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.

An evaluation of quality assurance guidelines comparing the American College of Radiology and American Association of Physicists in Medicine task group 284 for magnetic resonance simulation.

PURPOSE: The purpose of this study was to evaluate similarities and differences in quality assurance (QA) guidelines for a conventional diagnostic magnetic resonance (MR) system and a MR simulator (MR-SIM) system used for radiotherapy. METHODS: In this study, we compared QA testing guidelines from the American College of Radiology (ACR) MR Quality Control (MR QC) Manual to the QA section of the American Association of Physicists in Medicine (AAPM) Task Group 284 report (TG-284). Differences and similarities were identified in testing scope, frequency, and tolerances. QA testing results from an ACR accredited clinical diagnostic MR system following ACR MR QC instructions were then evaluated using TG-284 tolerances. RESULTS: Five tests from the ACR MR QC Manual were not included in TG-284. Five new tests were identified for MR-SIM systems in TG-284 and pertained exclusively to the external laser positioning system of MR-SIM systems. "Low-contrast object detectability" (LCD), "table motion smoothness and accuracy," "transmitter gain," and "geometric accuracy" tests differed between the two QA guides. Tighter tolerances were required in TG-284 for "table motion smoothness and accuracy" and "low contrast object detectability." "Transmitter gain" tolerance was dependent on initial baseline measurements, and TG-284 required that geometric accuracy be tested over a larger field of view than the ACR testing method. All tests from the ACR MR QC Manual for a conventional MR system passed ACR tolerances. The T2-weighted image acquired with ACR sequences failed the 40-spoke requirement from TG-284, transmitter gain was at the 5% tolerance of TG-284, and geometric accuracy could not be evaluated because of required equipment differences. Table motion passed both TG-284 and ACR required tolerances. CONCLUSION: Our study evaluated QA guidelines for an MR-SIM and demonstrated the additional QA requirements of a clinical diagnostic MR system to be used as an MR-SIM in radiotherapy as recommended by TG-284.

Stereotactic radiotherapy of appropriately selected meningiomas and metastatic brain tumor beds with gamma knife icon versus volumetric modulated arc therapy.

PURPOSE: To determine if the gamma knife icon (GKI) can provide superior stereotactic radiotherapy (SRT) dose distributions for appropriately selected meningioma and post-resection brain tumor bed treatments to volumetric modulated arc therapy (VMAT). MATERIALS AND METHODS: Appropriately selected targets were not proximal to great vessels, did not have sensitive soft tissue including organs-at-risk (OARs) within the planning target volume (PTV), and did not have concave tumors containing excessive normal brain tissue. Four of fourteen candidate meningioma patients and six of six candidate patients with brain tumor cavities were considered for this treatment planning comparison study. PTVs were generated for GKI and VMAT by adding 1 mm and 3 mm margins, respectively, to the GTVs. Identical PTV V100% -values were obtained for the GKI and VMAT plans for each patient. Meningioma and tumor bed prescription doses were 52.7-54.0 in 1.7-1.8 Gy fractions and 25 Gy in 5 Gy fractions, respectively. GKI dose rate was 3.735 Gy/min for 16 mm collimators. RESULTS: PTV radical dose homogeneity index was 3.03 ± 0.35 for GKI and 1.27 ± 0.19 for VMAT. Normal brain D1% , D5% , and D10% were lower for GKI than VMAT by 45.8 ± 10.9%, 38.9 ± 11.5%, and 35.4 ± 16.5% respectively. All OARs considered received lower maximum doses for GKI than VMAT. GKI and VMAT treatment times for meningioma plans were 12.1 ± 4.13 min and 6.2 ± 0.32 min, respectively, and, for tumor cavities, were 18.1 ± 5.1 min and 11.0 ± 0.56 min, respectively. CONCLUSIONS: Appropriately selected meningioma and brain tumor bed patients may benefit from GKI-based SRT due to the decreased normal brain and OAR doses relative to VMAT enabled by smaller margins. Care must be taken in meningioma patient selection for SRT with the GKI, even if they are clinically appropriate for VMAT.