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The integration of adaptive radiation therapy (ART), or modifying the treatment plan during the treatment course, is becoming more widely available in clinical practice. ART offers strong potential for minimizing treatment-related toxicity while escalating or de-escalating target doses based on the dose to organs at risk. Yet, ART workflows add complexity into the radiation therapy planning and delivery process that may introduce additional uncertainties. This work sought to review presently available ART workflows as well as technological considerations such as image quality, deformable image registration, and dose accumulation. Quality assurance considerations for ART components and minimum recommendations are described. Personnel and workflow efficiency recommendations are provided as well as a summary of currently available clinical evidence supporting the implementation of ART. Finally, to guide future clinical trial protocols, an example ART physician directive and a physics template following standard NRG Oncology protocol is provided.

Introduction The purpose of this study was to investigate the systematic localization accuracy, treatment planning capability, and delivery accuracy of an integrated magnetic resonance imaging guided Linear Accelerator (MR-Linac) platform for stereotactic radiosurgery. Materials and methods The phantom for the end-to-end test comprises three different compartments: a rectangular MR/CT target phantom, a Winston–Lutz cube, and a rectangular MR/CT isocenter phantom. Hidden target tests were performed at gantry angles of 0, 90, 180, and 270 degrees to quantify the systematic accuracy. Five patient plans with a total of eleven lesions were used to evaluate the dosimetric accuracy. Single-isocenter IMRT treatment plans using 10–15 coplanar beams were generated to treat the multiple metastases. Results The end-to-end localization accuracy of the system was 1.0 ± 0.1 mm. The conformity index, homogeneity index and gradient index of the plans were 1.26 ± 0.22, 1.22 ± 0.10, and 5.38 ± 1.44, respectively. The average absolute point dose difference between measured and calculated dose was 1.64 ± 1.90%, and the mean percentage of points passing the 3%/1 mm gamma criteria was 96.87%. Conclusions Our experience demonstrates that excellent plan quality and delivery accuracy was achievable on the MR-Linac for treating multiple brain metastases with a single isocenter.

© 2016 The Authors. Purpose The American Society for Radiation Oncology (ASTRO) Physics Core Curriculum Subcommittee (PCCSC) has updated the recommended physics curriculum for radiation oncology resident education to improve consistency in teaching, intensity, and subject matter. Methods and Materials The ASTRO PCCSC is composed of physicists and physicians involved in radiation oncology residency education. The PCCSC updated existing sections within the curriculum, created new sections, and attempted to provide additional clinical context to the curricular material through creation of practical clinical experiences. Finally, we reviewed the American Board of Radiology (ABR) blueprint of examination topics for correlation with this curriculum. Results The new curriculum represents 56 hours of resident physics didactic education, including a 4-hour initial orientation. The committee recommends completion of this curriculum at least twice to assure both timely presentation of material and re-emphasis after clinical experience. In addition, practical clinical physics and treatment planning modules were created as a supplement to the didactic training. Major changes to the curriculum include addition of Fundamental Physics, Stereotactic Radiosurgery/Stereotactic Body Radiation Therapy, and Safety and Incidents sections, and elimination of the Radiopharmaceutical Physics and Dosimetry and Hyperthermia sections. Simulation and Treatment Verification and optional Research and Development in Radiation Oncology sections were also added. A feedback loop was established with the ABR to help assure that the physics component of the ABR radiation oncology initial certification examination remains consistent with this curriculum. Conclusions The ASTRO physics core curriculum for radiation oncology residents has been updated in an effort to identify the most important physics topics for preparing residents for careers in radiation oncology, to reflect changes in technology and practice since the publication of previous recommended curricula, and to provide practical training modules in clinical radiation oncology physics and treatment planning. The PCCSC is committed to keeping the curriculum current and consistent with the ABR examination blueprint.

Purpose: To describe and evaluate a novel algorithm for generating synthetic CT images from MR-SIM data for dose calculations in MR-only treatment planning. Methods: A voxel-based weighted summation method was implemented to generate synthetic CT (synCT) images. MR data were acquired using Philips 1.0T Panorama high-field open MR-SIM. Retrospective patient data from seven prostate patients and one brain patient (three lesions) enrolled in an IRB-approved study were used. 3D T1-weighted fast field echo and 3D T2-weighted turbo spin echo sequences were utilized for all patients. A 3D balanced turbo field echo sequence using spectral presaturation with inversion recovery was acquired for prostate patients, but 3D ultra-short echo time (UTE)-DIXON was instead acquired for the brain patient to amplify bone signal for semi-automatic bone segmentation. Weight optimization was performed using a training subset of patients. HU value differences between planning CT and synCTs were analyzed using mean absolute error (MAE). Original patient CT-based treatment plans were mapped onto synCTs, dose was recalculated using original leaf motion and MU values, and DRRs were generated. Dosevolume metrics and gamma analysis were used for dosimetric evaluation. Results: Average whole-body MAE of synCTs across all patients was 75+12 HU. In prostate cancer patients, average HU difference between planning and synCTs was 0.9±1.0% for soft tissue structures and 4.3±2.5% for bony structures. DRRs were generated from synCTs and qualitatively showed good geometric agreement with planning CT-generated DRRs. D99, mean dose, and maximum dose to CTV calculated using the synCT remained within 1.2% of planning CT-based dose calculations. All gamma analysis evaluated at 2%/2mm dose difference/distance to agreement) pass rates were greater than 95% with an average of 99.9±0.1% for prostate patients and 98.4±2.2% for three brain lesions. Conclusion: SynCTs were generated with clinically acceptable accuracy comparable to planning CTs, enabling dose computations for MR-only simulation. Research supported in part by a grant from Philips HealthCare (Best, Netherlands).

Purpose: An important question rarely discussed in the CT vs. MRI simulation debate is whether MRI reference images provide an adequate image‐set to use with daily localization such as cone‐beam CT (CBCT). This study compares clinical couch shifts based on daily CBCT images to shifts measured from MR images as the reference dataset for prostate IMRT treatment. Methods: Eight patients undergoing a pilot study had MR imaging along with CT simulation with the intent of evaluating a MR‐simulation process. Patients had T1, T2 and bTFE (balanced Turbo Field Echo) sequences. The remainder of the treatment planning process continued using traditional procedures using CT. Therapists used only the CT scan as a reference for localization. Retrospectively, an observer measured shifts between daily CBCT images and MR reference images. Results: The differences in shift positions for the cohort between therapists and the observer are −0.16cm ± 0.25cm (AP), 0.04cm ± 0.19cm (SI), and — 0.01cm ± 0.14cm (LR). The mean group error for the therapists and the observer were less than 2 mm in all directions. Based on these shifts, the calculated margins for the therapists would be 0.87cm (AP), 0.65cm (SI), and 0.71cm (LR) and for the observer would be 1.1cm (AP), 0.66cm (SI), and 0.70cm (LR). For SI and LR directions both sets of margins are very close to one another. An outlier impacted the AP margin difference by 2.3mm and should be investigated further. This initial analysis suggests that each modality can be considered clinically sufficient for daily localization. Conclusion: The results of this study suggest that MR reference image‐sets can be used for daily image‐guided localization of prostate cancers with at least the same accuracy as current methods. MR simulation provides substantial soft‐tissue contrast and can improve tissue targeting in radiation oncology, as a Result further investigation is warranted.

Purpose: To establish a centralized database platform for radiotherapyquality assurance (QA). This can be an interactive platform where physicists can download digital benchmark cases from, perform radiotherapy planning and QA measurements with various planning and delivery systems, and upload the results. Data retrieval and analysis functions can then be developed for the quality assessment of the radiation plan and measurement in the context of prior data with similar planning, delivery and measurement technologies. Data mining of the system can in turn generate quantitative guidance for clinical trial QA requirements that have the optimum combination of stringent mandates and realistic accommodations. Methods: Built upon prior experience of Task Group(TG) 119 from IMRT workgroup of AAPM, and digital data phantoms from EORTC, these groups collaborate with clinical trial work group of AAPM and advanced technology consortium (ATC) to establish a virtual QA environment within National Biomedical Imaging Archive (NBIA). Digital phantoms from TG 119 and EORTC dosimetric evaluation with DICOM image, structures, plan and dose were uploaded to the virtual QA environment for the established community to download and perform evaluation of basic dosimetric parameters including, structure volumes, geometric location of structure center, designated points on dose volume histogram (DVH). Nine different treatment planning or related systems with various versions are included in these evaluations. Results: Volume variations among versions of these nine systems are 2.7%, 4.2%, 14.7%, 4.7% for prostate PTV, prostate, rectum and bladder respectively. DVH reading differences are 0.1%, 2.4% and 1.5% for prostate D95, rectum D30 and bladder D30. DVH reading differences for cube 10 are 1%, 86% and 100% for V0, V20 and V30 respectively. Conclusions: A virtual QA environment has been established within NBIA for radiation oncology inter‐system and inter‐institution QA. Initial testing in reading geometric and dosimetric parameters identified significant variations among systems.

Purpose: Patient specific IMRT QA has been an integral part of the IMRT process in most institutions since the advent of IMRT. There has been the question whether we need to continue with this practice for every patient now that we have data for multiple similar cases. However, there have always been singular cases that fail IMRT QA criteria. This study looks to correlate treatment planning parameters and other metrics with the IMRT QA passing rate hoping to predict cases which may still require IMRT QA measurements. Methods and Materials: 15 head and neck treatment plans (101 total treatment beams) were delivered and analyzed using a 2‐dimensional diode array (MapCheck®). Each treatment beam was analyzed using a gamma analysis with evaluation criteria of 10 percent threshold, 3 percent dose deviation and 3 millimeters distance‐to‐agreement as this is the standard for our institution. Parameters used to correlate with passing rate were: monitor units delivered, dose at isocenter, delivery efficiency (MU/cGy), number of segments, average leaf pair opening, and a 2‐dimensional modulation index developed from the original Webb equation. Results: Univariate analysis showed no factors that correlated with passing rate. The R‐squared values ranged from 0.0014 to 0.0565. MU number did have a p value of 0.016, but no other indicator of plan complexity predicted passing rate. Conclusions: This work demonstrates our attempt to show a relationship between IMRT QA passing rates and treatment plan parameters. We are unable to correlate any of these metrics with patient QA pass rate as specified. Treatment delivery uncertainties (e.g. flatness and symmetry) may play as pivot a role in QA passing rate as the treatment planning parameters used. Further investigation will involve combination of these uncertainties.

Purpose: To commission CMS Monaco IMRTtreatment planning system for clinical applications using a standard test suite. Methods and Materials: A system test including IMRT planning and plan delivery was performed for CMS Monaco IMRTtreatment planning system, version 1.0.2. Four cases from TG‐119 were studied: one head and neck case, one prostate case, and two C shape cases. IMRT plans were generated with the dose goals set by TG‐119 and were compared with the plans generated using CMS XiO treatment planning system. Analysis metrics were for dose coverage, number of segments, total MU, MU efficiency, number of segments per beam, and delivery time. The IMRT plans were delivered on an Elekta Synergy linear accelerator with step‐and‐shoot technique. This linac has 4‐mm MLC.IMRT QA was performed with field by field review using a diode array device Mapcheck. Results: Monaco plans showed similar target dose coverage as XiO plans and improved organ sparing in some cases. The ratios (Monaco/XiO) of number of total segments, total MU, number of segments per beam, and MU efficiency (Dose/MU) were 0.21–0.76, 0.56–0.77, 0.21–0.50, and 1.31–1.86, respectively. The delivery time of a Monaco plan was shorter than the XiO plan having the same number of beams by approximately 33%. IMRT QA pass rates of the Monaco plans were 93.8%–97.6% for the criteria of 3‐mm, 3%, and 10% threshold, which in three cases were higher than the XiO plans (93.1%–96.6%). Conclusion: The IMRT plans from the Monaco treatment planning system showed advantages over XiO plans. These advantages include smaller number of segments, smaller MU, smaller number of segments per beam, and higher MU efficiency. Additionally, the delivery time was shorter. The QA pass rates were similar to or higher than the XiO plans.

Purpose:Treatment planning system/delivery system combinations affect IMRT optimization. Delivery method (step and shoot vs. dynamic), leakage radiation variations (lack of secondary collimators on the Elekta SL‐S MLC head), hardware constraints and leaf edge shape all factor in when a radiation treatment planning system (RTPS) optimizes MLC segments. Different optimization algorithms within treatment planning systems influence IMRT planning results as well. In our study we evaluated delivery efficiencies from Elekta, Varian and Siemens machines coupling with two different treatment planning systems. Method and Materials: 16 IMRT plans were created for prostate and headneck (HN) sites (5 and 6 co‐planar beams respectively) using CMS Xio and Philips ADAC Pinnacle. Plans were optimized using the same dose and DVH objectives and modulated using clinical thresholds. The monitor units needed to deliver the weight point dose for each field were tabulated for each site and machine source data. Results: All plans have similar quality meeting the objectives. HN plans have an average MU efficiency of 5.02 +/− 1.09 MU/cGy. The plans created on the Pinnacle RTPS had a lower mean efficiency but was not statistically significant due to the high standard deviation. Prostate plans were more efficient than the HN plans with an average value of 2.19 +/− 0.47 MU/cGy. Pinnacle prostate plans had a mean MU efficiency of 1.69 +/− 0.18 MU/cGy compared to 2.51 +/− 0.22 MU/cGy from Xio. Conclusion: There is no statistical difference in MU efficiency for HN plans from different planning/delivery system combinations. Prostate plans have higher delivery MU efficiencies than those from HN plans, most likely due to less modulation. For the prostate plans there exist slight differences in MU efficiencies with unknown clinical significance between different planning/delivery systems.

Purpose: Commissioning treatment planning systems for IMRT require more tests and verification of the beam model than those required for 3D conformal techniques. This study presents the dependence of IMRT QA pass/fail rate on the modeling of MLC leaf transmission in a MiniMLC linear accelerator. This particular modeling parameter doesn't affect the tests for 3D conformal situations per commissioning guideline. The results of both film QA and Mapcheck analysis will be presented. Method and Materials: Elekta Synergy S linac equipped with Beam Modulator collimating head was used (4mm leaves, no movable backup diaphragms) Beams were modeled in CMS XIO following the guidelines of TG53. IMRT plans were calculated for a phantom target (mock prostate) and the accuracy of the dose delivery was measured using both film and Mapcheck Gamma analysis. Then, the beam model in XIO planning system was replaced by the model with substantially lower MLC transmission factors, the plans were re‐optimized, and re‐delivered. Results: Original beam fitting (following linac commissioning) was internally verified by at least two independent measurements and checked against standard datasets (average passing rate of 97.8%). However, in our test plans, these fits showed relatively poor agreement between planned and delivered treatment as verified by both film and Mapcheck‐based dosimetry. Average passing rate using film dosimetry was 88% and using Mapcheck it was 71%. After beam re‐fitting using the decreased MLC Transmission factor, the agreement between planned and delivered dose increased to 98% and 87%, respectively. Conclusion: Commissioning of treatment planning systems for IMRT should include measurement verifications for IMRT plans of relatively complex modulation, in addition to standard tests from guidelines for 3D conformal planning.

Background This study describes initial testing and evaluation of a vertical-field open Magnetic Resonance Imaging (MRI) scanner for the purpose of simulation in radiation therapy for prostate cancer. We have evaluated the clinical workflow of using open MRI as a sole modality for simulation and planning. Relevant results related to MRI alignment (vs. CT) reference dataset with Cone-Beam CT (CBCT) for daily localization are presented. Methods Ten patients participated in an IRB approved study utilizing MRI along with CT simulation with the intent of evaluating the MRI-simulation process. Differences in prostate gland volume, seminal vesicles, and penile bulb were assessed with MRI and compared to CT. To evaluate dose calculation accuracy, bulk-density-assignments were mapped onto respective MRI datasets and treated IMRT plans were re-calculated. For image localization purposes, 400 CBCTs were re-evaluated with MRI as the reference dataset and daily shifts compared against CBCT-to-CT registration. Planning margins based on MRI/CBCT shifts were computed using the van Herk formalism. Results Significant organ contour differences were noted between MRI and CT. Prostate volumes were on average 39.7% (p = 0.002) larger on CT than MRI. No significant difference was found in seminal vesicle volumes (p = 0.454). Penile bulb volumes were 61.1% higher on CT, without statistical significance (p = 0.074). MRI-based dose calculations with assigned bulk densities produced agreement within 1% with heterogeneity corrected CT calculations. The differences in shift positions for the cohort between CBCT-to-CT registration and CBCT-to-MRI registration are −0.15 ± 0.25 cm (anterior-posterior), 0.05 ± 0.19 cm (superior-inferior), and −0.01 ± 0.14 cm (left-right). Conclusions This study confirms the potential of using an open-field MRI scanner as primary imaging modality for prostate cancer treatment planning simulation, dose calculations and daily image localization.