AAPM Report 373: The content, structure, and value of the Professional Doctorate in Medical Physics (DMP).

The Professional Doctorate in Medical Physics (DMP) was originally conceived as a solution to the shortage of medical physics residency training positions. While this shortage has now been largely satisfied through conventional residency training positions, the DMP has expanded to multiple institutions and grown into an educational pathway that provides specialized clinical training and extends well beyond the creation of additional training spots. As such, it is important to reevaluate the purpose and the value of the DMP. Additionally, it is important to outline the defining characteristics of the DMP to assure that all existing and future programs provide this anticipated value. Since the formation and subsequent accreditation of the first DMP program in 2009-2010, four additional programs have been created and accredited. However, no guidelines have yet been recommended by the American Association of Physicists in Medicine. CAMPEP accreditation of these programs has thus far been based only on the respective graduate and residency program standards. This allows the development and operation of DMP programs which contain only the requisite Master of Science (MS) coursework and a 2-year clinical training program. Since the MS plus 2-year residency pathway already exists, this form of DMP does not provide added value, and one may question why this existing pathway should be considered a doctorate. Not only do we, as a profession, need to outline the defining characteristics of the DMP, we need to carefully evaluate the potential advantages and disadvantages of this pathway within our education and training infrastructure. The aims of this report from the Working Group on the Professional Doctorate Degree for Medical Physicists (WGPDMP) are to (1) describe the current state of the DMP within the profession, (2) make recommendations on the structure and content of the DMP for existing and new DMP programs, and (3) evaluate the value of the DMP to the profession of medical physics.

A sensitivity analysis of probability maps in deep-learning-based anatomical segmentation.

PURPOSE: Deep-learning-based segmentation models implicitly learn to predict the presence of a structure based on its overall prominence in the training dataset. This phenomenon is observed and accounted for in deep-learning applications such as natural language processing but is often neglected in segmentation literature. The purpose of this work is to demonstrate the significance of class imbalance in deep-learning-based segmentation and recommend tuning of the neural network optimization objective. METHODS: An architecture and training procedure were chosen to represent common models in anatomical segmentation. A family of 5-block 2D U-Nets were independently trained to segment 10 structures from the Cancer Imaging Archive's Head-Neck-Radiomics-HN1 dataset. We identify the optimal threshold for our models according to their Dice score on the validation datasets and consider perturbations about the optimum. A measure of structure prominence in segmentation datasets is defined, and its impact on the optimal threshold is analyzed. Finally, we consider the use of a 2D Dice objective in addition to binary cross entropy. RESULTS: We observe significant decreases in perceived model performance with conventional 0.5-thresholding. Perturbations of as little as ±0.05 about the optimum threshold induce a median reduction in Dice score of 11.8% for our models. There is statistical evidence to suggest a weak correlation between training dataset prominence and optimal threshold (Pearson r = 0.92 and p ≈ 10 - 4 ). We find that network optimization with respect to the 2D Dice score itself significantly reduces variability due to thresholding but does not unequivocally create the best segmentation models when assessed with distance-based segmentation metrics. CONCLUSION: Our results suggest that those practicing deep-learning-based contouring should consider their postprocessing procedures as a potential avenue for improved performance. For intensity-based postprocessing, we recommend a mixed objective function consisting of the traditional binary cross entropy along with the 2D Dice score.

Quantifying false positional corrections due to facial motion using SGRT with open-face Masks.

PURPOSE: Studies have evaluated the viability of using open-face masks as an immobilization technique to treat intracranial and head and neck cancers. This method offers less stress to the patient with comparable accuracy to closed-face masks. Open-face masks permit implementation of surface guided radiation therapy (SGRT) to assist in positioning and motion management. Research suggests that changes in patient facial expressions may influence the SGRT system to generate false positional corrections. This study aims to quantify these errors produced by the SGRT system due to face motion. METHODS: Ten human subjects were immobilized using open-face masks. Four discrete SGRT regions of interest (ROIs) were analyzed based on anatomical features to simulate different mask openings. The largest ROI was lateral to the cheeks, superior to the eyebrows, and inferior to the mouth. The smallest ROI included only the eyes and bridge of the nose. Subjects were asked to open and close their eyes and simulate fear and annoyance and peak isocenter shifts were recorded. This was performed in both standard and SRS specific resolutions with the C-RAD Catalyst HD system. RESULTS: All four ROIs analyzed in SRS and Standard resolutions demonstrated an average deviation of 0.3 ± 0.3 mm for eyes closed and 0.4 ± 0.4 mm shift for eyes open, and 0.3 ± 0.3 mm for eyes closed and 0.8 ± 0.9 mm shift for eyes open. The average deviation observed due to changing facial expressions was 1.4 ± 0.9 mm for SRS specific and 1.6 ± 1.6 mm for standard resolution. CONCLUSION: The SGRT system can generate false positional corrections for face motion and this is amplified at lower resolutions and smaller ROIs. These errors should be considered in the overall tolerances and treatment plan when using open-face masks with SGRT and may warrant additional radiographic imaging.

A customizable aluminum compensator system for total body irradiation.

PURPOSE: To develop a simplified aluminum compensator system for total body irradiation (TBI) that is easy to assemble and modify in a short period of time for customized patient treatments. METHODS: The compensator is composed of a combination of 0.3 cm thick aluminum bars, two aluminum T-tracks, spacers, and metal bolts. The system is mounted onto a plexiglass block tray. The design consists of 11 fixed sectors spanning from the patient's head to feet. The outermost sectors utilize 7.6 cm wide aluminum bars, while the remaining sectors use 2.5 cm wide aluminum bars. There is a magnification factor of 5 from the compensator to the patient treatment plane. Each bar of aluminum is interconnected at each adjacent sector with a tongue and groove arrangement and fastened to the T-track using a metal washer, bolt, and nut. Inter-bar leakage of the compensator was tested using a water tank and diode. End-to-end measurements were performed with an ion chamber in a solid water phantom and also with a RANDO phantom using internal and external optically stimulated luminescent detectors (OSLDs). In-vivo patient measurements from the first 20 patients treated with this aluminum compensator were compared to those from 20 patients treated with our previously used lead compensator system. RESULTS: The compensator assembly time was reduced to 20-30 min compared to the 2-4 h it would take with the previous lead design. All end-to-end measurements were within 10% of that expected. The median absolute in-vivo error for the aluminum compensator was 3.7%, with 93.8% of measurements being within 10% of that expected. The median error for the lead compensator system was 5.3%, with 85.1% being within 10% of that expected. CONCLUSION: This design has become the standard compensator at our clinic. It allows for quick assembly and customization along with meeting the Task Group 29 recommendations for dose uniformity.

Deep learning-based survival analysis for brain metastasis patients with the national cancer database.

PURPOSE: Prognostic indices such as the Brain Metastasis Graded Prognostic Assessment have been used in clinical settings to aid physicians and patients in determining an appropriate treatment regimen. These indices are derivative of traditional survival analysis techniques such as Cox proportional hazards (CPH) and recursive partitioning analysis (RPA). Previous studies have shown that by evaluating CPH risk with a nonlinear deep neural network, DeepSurv, patient survival can be modeled more accurately. In this work, we apply DeepSurv to a test case: breast cancer patients with brain metastases who have received stereotactic radiosurgery. METHODS: Survival times, censorship status, and 27 covariates including age, staging information, and hormone receptor status were provided for 1673 patients by the NCDB. Monte Carlo cross-validation with 50 samples of 1400 patients was used to train and validate the DeepSurv, CPH, and RPA models independently. DeepSurv was implemented with L2 regularization, batch normalization, dropout, Nesterov momentum, and learning rate decay. RPA was implemented as a random survival forest (RSF). Concordance indices of test sets of 140 patients were used for each sample to assess the generalizable predictive capacity of each model. RESULTS: Following hyperparameter tuning, DeepSurv was trained at 32 min per sample on a 1.33 GHz quad-core CPU. Test set concordance indices of 0.7488 ± 0.0049, 0.6251 ± 0.0047, and 0.7368 ± 0.0047, were found for DeepSurv, CPH, and RSF, respectively. A Tukey HSD test demonstrates a statistically significant difference between the mean concordance indices of the three models. CONCLUSION: Our results suggest that deep learning-based survival prediction can outperform traditional models, specifically in a case where an accurate prognosis is highly clinically relevant. We recommend that where appropriate data are available, deep learning-based prognostic indicators should be used to supplement classical statistics.

An open-source tool to visualize potential cone collisions while planning SRS cases.

PURPOSE: To create an open-source visualization program that allows one to find potential cone collisions while planning intracranial stereotactic radiosurgery cases. METHODS: Measurements of physical components in the treatment room (gantry, cone, table, localization stereotactic radiation surgery frame, etc.) were incorporated into a script in MATLAB (MathWorks, Natick, MA) that produces three-dimensional visualizations of the components. A localization frame, used during simulation, fully contains the patient. This frame was used to represent a safety zone for collisions. Simple geometric objects are used to approximate the simulated components. The couch is represented as boxes, the gantry head and cone are represented by cylinders, and the patient safety zone can be represented by either a box or ellipsoid. These objects are translated and rotated based upon the beam geometry and the treatment isocenter to mimic treatment. A simple graphical user interface (GUI) was made in MATLAB (compatible with GNU Octave) to allow users to pass the treatment isocenter location, the initial and terminal gantry angles, the couch angle, and the number of angular points to visualize between the initial and terminal gantry angle. RESULTS: The GUI provides a fast and simple way to discover collisions in the treatment room before the treatment plan is completed. Twenty patient arcs were used as an end-to-end validation of the system. Seventeen of these appeared the same in the software as in the room. Three of the arcs appeared closer in the software than in the room. This is due to the treatment couch having rounded corners, whereas the software visualizes sharp corners. CONCLUSIONS: This simple GUI can be used to find the best orientation of beams for each patient. By finding collisions before a plan is being simulated in the treatment room, a user can save time due to replanning of cases.

Patient-specific dose quality assurance of single-isocenter multiple brain metastasis stereotactic radiosurgery using PTW Octavius 4D.

PURPOSE: Single-isocenter multiple brain metastasis stereotactic radiosurgery is an efficient treatment modality increasing in clinical practice. The need to provide accurate, patient-specific quality assurance (QA) for these plans is met by several options. This study reviews some of these options and explores the use of the Octavius 4D as a solution for patient-specific plan quality assurance. METHODS: The Octavius 4D Modular Phantom (O4D) with the 1000 SRS array was evaluated in this study. The array consists of 977 liquid-filled ion chambers. The center 5.5 cm × 5.5 cm area has a detector spacing of 2.5 mm. The ability of the O4D to reconstruct three-dimensional (3D) dose was validated against a 3D gel dosimeter, ion chamber, and film measurements. After validation, 15 patients with 2-11 targets had their plans delivered to the phantom. The criteria used for the gamma calculation was 3%/1 mm. The portion of targets which were measurable by the phantom was countable. The accompanying software compiled the measured doses allowing each target to be counted from the measured dose distribution. RESULTS: Spatial resolution was sufficient to verify the high dose distributions characteristic of SRS. Amongst the 15 patients there were 74 targets. Of the 74 targets, 61 (82%) of them were visible on the measured dose distribution. The average gamma passing rate was 99.3% (with sample standard deviation of 0.68%). CONCLUSIONS: The high resolution provided by the O4D with 1000 SRS board insert allows for very high-resolution measurement. This high resolution in turn can allow for high gamma passing rates. The O4D with the 1000 SRS array is an acceptable method of performing quality assurance for single-isocenter multiple brain metastasis SRS.

A dosimetric analysis of a spine SBRT specific treatment planning system.

PURPOSE: The Brainlab Elements treatment planning system utilizes distinct modules for treatment planning specific to stereotactic treatment sites including single or multiple brain lesions as well as spine. This work investigates the hypothesis that an optimization tailored specifically to spine can in fact create dosimetrically superior plans to those created in more general use treatment planning systems (TPS). METHODS: Ten spine patients at our institution were replanned in Brainlab Elements, Phillips Pinnacle3 , and Elekta Monaco. The planning target volume (PTV) included the vertebral body (in either the thoracic or lumbar spine), pedicles, and transverse processes. In all plans, the target was prescribed 20 Gy to 95% of the PTV. Objectives for the study included D5%<25 Gy and spinal cord D0.035cc < 14 Gy. Plans were evaluated by the satisfaction of the objectives as well total monitor units (MU), gradient index (GI), conformity index (CI), and dose gradient (distance between 100% and 50% isodose lines) in a selected slice between the vertebral body and spinal cord. RESULTS: All TPS produced clinically acceptable plans. The sharpest dose gradient was achieved with Elements (mean 3.3 ± 0.2 mm). This resulted in lowest spinal cord maximum point doses (6.6 ± 1.0 Gy). Gradient indices were also the smallest for Elements (3.6 ± 0.5). Further improvement in gradient index and spinal cord sparing were not performed due to the subsequent violation of the PTV D5% < 25 Gy constraint or the loss of conformity due to the loss of coverage at the PTV-spinal canal interface. CONCLUSIONS: Brainlab Elements planning which relies on arc duplication to specifically optimize for spine anatomy did result in dosimetrically superior plans while holding prescription levels constant. While any planning system can improve upon specific dosimetric objectives, the simultaneous satisfaction of all constraints was best achieved with Brainlab Elements.

A method to predict patient-specific table coordinates for quality assurance in external beam radiation therapy.

PURPOSE: While external beam radiotherapy treatment planning determines nearly every mechanical and dosimetric parameter of the linear accelerator (LINAC), the table coordinates in all three dimensions are generally unknown until initial patient setup at the LINAC. Knowing these parameters in advance could help verify the direction of patient shifts and prevent wrong-site errors. This study aims to determine the feasibility and accuracy of table coordinate prediction for indexed immobilization devices. METHODS: A total of 303 table coordinates were predicted for patients on Varian and Elekta linear accelerators with immobilization devices including Orfit mask with baseplate, wingboard, breastboard and BodyFix. Predictions were made for all three spatial dimensions except for Body Fix setups due to the lack of a radiographically apparent indexing-related landmark. Coordinates were predicted by measuring baseline table coordinates in all dimensions at specified landmark positions. RESULTS: Predictions were accurate within 2 cm for 86% of coordinates (71% within 1 cm). Table coordinates were predicted most accurately for head and neck patients with a base plate and the most difficult prediction was in the lateral direction for breastboard patients. CONCLUSIONS: With proper indexing, table coordinates can be predicted with reasonable accuracy. The data suggest an action of level of 2 cm with certain exceptions for specific immobilization devices and directions.

Comparative performance evaluation of a new a-Si EPID that exceeds quad high-definition resolution.

PURPOSE: Electronic portal imaging devices (EPIDs) are an integral part of the radiation oncology workflow for treatment setup verification. Several commercial EPID implementations are currently available, each with varying capabilities. To standardize performance evaluation, Task Group Report 58 (TG-58) and TG-142 outline specific image quality metrics to be measured. A LinaTech Image Viewing System (IVS), with the highest commercially available pixel matrix (2688x2688 pixels), was independently evaluated and compared to an Elekta iViewGT (1024x1024 pixels) and a Varian aSi-1000 (1024x768 pixels) using a PTW EPID QC Phantom. METHODS: The IVS, iViewGT, and aSi-1000 were each used to acquire 20 images of the PTW QC Phantom. The QC phantom was placed on the couch and aligned at isocenter. The images were exported and analyzed using the epidSoft image quality assurance (QA) software. The reported metrics were signal linearity, isotropy of signal linearity, signal-tonoise ratio (SNR), low contrast resolution, and high-contrast resolution. These values were compared between the three EPID solutions. RESULTS: Computed metrics demonstrated comparable results between the EPID solutions with the IVS outperforming the aSi-1000 and iViewGT in the low and high-contrast resolution analysis. CONCLUSION: The performance of three commercial EPID solutions have been quantified, evaluated, and compared using results from the PTW QC Phantom. The IVS outperformed the other panels in low and high-contrast resolution, but to fully realize the benefits of the IVS, the selection of the monitor on which to view the high-resolution images is important to prevent down sampling and visual of resolution.

A quantitative measure for radiation treatment plan quality.

PURPOSE: To develop and validate an intensity modulated radiation therapy (IMRT) treatment plan quantitative score using QUANTEC dose/volume parameters to assess plan quality. METHODS: 132 IMRT and volumetric modulated Arc therapy (VMAT) patient plans of various treatment sites were evaluated. The optimized plan's dose volume histogram (DVH) was exported to Velocity for evaluation. The proposed scoring was based on calculating the shortest distance from the QUANTEC objective to the DVH line of each organ. Each plan was normalized against the ideal plan where the organs at risk (OARs) received no dose and hence the distance between the QUANTEC objective and the DVH line was maximized. These normalized scores enabled the comparison of the quality of plans across treatment sites and dosimetrists. The scores were plotted and statistically analyzed to serve as a basis for future research. RESULTS: The score for each treatment site was evaluated and the average percentage scores±SD were found to be 43.5 ± 21.0, 33.3 ± 31.7, 42.6 ± 23.3, 40.2 ± 24.4, 33.5 ± 23.5 for the sites of abdomen, brain, chest, head/neck, and pelvis respectively. Differences in scores between the treatment sites were largely attributed to OAR segmentation and proximity of the OAR to the planning target volume (PTV). Small score differences between dosimetrists were attributed to the number of plans they have completed. CONCLUSION: This approach allows comparison of patient treatments which will help improve patient care and treatment outcomes. A larger sample of treatment plans is being evaluated to investigate the effect of dosimetrist's experience on plan quality.

Dosimetric validation of Monaco treatment planning system on an Elekta VersaHD linear accelerator.

The purpose of this study is to perform dosimetric validation of Monaco treatment planning system version 5.1. The Elekta VersaHD linear accelerator with high dose rate flattening filter-free photon modes and electron energies was used in this study. The dosimetric output of the new Agility head combined with the FFF photon modes warranted this investigation into the dosimetric accuracy prior to clinical usage. A model of the VersaHD linac was created in Monaco TPS by Elekta using commissioned beam data including percent depth dose curves, beam profiles, and output factors. A variety of 3D conformal fields were created in Monaco TPS on a combined Plastic water/Styrofoam phantom and validated against measurements with a calibrated ion chamber. Some of the parameters varied including source to surface distance, field size, wedges, gantry angle, and depth for all photon and electron energies. In addition, a series of step and shoot IMRT, VMAT test plans, and patient plans on various anatomical sites were verified against measurements on a Delta4 diode array. The agreement in point dose measurements was within 2% for all photon and electron energies in the homogeneous phantom and within 3% for photon energies in the heterogeneous phantom. The mean ± SD gamma passing rates of IMRT test fields yielded 93.8 ± 4.7% based on 2% dose difference and 2 mm distance-to-agreement criteria. Eight previously treated IMRT patient plans were replanned in Monaco TPS and five measurements on each yielded an average gamma passing rate of 95% with 6.7% confidence limit based on 3%, 3 mm gamma criteria. This investigation on dosimetric validation ensures accuracy of modeling VersaHD linac in Monaco TPS thereby improving patient safety.

Effect of electron contamination on in vivo dosimetry for lung block shielding during TBI.

Our institution performs in vivo verification measurement for each of our total body irradiation (TBI) patients with optically stimulated luminescent dosimeters (OSLD). The lung block verification measurements were commonly higher than expected. The aim of this work is to understand this discrepancy and improve the accuracy of these lung block verification measurements. Initially, the thickness of the lung block was increased to provide adequate lung sparing. Further tests revealed the increase was due to electron contamination dose emanating from the lung block. The thickness of the bolus material covering the OSLD behind the lung block was increased to offset the electron contamination. In addition, the distance from the lung block to the dosimeter was evaluated for its effect on the OSLD reading and found to be clinically insignificant over the range of variability in our clinic. The results show that the improved TBI treatment technique provides for better accuracy of measured dose in vivo and consistency of patient setup.

Commissioning an Elekta Versa HD linear accelerator.

The purpose of this study is to report the dosimetric aspects of commissioning performed on an Elekta Versa HD linear accelerator (linac) with high-dose-rate flattening filter-free (FFF) photon modes and electron modes. Acceptance and commissioning was performed on the Elekta Versa HD linac with five photon energies (6 MV, 10 MV, 18 MV, 6 MV FFF, 10 MV FFF), four electron energies (6 MeV, 9MeV, 12 MeV, 15 MeV) and 160-leaf (5 mm wide) multileaf collimators (MLCs). Mechanical and dosimetric data were measured and evaluated. The measurements include percent depth doses (PDDs), in-plane and cross-plane profiles, head scatter factor (Sc), relative photon output factors (Scp), universal wedge transmission factor, MLC transmission factors, and electron cone factors. Gantry, collimator, and couch isocentricity measurements were within 1 mm, 0.7 mm, and 0.7 mm diameter, respectively. The PDDs of 6 MV FFF and 10 MV FFF beams show deeper dmax and steeper falloff with depth than the corresponding flattened beams. While flatness values of 6 MV FFF and 10 MV FFF normalized profiles were expectedly higher than the corresponding flattened beams, the symmetry values were almost identical. The cross-plane penumbra values were higher than the in-plane penumbra values for all the energies. The MLC transmission values were 0.5%, 0.6%, and 0.6% for 6 MV, 10 MV, and 18 MV photon beams, respectively. The electron PDDs, profiles, and cone factors agree well with the literature. The outcome of radiation treatment is directly related to the accuracy in the dose modeled in the treatment planning system, which is based on the commissioned data. Commissioning data provided us a valuable insight into the dosimetric characteristics of the beam. This set of commissioning data can provide comparison data to others performing Versa HD commissioning, thereby improving patient safety.

Monte Carlo modeling of linear accelerator using distributed computing.

PURPOSE: The distributed computing implementation of the EGSnrc Monte Carlo system using a computer cluster is investigated and tested. METHODS: The computational performance was tested for various scenarios with different number of computers used in order to assess the efficiency of the cluster. The presented computation times and efficiencies include the linac head modeling with full simulation of the multi leaf collimator (MLC) geometry (including tongue and groove) and stereotactic radiosurgery cones as well as the radiation transport simulation and dose computation within water phantom and patient geometry. RESULTS: The simulations performed in the cluster environment had the same total number of histories recorded and simulated as the simulations in a single computer. The statistical uncertainty achieved was the same for all scenarios. CONCLUSIONS: The investigated approach shows almost linear performance scaling vs number of computers involved.

Evaluation of Dosimetry Check software for IMRT patient-specific quality assurance.

The purpose of this study is to evaluate the use of the Dosimetry Check system for patient-specific IMRT QA. Typical QA methods measure the dose in an array dosimeter surrounded by homogenous medium for which the treatment plan has been recomputed. With the Dosimetry Check system, fluence measurements acquired on a portal dosimeter is applied to the patient's CT scans. Instead of making dose comparisons in a plane, Dosimetry Check system produces isodose lines and dose-volume histograms based on the planning CT images. By exporting the dose distribution from the treatment planning system into the Dosimetry Check system, one is able to make a direct comparison between the calculated dose and the planned dose. The versatility of the software is evaluated with respect to the two IMRT techniques - step and shoot and volumetric arc therapy. The system analyzed measurements made using EPID, PTW seven29, and IBA MatriXX, and an intercomparison study was performed. Plans from patients previously treated at our institution with treated anatomical site on brain, head & neck, liver, lung, and prostate were analyzed using Dosimetry Check system for any anatomical site dependence. We have recommendations and possible precautions that may be necessary to ensure proper QA with the Dosimetry Check system.

Flattening filter-free accelerators: a report from the AAPM Therapy Emerging Technology Assessment Work Group.

This report describes the current state of flattening filter-free (FFF) radiotherapy beams implemented on conventional linear accelerators, and is aimed primarily at practicing medical physicists. The Therapy Emerging Technology Assessment Work Group of the American Association of Physicists in Medicine (AAPM) formed a writing group to assess FFF technology. The published literature on FFF technology was reviewed, along with technical specifications provided by vendors. Based on this information, supplemented by the clinical experience of the group members, consensus guidelines and recommendations for implementation of FFF technology were developed. Areas in need of further investigation were identified. Removing the flattening filter increases beam intensity, especially near the central axis. Increased intensity reduces treatment time, especially for high-dose stereotactic radiotherapy/radiosurgery (SRT/SRS). Furthermore, removing the flattening filter reduces out-of-field dose and improves beam modeling accuracy. FFF beams are advantageous for small field (e.g., SRS) treatments and are appropriate for intensity-modulated radiotherapy (IMRT). For conventional 3D radiotherapy of large targets, FFF beams may be disadvantageous compared to flattened beams because of the heterogeneity of FFF beam across the target (unless modulation is employed). For any application, the nonflat beam characteristics and substantially higher dose rates require consideration during the commissioning and quality assurance processes relative to flattened beams, and the appropriate clinical use of the technology needs to be identified. Consideration also needs to be given to these unique characteristics when undertaking facility planning. Several areas still warrant further research and development. Recommendations pertinent to FFF technology, including acceptance testing, commissioning, quality assurance, radiation safety, and facility planning, are presented. Examples of clinical applications are provided. Several of the areas in which future research and development are needed are also indicated.

Stereotactic body radiation therapy patient specific quality assurance using a two-dimensional array at extended source to surface distance.

METHODS: Five patients with 38 fields have been analyzed in this study. The plans were optimized for the following clinical sites: one liver, one lung, one brain, one prostate and one spine. The detector array used for the measurements was the PTW Seven29 array. All the plans were optimized and calculated using Eclipse v8.9. The center of the array was setup at 215 cm from the source and all the fields were measured and analyzed one by one. All the 30 measurements were performed on a NovalisTX linear accelerator equipped with a high definition multileaf collimator. The evaluation was based mainly on gamma index passing rates using 2 mm distance to agreement (DTA) and 2% dose difference. RESULTS: The accuracy of the Eclipse Treatment Planning System (TPS) at extended Source to Surface Distances (SSDs) using an ionization chamber was measured to be within 1.0%. All the field measurements were performed and analyzed 35 individually. The percent of the points that had a gamma index of less than 1 using 3%/3 mm was >99% for all the measurements. In order to better evaluate our process and distinguish smaller differences a new set of results was obtained by applying gamma index tolerances of 2%/2mm. In this case, the gamma index passing rates ranged from 90.8 to 100% (95.5%±3%). The profile comparison showed that the detector array measurements followed closely the calculated 40 profiles, even for fields optimized with multiple peaks and valleys. CONCLUSION: The choice of the IMRT QA device has an important role in the results of the patient specific QA of the delivered dose to the patient in the case of small targets as in the treatment of spinal targets. In this study, we demonstrated that an extended SSD measurement can improve the sampling resolution of a two-dimensional (2D) detector array, in our case the PTW 45 Seven29 array. This method was shown to be accurate and efficient for measuring highly modulated small fields for pre-treatment patient specific QA.

Phase I quality control framework for monitoring organ-at-risk dose.

Objective.The aim of this work was to develop a Phase I control chart framework for the recently proposed multivariate risk-adjusted Hotelling'sT2chart. Although this control chart alone can identify most patients receiving extreme organ-at-risk (OAR) dose, it is restricted by underlying distributional assumptions, making it sensitive to extreme observations in the sample, as is typically found in radiotherapy plan quality data such as dose-volume histogram (DVH) points. This can lead to slightly poor-quality plans that should have been identified as out-of-control (OC) to be signaled in-control (IC).Approach. We develop a robust iterative control chart framework to identify all OC patients with abnormally high OAR dose and improve them via re-optimization to achieve an IC sample prior to establishing the Phase I control chart, which can be used to monitor future treatment plans.Main Results. Eighty head-and-neck patients were used in this study. After the first iteration, P14, P67, and P68 were detected as OC for high brainstem dose, warranting re-optimization aimed to reduce brainstem dose without worsening other planning criteria. The DVH and control chart were updated after re-optimization. On the second iteration, P14, P67, and P68 were IC, but P40 was identified as OC. After re-optimizing P40's plan and updating the DVH and control chart, P40 was IC, but P14* (P14's re-optimized plan) and P62 were flagged as OC. P14* could not be re-optimized without worsening target coverage, so only P62 was re-optimized. Ultimately, a fully IC sample was achieved. Multiple iterations were needed to identify and improve all OC patients, and to establish a more robust control limit to monitor future treatment plans.Significance. The iterative procedure resulted in a fully IC sample of patients. With this sample, a more robust Phase I control chart that can monitor OAR doses of new plans was established.

Standardizing and improving dose predictions for head and neck cancers using complete sets of OAR contours.

BACKGROUND: Radiotherapy dose predictions have been trained with data from previously treated patients of similar sites and prescriptions. However, clinical datasets are often inconsistent and do not contain the same number of organ at risk (OAR) structures. The effects of missing contour data in deep learning-based dose prediction models have not been studied. PURPOSE: The purpose of this study was to investigate the impacts of incomplete contour sets in the context of deep learning-based radiotherapy dose prediction models trained with clinical datasets and to introduce a novel data substitution method that utilizes automated contours for undefined structures. METHODS: We trained Standard U-Nets and Cascade U-Nets to predict the volumetric dose distributions of patients with head and neck cancers (HNC) using three input variations to evaluate the effects of missing contours, as well as a novel data substitution method. Each architecture was trained with the original contour (OC) inputs, which included missing information, hybrid contour (HC) inputs, where automated OAR contours generated in software were substituted for missing contour data, and automated contour (AC) inputs containing only automated OAR contours. 120 HNC treatments were used for model training, 30 were used for validation and tuning, and 44 were used for evaluation and testing. Model performance and accuracy were evaluated with global whole body dose agreement, PTV coverage accuracy, and OAR dose agreement. The differences in these values between dataset variations were used to determine the effects of missing data and automated contour substitutions. RESULTS: Automated contours used as substitutions for missing data were found to improve dose prediction accuracy in the Standard U-Net and Cascade U-Net, with a statistically significant difference in some global metrics and/or OAR metrics. For both models, PTV coverage between input variations was unaffected by the substitution technique. Automated contours in HC and AC datasets improved mean dose accuracy for some OAR contours, including the mandible and brainstem, with a greater improvement seen with HC datasets. Global dose metrics, including mean absolute error, mean error, and percent error were different for the Standard U-Net but not for the Cascade U-Net. CONCLUSION: Automated contours used as a substitution for contour data improved prediction accuracy for some but not all dose prediction metrics. Compared to the Standard U-Net models, the Cascade U-Net achieved greater precision.