scattering proton therapy (PSPT) beam line together with a computed tomography (CT) based model of the patient anatomy to investigate the heterogeneity‐induced dosimetric uncertainties in PSPT treatment planning. A CT scan and a treatment plan were used to assemble the input files for the Monte Carlo code MCNPX. The total energy deposition inside the patient volume was computed with statistical uncertainty of less than 2% and converted to dose‐in‐water. The results were compared to the dose computed by the clinical treatment planning system (TPS). The corresponding dose in the water phantom was computed using both the TPS and the Monte Carlo model and used to calibrate the absolute dosimetry in the Monte Carlo model. Results: While there is acceptable agreement between the TPS and the Monte Carlo dose in the water phantom, there are noticeable differences caused by heterogeneities. The regions of largest differences are the points around and beyond the distal edge and points around the lateral penumbra. These are most pronounced when the beam passes through or is stopped in the low density regions of the lung, and increase with the range of the beam and the amount of the heterogeneities in the beam path. Conclusions: In some lung cases the actual target coverage and the accuracy of dose to lung tissues around and beyond the distal edge of the proton beam, calculated with conventional algorithms, may be suboptimal.

WE‐E‐220‐03: SonoKnife: Development, Testing and Treatment Planning

SonoKnife is a line‐focused ultrasound device concept for non‐invasive thermal therapy, which was motivated by the poor outcomes of advanced head and neck cancer recurrences. We hypothesized that by incorporating thermal therapy in the treatment regimen of this high risk patient population, the need for further surgery may be reduced or circumvented while enhancing the therapeutic benefits of additional conventional therapies (i.e., radiotherapy +/− chemotherapy).The simplest SonoKnife source is a cylindrical section ultrasonic transducer, which creates a “line” focus, the length of the line being comparable to the altitude of the cylindrical section. In addition to lowering the chances of producing nonlinear and cavitation effects due to lower peak acoustic intensities (in comparison to an equivalent area point‐focus radiator), we hypothesized that a line‐focused device could heat/ablate a given target volume faster, thus reducing treatment times. Conformality to the target volume could be achieved by varying the length of the line‐focus using a linear array of cylindrical sections.In order to test feasibility and, in the long run, our hypotheses, we investigated thermal ablation (52–65°C) by SonoKnives with numerical models and experiments in phantoms, ex‐vivo porcine liver and in‐vivo in piglets. Line‐foci were generated by cylindrical‐section, single‐element, ultrasound transducers whether numerically or experimentally. Numerically, simulations were performed to characterize the acoustic edge, defined as the focal volume within the 50% isobar, for basic design parameters: transducer dimensions, line‐focus depth, frequency, and coupling. Experimentally, thermal ablations in gel‐phantoms and ex‐vivo samples produced lesions similar to those simulated; however, ablations in live piglets were characterized by cutaneous burns even after ice‐cooling.Essential for the clinical deployment of the SonoKnife are precise image‐guidance and treatment planning. We have addressed the latter by integrating acoustic commercial software with treatment planning platforms used for radiotherapy treatment planning. Our initial efforts for both acoustic and thermal modeling in image‐based anatomical space will be presented.In summary, the modeling, in‐phantom and ex‐vivo results support the feasibility of thermal ablation with a SonoKnife. However, unless human skin is acoustically much less absorbing than the pigletˈs, further design modifications and studies are needed. Moreover, for a more comprehensive numerical characterization, tissue heterogeneities and nonlinear effects must be taken into account; and realistic thermal simulation studies need to be performed including scanning of the acoustic edge. Finally, the deployment of a clinical‐grade system must incorporate advanced treatment planning and precise real‐time image‐guidance, and much work remains to be done before these are realities.Learning Objectives:1. Learn about the SonoKnife concept and its potential advantages relative to spherically focused HIFU transducers.2. Appreciate how the basic design parameters affect the acoustic edge and field parameters.3. Compare numerical, in‐aqua, ex‐vivo and in‐vivo measurements and/or thermal ablation results.4. Understand the main components and challenges for a clinical sonothermal treatment planning system.

TH‐E‐BRB‐03: Potential for a Monte Carlo Based Treatment Planning System to Replace Patient Specific QA Measurements in a Large University Hospital Setting

Purpose: To asses the feasibility and benefits of replacing patient specific QA measurements and back‐up MU calculations with Monte Carlo based dose calculations for improved plan verification in radiation therapy. Methods: An in‐house Monte Carlo based planning system (MMCTP) has been clinically implemented at McGill University. Over 50 patient plans have been recalculated on the system by both dosimetrists and physicists. Initial plans chosen for recalculation were head and neck IMRT and lung SBRT plans, where simple monitor unit calculators and measurements in water equivalent phantoms are often insufficient to show agreement of the planned and delivered doses within 5%. Results: MMCTP was found to be easy to use by both dosimetrists and physicists. Eighteen head and neck IMRT plans were recalculated by a dosimetrist and over 35 lung SBRT plans have been calculated by the physics staff. As no effort was spent in implementing fast MC engines, the calculation time is long but all mechanical operations, i.e., the transfer of the plans from the clinical treatment planning system to the MMCTP system and starting the calculation, take only a few minutes. Differences between MMCTP and the clinical TPS (Varian Eclipse, AAA) were small in terms of dose delivered to the PTV for the head and neck IMRT group but differences of greater the 5% were seen among the lung SBRT group. For both groups, the largest differences were seen for anatomy close to the skin and near air cavities. Conclusions: Verifying the delivery accuracy of complex treatment plans can be challenging. Current techniques requiring recalculation and measurement of plans in uniform phantoms are cumbersome and do not take into account the patient specific heterogeneity effects. The MMCTP treatment planning system requires minimal physics resources and likely introduces a more comprehensive method of evaluating calculation accuracy throughout the treatment volume.

Conversion of helical tomotherapy plans to step-and-shoot IMRT plans-Pareto front evaluation of plans from a new treatment planning system

The resulting plans from a new type of treatment planning system called SharePlan have been studied. This software allows for the conversion of treatment plans generated in a TomoTherapy system for helical delivery, into plans deliverable on C-arm linear accelerators (linacs), which is of particular interest for clinics with a single TomoTherapy unit. The purpose of this work was to evaluate and compare the plans generated in the SharePlan system with the original TomoTherapy plans and with plans produced in our clinical treatment planning system for intensity-modulated radiation therapy (IMRT) on C-arm linacs. In addition, we have analyzed how the agreement between SharePlan and TomoTherapy plans depends on the number of beams and the total number of segments used in the optimization. Optimized plans were generated for three prostate and three head-and-neck (H&N) cases in the TomoTherapy system, and in our clinical treatment planning systems (TPS) used for IMRT planning with step-and-shoot delivery. The TomoTherapy plans were converted into step-and-shoot IMRT plans in SharePlan. For each case, a large number of Pareto optimal plans were created to compare plans generated in SharePlan with plans generated in the Tomotherapy system and in the clinical TPS. In addition, plans were generated in SharePlan for the three head-and-neck cases to evaluate how the plan quality varied with the number of beams used. Plans were also generated with different number of beams and segments for other patient cases. This allowed for an evaluation of how to minimize the number of required segments in the converted IMRT plans without compromising the agreement between them and the original TomoTherapy plans. The plans made in SharePlan were as good as or better than plans from our clinical system, but they were not as good as the original TomoTherapy plans. This was true for both the head-and-neck and the prostate cases, although the differences between the plans for the latter were small. The evaluation of the head-and-neck cases also showed that the plans generated in SharePlan were improved when more beams were used. The SharePlan Pareto front came close to the front for the TomoTherapy system when a sufficient number of beams were added. The results for plans generated with varied number of beams and segments demonstrated that the number of segments could be minimized with maintained agreement between SharePlan and TomoTherapy plans when 10-19 beams were used. This study showed (using Pareto front evaluation) that the plans generated in Share-Plan are comparable to plans generated in other TPSs. The evaluation also showed that the plans generated in SharePlan could be improved with the use of more beams. To minimize the number of segments needed in a plan with maintained agreement between the converted IMRT plans and the original TomoTherapy plans, 10-19 beams should be used, depending on target complexity. SharePlan has proved to be useful and should thereby be a time-saving complement as a backup system for clinics with a single TomoTherapy system installed alongside conventional C-arm linacs.

A Dosimetric Uncertainty Analysis for Photon-Emitting Brachytherapy Sources: Report of AAPM Task Group No. 138 and GEC-ESTRO

This report addresses uncertainties pertaining to brachytherapy single-source dosimetry preceding clinical use. The International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement (GUM) and the National Institute of Standards and Technology (NIST) Technical Note 1297 are taken as reference standards for uncertainty formalism. Uncertainties in using detectors to measure or utilizing Monte Carlo methods to estimate brachytherapy dose distributions are provided with discussion of the components intrinsic to the overall dosimetric assessment. Uncertainties provided are based on published observations and cited when available. The uncertainty propagation from the primary calibration standard through transfer to the clinic for air-kerma strength is covered first. Uncertainties in each of the brachytherapy dosimetry parameters of the TG-43 formalism are then explored, ending with transfer to the clinic and recommended approaches. Dosimetric uncertainties during treatment delivery are considered briefly but are not included in the detailed analysis. For low- and high-energy brachytherapy sources of low dose rate and high dose rate, a combined dosimetric uncertainty <5% (k=1) is estimated, which is consistent with prior literature estimates. Recommendations are provided for clinical medical physicists, dosimetry investigators, and source and treatment planning system manufacturers. These recommendations include the use of the GUM and NIST reports, a requirement of constancy of manufacturer source design, dosimetry investigator guidelines, provision of the lowest uncertainty for patient treatment dosimetry, and the establishment of an action level based on dosimetric uncertainty. These recommendations reflect the guidance of the American Association of Physicists in Medicine (AAPM) and the Groupe Europeen de Curietherapie–European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) for their members and may also be used as guidance to manufacturers and regulatory agencies in developing good manufacturing practices for sources used in routine clinical treatments.

A dosimetric uncertainty analysis for photon-emitting brachytherapy sources: report of AAPM Task Group No. 138 and GEC-ESTRO.

This report addresses uncertainties pertaining to brachytherapy single-source dosimetry preceding clinical use. The International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement (GUM) and the National Institute of Standards and Technology (NIST) Technical Note 1297 are taken as reference standards for uncertainty formalism. Uncertainties in using detectors to measure or utilizing Monte Carlo methods to estimate brachytherapy dose distributions are provided with discussion of the components intrinsic to the overall dosimetric assessment. Uncertainties provided are based on published observations and cited when available. The uncertainty propagation from the primary calibration standard through transfer to the clinic for air-kerma strength is covered first. Uncertainties in each of the brachytherapy dosimetry parameters of the TG-43 formalism are then explored, ending with transfer to the clinic and recommended approaches. Dosimetric uncertainties during treatment delivery are considered briefly but are not included in the detailed analysis. For low- and high-energy brachytherapy sources of low dose rate and high dose rate, a combined dosimetric uncertainty <5% (k=1) is estimated, which is consistent with prior literature estimates. Recommendations are provided for clinical medical physicists, dosimetry investigators, and source and treatment planning system manufacturers. These recommendations include the use of the GUM and NIST reports, a requirement of constancy of manufacturer source design, dosimetry investigator guidelines, provision of the lowest uncertainty for patient treatment dosimetry, and the establishment of an action level based on dosimetric uncertainty. These recommendations reflect the guidance of the American Association of Physicists in Medicine (AAPM) and the Groupe Européen de Curiethérapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) for their members and may also be used as guidance to manufacturers and regulatory agencies in developing good manufacturing practices for sources used in routine clinical treatments.

Automated Planning of Tangential Breast Intensity-Modulated Radiotherapy Using Heuristic Optimization

To present an automated technique for two-field tangential breast intensity-modulated radiotherapy (IMRT) treatment planning. A total of 158 planned patients with Stage 0, I, and II breast cancer treated using whole-breast IMRT were retrospectively replanned using automated treatment planning tools. The tools developed are integrated into the existing clinical treatment planning system (Pinnacle(3)) and are designed to perform the manual volume delineation, beam placement, and IMRT treatment planning steps carried out by the treatment planning radiation therapist. The automated algorithm, using only the radio-opaque markers placed at CT simulation as inputs, optimizes the tangential beam parameters to geometrically minimize the amount of lung and heart treated while covering the whole-breast volume. The IMRT parameters are optimized according to the automatically delineated whole-breast volume. The mean time to generate a complete treatment plan was 6 min, 50 s ± 1 min 12 s. For the automated plans, 157 of 158 plans (99%) were deemed clinically acceptable, and 138 of 158 plans (87%) were deemed clinically improved or equal to the corresponding clinical plan when reviewed in a randomized, double-blinded study by one experienced breast radiation oncologist. In addition, overall the automated plans were dosimetrically equivalent to the clinical plans when scored for target coverage and lung and heart doses. We have developed robust and efficient automated tools for fully inversed planned tangential breast IMRT planning that can be readily integrated into clinical practice. The tools produce clinically acceptable plans using only the common anatomic landmarks from the CT simulation process as an input. We anticipate the tools will improve patient access to high-quality IMRT treatment by simplifying the planning process and will reduce the effort and cost of incorporating more advanced planning into clinical practice.

Comparison of dose calculation methods for brachytherapy of intraocular tumors

To investigate dosimetric differences among several clinical treatment planning systems (TPS) and Monte Carlo (MC) codes for brachytherapy of intraocular tumors using 125I or 103Pd plaques, and to evaluate the impact on the prescription dose of the adoption of MC codes and certain versions of a TPS (Plaque Simulator with optional modules). Three clinical brachytherapy TPS capable of intraocular brachytherapy treatment planning and two MC codes were compared. The TPS investigated were Pinnacle v8.0dp1, BrachyVision v8.1, and Plaque Simulator v5.3.9, all of which use the AAPM TG-43 formalism in water. The Plaque Simulator software can also handle some correction factors from MC simulations. The MC codes used are MCNP5 v1.40 and BrachyDose/EGSnrc. Using these TPS and MC codes, three types of calculations were performed: homogeneous medium with point sources (for the TPS only, using the 1D TG-43 dose calculation formalism); homogeneous medium with line sources (TPS with 2D TG-43 dose calculation formalism and MC codes); and plaque heterogeneity-corrected line sources (Plaque Simulator with modified 2D TG-43 dose calculation formalism and MC codes). Comparisons were made of doses calculated at points-of-interest on the plaque central-axis and at off-axis points of clinical interest within a standardized model of the right eye. For the homogeneous water medium case, agreement was within approximately 2% for the point- and line-source models when comparing between TPS and between TPS and MC codes, respectively. For the heterogeneous medium case, dose differences (as calculated using the MC codes and Plaque Simulator) differ by up to 37% on the central-axis in comparison to the homogeneous water calculations. A prescription dose of 85 Gy at 5 mm depth based on calculations in a homogeneous medium delivers 76 Gy and 67 Gy for specific 125I and 103Pd sources, respectively, when accounting for COMS-plaque heterogeneities. For off-axis points-of-interest, dose differences approached factors of 7 and 12 at some positions for 125I and 103Pd, respectively. There was good agreement (approximately 3%) among MC codes and Plaque Simulator results when appropriate parameters calculated using MC codes were input into Plaque Simulator. Plaque Simulator and MC users are perhaps at risk of overdosing patients up to 20% if heterogeneity corrections are used and the prescribed dose is not modified appropriately. Agreement within 2% was observed among conventional brachytherapy TPS and MC codes for intraocular brachytherapy dose calculations in a homogeneous water environment. In general, the magnitude of dose errors incurred by ignoring the effect of the plaque backing and Silastic insert (i.e., by using the TG-43 approach) increased with distance from the plaque's central-axis. Considering the presence of material heterogeneities in a typical eye plaque, the best method in this study for dose calculations is a verified MC simulation.

Optimum Margin for Target Expansion in Head and Neck IMRT with Daily IGRT

Materials/Methods: A total of 158 planned patients with Stage 0, I, and II breast cancer treated using whole-breast IMRT were retrospectively re-planned using automated treatment planning tools. The tools developed are integrated into the existing clinical treatment planning system (Pinnacle, Philips Radiation Oncology System) and are designed to perform the manual volume delineation, beam placement and IMRT treatment planning steps carried out by the treatment planning radiation therapist. The automated algorithm, using only the radioopaque markers placed at CT simulation as inputs (i) delineate the whole breast volume and normal tissue structures such as the lungs and heart using model-based segmentation methods, (ii) optimize the tangential beam parameters to geometrically minimize the amount of lung and heart treated while covering the whole breast volume and (iii) optimize IMRT parameters based on the automatically delineated whole breast volume. In addition, the post-surgery cavity (seroma), manually delineated by the radiation oncologist, can be incorporated into the automated planning process to ensure both geometric and dosimetric cavity coverage.