A radiotherapy community data-driven approach to determine which complexity metrics best predict the impact of atypical TPS beam modeling on clinical dose calculation accuracy.

PURPOSE: To quantify the impact of treatment planning system beam model parameters, based on the actual spread in radiotherapy community data, on clinical treatment plans and determine which complexity metrics best describe the impact beam modeling errors have on dose accuracy. METHODS: Ten beam modeling parameters for a Varian accelerator were modified in RayStation to match radiotherapy community data at the 2.5, 25, 50, 75, and 97.5 percentile levels. These modifications were evaluated on 25 patient cases, including prostate, non-small cell lung, H&N, brain, and mesothelioma, generating 1,000 plan perturbations. Differences in the mean planned dose to clinical target volumes (CTV) and organs at risk (OAR) were evaluated with respect to the planned dose using the reference (50th-percentile) parameter values. Correlation between CTV dose differences, and 18 different complexity metrics were evaluated using linear regression; R-squared values were used to determine the best metric. RESULTS: Perturbations to MLC offset and transmission parameters demonstrated the greatest changes in dose: up to 5.7% in CTVs and 16.7% for OARs. More complex clinical plans showed greater dose perturbation with atypical beam model parameters. The mean MLC Gap and Tongue & Groove index (TGi) complexity metrics best described the impact of TPS beam modeling variations on clinical dose delivery across all anatomical sites; similar, though not identical, trends between complexity and dose perturbation were observed among all sites. CONCLUSION: Extreme values for MLC offset and MLC transmission beam modeling parameters were found to most substantially impact the dose distribution of clinical plans and careful attention should be given to these beam modeling parameters. The mean MLC Gap and TGi complexity metrics were best suited to identifying clinical plans most sensitive to beam modeling errors; this could help provide focus for clinical QA in identifying unacceptable plans.

Analysis of Performance and Failure Modes of the IROC Proton Liver Phantom.

Purpose: To analyze trends in institutional performance and failure modes for the Imaging and Radiation Oncology Core's (IROC's) proton liver phantom. Materials and Methods: Results of 66 phantom irradiations from 28 institutions between 2015 and 2020 were retrospectively analyzed. Univariate analysis and random forest models were used to associate irradiation conditions with phantom results. Phantom results included pass/fail classification, average thermoluminescent dosimeter (TLD) ratio of both targets, and percentage of pixels passing gamma of both targets. The following categories were evaluated in terms of how they predicted these outcomes: irradiation year, treatment planning system (TPS), TPS algorithm, treatment machine, number of irradiations, treatment technique, motion management technique, number of isocenters, and superior-inferior extent (in cm) of the 90% TPS isodose line for primary target 1 (PTV1) and primary target 2 (PTV2). In addition, failures were categorized by failure mode. Results: Average pass rate was approximately 52% and average TLD ratio for both targets had slightly improved. As the treatment field increased to cover the target, the pass rate statistically significantly fell. Lower pass rates were observed for Mevion machines, scattered irradiation techniques, and gating and internal target volume (ITV) motion management techniques. Overall, the accuracy of the random forest modeling of the phantom results was approximately 73% ± 14%. The most important predictor was the superior-inferior extent for both targets and irradiation year. Three failure modes dominated the failures of the phantom: (1) systematic underdosing, (2) poor localization in the superior-inferior direction, and (3) range error. Only 44% of failures have similar failure modes between the 2 targets. Conclusion: Improvement of the proton liver phantom has been observed; however, the pass rate remains the lowest among all IROC phantoms. Through various analysis techniques, range uncertainty, motion management, and underdosing are the main culprits of failures of the proton liver phantom. Clinically, careful consideration of the influences of liver proton therapy is needed to improve phantom performance and patient outcome.

Characterizing the interplay of treatment parameters and complexity and their impact on performance on an IROC IMRT phantom using machine learning.

AIM OF THE STUDY: To elucidate the important factors and their interplay that drive performance on IMRT phantoms from the Imaging and Radiation Oncology Core (IROC). METHODS: IROC's IMRT head and neck phantom contains two targets and an organ at risk. Point and 2D dose are measured by TLDs and film, respectively. 1,542 irradiations between 2012-2020 were retrospectively analyzed based on output parameters, complexity metrics, and treatment parameters. Univariate analysis compared parameters based on pass/fail, and random forest modeling was used to predict output parameters and determine the underlying importance of the variables. RESULTS: The average phantom pass rate was 92% and has not significantly improved over time. The step-and-shoot irradiation technique had significantly lower pass rates that significantly affected other treatment parameters' pass rates. The complexity of plans has significantly increased with time, and all aperture-based complexity metrics (except MCS) were associated with the probability of failure. Random forest-based prediction of failure had an accuracy of 98% on held-out test data not used in model training. While complexity metrics were the most important contributors, the specific metric depended on the set of treatment parameters used during the irradiation. CONCLUSION: With the prevalence of errors in radiotherapy, understanding which parameters affect treatment delivery is vital to improve patient treatment. Complexity metrics were strongly predictive of irradiation failure; however, they are dependent on the specific treatment parameters. In addition, the use of one complexity metric is insufficient to monitor all aspects of the treatment plan.

Statistical process control to monitor use of a web-based autoplanning tool.

PURPOSE: To investigate the use of statistical process control (SPC) for quality assurance of an integrated web-based autoplanning tool, Radiation Planning Assistant (RPA). METHODS: Automatically generated plans were downloaded and imported into two treatment planning systems (TPSs), RayStation and Eclipse, in which they were recalculated using fixed monitor units. The recalculated plans were then uploaded back to the RPA, and the mean dose differences for each contour between the original RPA and the TPSs plans were calculated. SPC was used to characterize the RPA plans in terms of two comparisons: RayStation TPS versus RPA and Eclipse TPS versus RPA for three anatomical sites, and variations in the machine parameters dosimetric leaf gap (DLG) and multileaf collimator transmission factor (MLC-TF) for two algorithms (Analytical Anisotropic Algorithm [AAA]) and Acuros in the Eclipse TPS. Overall, SPC was used to monitor the process of the RPA, while clinics would still perform their routine patient-specific QA. RESULTS: For RayStation, the average mean percent dose differences across all contours were 0.65% ± 1.05%, -2.09% ± 0.56%, and 0.28% ± 0.98% and average control limit ranges were 1.89% ± 1.32%, 2.16% ± 1.31%, and 2.65% ± 1.89% for the head and neck, cervix, and chest wall, respectively. In contrast, Eclipse's average mean percent dose differences across all contours were -0.62% ± 0.34%, 0.32% ± 0.23%, and -0.91% ± 0.98%, while average control limit ranges were 1.09% ± 0.77%, 3.69% ± 2.67%, 2.73% ± 1.86%, respectively. Averaging all contours and removing outliers, a 0% dose difference corresponded with a DLG value of 0.202 ± 0.019 cm and MLC-TF value of 0.020 ± 0.001 for Acuros and a DLG value of 0.135 ± 0.031 cm and MLC-TF value of 0.015 ± 0.001 for AAA. CONCLUSIONS: Differences in mean dose and control limits between RPA and two separately commissioned TPSs were determined. With varying control limits and means, SPC provides a flexible and useful process quality assurance tool for monitoring a complex automated system such as the RPA.

The current status and shortcomings of stereotactic radiosurgery.

Background: Stereotactic radiosurgery (SRS) is a common treatment for intracranial lesions. This work explores the state of SRS treatment delivery to characterize current treatment accuracy based on treatment parameters. Methods: NCI clinical trials involving SRS rely on an end-to-end treatment delivery on a patient surrogate (credentialing phantom) from the Imaging and Radiation Oncology Core (IROC) to test their treatment accuracy. The results of 1072 SRS phantom irradiations between 2012 and 2020 were retrospectively analyzed. Univariate analysis and random forest models were used to associate irradiation conditions with phantom performance. The following categories were evaluated in terms of how they predicted outcomes: year of irradiation, TPS algorithm, machine model, energy, and delivered field size. Results: Overall, only 84.6% of irradiations have met the IROC/NCI acceptability criteria. Pass rate has remained constant over time, while dose calculation accuracy has slightly improved. Dose calculation algorithm (P < .001), collimator (P = .024), and field size (P < .001) were statistically significant predictors of pass/fail. Specifically, pencil beam algorithms and cone collimators were more likely to be associated with failing phantom results. Random forest modeling identified the size of the field as the most important factor for passing or failing followed by algorithm. Conclusion: Constant throughout this retrospective study, approximately 15% of institutions fail to meet IROC/NCI standards for SRS treatment. In current clinical practice, this is particularly associated with smaller fields that yielded less accurate results. There is ongoing need to improve small field dosimetry, beam modeling, and QA to ensure high treatment quality, patient safety, and optimal clinical trials.

Failure Modes in IROC Photon Liver Phantom Irradiations.

PURPOSE: Our purpose was to analyze and classify the patterns of failure for irradiations of the Imaging and Radiation Oncology Core photon liver phantom. METHODS AND MATERIALS: Imaging and Radiation Oncology Core's anthropomorphic liver phantom simulates multitarget liver disease with respiratory motion. Two hundred forty-nine liver phantom results from 2013 to 2019 were analyzed. Phantom irradiations that failed were categorized by the error attributed to the failure. Phantom results were also compared by demographic data, such as machine type, treatment planning system, motion management technique, number of isocenters, and whether the phantom was a first time or repeat irradiation. RESULTS: The failure rate for the liver phantom was 27%. From the 68 irradiations that did not pass, 5 failure modes were identified. The most common failure mode was localization errors in the direction of motion, with over 50% of failures attributed to this mode. The second-most common failure mode was systematic dose errors. The internal target volume technique performed worse than other motion management techniques. Failure modes were different by the number of isocenters used, with multi-isocenter irradiations having more failure modes in a single phantom irradiation. CONCLUSIONS: Motion management techniques and proper alignment of moving targets play a large role in the successful irradiation of the liver phantom. These errors should be examined to ensure accurate patient treatment for liver disease or other sites where multiple moving targets are present.

Survey results of 3D-CRT and IMRT quality assurance practice.

PURPOSE: To create a snapshot of common practices for 3D-CRT and intensity-modulated radiation therapy (IMRT) QA through a large-scale survey and compare to TG-218 recommendations. METHODS: A survey of 3D-CRT and IMRT QA was constructed at and distributed by the IROC-Houston QA center to all institutions monitored by IROC (n = 2,861). The first part of the survey asked about methods to check dose delivery for 3D-CRT. The bulk of the survey focused on IMRT QA, inquiring about treatment modalities, standard tools used to verify planned dose, how assessment of agreement is calculated and the comparison criteria used, and the strategies taken if QA fails. RESULTS: The most common tools for dose verification were a 2D diode array (52.8%), point(s) measurement (39.0%), EPID (27.4%), and 2D ion chamber array (23.9%). When IMRT QA failed, the highest average rank strategy utilized was to remeasure with the same setup, which had an average position ranking of 1.1 with 90.4% of facilities employing this strategy. The second highest average ranked strategy was to move to a new calculation point and remeasure (54.9%); this had an average ranking of 2.1. CONCLUSION: The survey provided a snapshot of the current state of dose verification for IMRT radiotherapy. The results showed variability in approaches and that work is still needed to unify and tighten criteria in the medical physics community, especially in reference to TG-218's recommendations.

Impact of planned dose reporting methods on Gamma pass rates for IROC lung and liver motion phantoms treated with pencil beam scanning protons.

PURPOSE: The purpose of this study is to evaluate the impact of two methods of reporting planned dose distributions on the Gamma analysis pass rates for comparison with measured 2D film dose and simulated delivered 3D dose for proton pencil beam scanning treatment of the Imaging and Radiation Oncology Core (IROC) proton lung and liver mobile phantoms. METHODS AND MATERIALS: Four-dimensional (4D) computed-tomography (CT) image sets were acquired for IROC proton lung and liver mobile phantoms, which include dosimetry inserts that contains targets, thermoluminescent dosimeters and EBT2 films for plan dose verification. 4DCT measured fixed motion magnitudes were 1.3 and 1.0 cm for the lung and liver phantoms, respectively. To study the effects of motion magnitude on the Gamma analysis pass rate, three motion magnitudes for each phantom were simulated by creating virtual 4DCT image sets with motion magnitudes scaled from the scanned phantom motion by 50, 100, and 200%. The internal target volumes were contoured on the maximum intensity projection CTs of the 4DCTs for the lung phantom and on the minimum intensity projection CTs of the 4DCTs for the liver phantom. Treatment plans were optimized on the average intensity projection (AVE) CTs of the 4DCTs using the RayStation treatment planning system. Plan doses were calculated on the AVE CTs, which was defined as the planned AVE dose (method one). Plan doses were also calculated on all 10 phase CTs of the 4DCTs and were registered using target alignment to and equal-weight-summed on the 50% phase (T50) CT, which was defined as the planned 4D dose (method two). The planned AVE doses and 4D doses for phantom treatment were reported to IROC, and the 2D-2D Gamma analysis pass rates for measured film dose relative to the planned AVE and 4D doses were compared. To evaluate motion interplay effects, simulated delivered doses were calculated for each plan by sorting spots into corresponding respiratory phases using spot delivery time recorded in the log files by the beam delivery system to calculate each phase dose and accumulate dose to the T50 CTs. Ten random beam starting phases were used for each beam to obtain the range of the simulated delivered dose distributions. 3D-3D Gamma analyses were performed to compare the planned 4D/AVE doses with simulated delivered doses. RESULTS: The planned 4D dose matched better with the measured 2D film dose and simulated delivered 3D dose than the planned AVE dose. Using planned 4D dose as institution reported planned dose to IROC improved IROC film dose 2D-2D Gamma analysis pass rate from 92 to 96% on average for three films for the lung phantom (7% 5 mm), and from 92 to 94% in the sagittal plane for the liver phantom (7% 4 mm), respectively, compared with using the planned AVE dose. The 3D-3D Gamma analysis (3% 3 mm) pass rate showed that the simulated delivered doses for lung and liver phantoms using 10 random beam starting phases for each delivered beam matched the planned 4D dose significantly better than the planned AVE dose for phantom motions larger than 1 cm (p ≤ 0.04). CONCLUSIONS: It is recommended to use the planned 4D dose as the institution reported planned dose to IROC to compare with the measured film dose for proton mobile phantoms to improve film Gamma analysis pass rate in the IROC credentialing process.

Independent recalculation outperforms traditional measurement-based IMRT QA methods in detecting unacceptable plans.

PURPOSE: To evaluate the performance of an independent recalculation and compare it against current measurement-based patient specific intensity-modulated radiation therapy (IMRT) quality assurance (QA) in predicting unacceptable phantom results as measured by the Imaging and Radiation Oncology Core (IROC). METHODS: When institutions irradiate the IROC head and neck IMRT phantom, they are also asked to submit their internal IMRT QA results. Separately from this, IROC has previously created reference beam models on the Mobius3D platform to independently recalculate phantom results based on the institution's DICOM plan data. The ability of the institutions' IMRT QA to predict the IROC phantom result was compared against the independent recalculation for 339 phantom results collected since 2012. This was done to determine the ability of these systems to detect failing phantom results (i.e., large errors) as well as poor phantom results (i.e., modest errors). Sensitivity and specificity were evaluated using common clinical thresholds, and receiver operator characteristic (ROC) curves were used to compare across different thresholds. RESULTS: Overall, based on common clinical criteria, the independent recalculation was 12 times more sensitive at detecting unacceptable (failing) IROC phantom results than clinical measurement-based IMRT QA. The recalculation was superior, in head-to-head comparison, to the EPID, ArcCheck, and MapCheck devices. The superiority of the recalculation vs these array-based measurements persisted under ROC analysis as the recalculation curve had a greater area under it and was always above that for these measurement devices. For detecting modest errors (poor phantom results rather than failing phantom results), neither the recalculation nor measurement-based IMRT QA performed well. CONCLUSIONS: A simple recalculation outperformed current measurement-based IMRT QA methods at detecting unacceptable plans. These findings highlight the value of an independent recalculation, and raise further questions about the current standard of measurement-based IMRT QA.

Chemical exchange-sensitive spin-lock (CESL) MRI of glucose and analogs in brain tumors.

PURPOSE: Glucose uptake and metabolism can be measured by chemical exchange-sensitive spin-lock (CESL) MRI with an administration of glucose or its analogs. This study investigates the sensitivity, the spatiotemporal characteristics, and the signal source of glucoCESL with a 9L rat brain tumor model. METHODS: Dynamic CESL MRI with intravenous injection of D-glucose, 2-deoxy-D-glucose (2DG), and L-glucose were measured and compared with gadolinium-based dynamic contrast-enhanced (DCE) MRI. RESULTS: The CESL signals with an injection of glucose or analogs have faster and larger changes in tumors than normal brain tissue. In tumors, the CESL signal with 2DG injection has larger and slower peak response than that with D-glucose due to the accumulation of 2DG and 2DG-6-phosphate in the intracellular compartment, whereas L-glucose, which cannot be transported intracellularly by glucose transporters, only induces a small change. The initial glucoCESL maps (< 4 minutes) are qualitatively similar to DCE maps, whereas later maps (> 4 minutes) show more widespread responses. The rise times of D-glucose-CESL and 2DG-CESL signals in the tumor are slower than that of DCE. Our data suggest that the initial CESL contrast primarily reflects a passive increase of glucose content in the extracellular space of tumors due to a higher vascular permeability, whereas the later period may have a significant contribution from the uptake/metabolism of glucose in the intracellular compartment. CONCLUSIONS: Our results demonstrate that glucoCESL MRI has both extracellular and intracellular contributions, and can be a useful tool for measurements of both vascular permeability and glucose uptake in tumors.