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This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was <200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: Treatment planning for craniospinal irradiation (CSI) is complex and time-consuming, especially for resource-constrained centers. To alleviate demanding workflows, we successfully automated the pediatric CSI planning pipeline in previous work. In this work, we validated our CSI autosegmentation and autoplanning tool on a large dataset from St. Jude Children's Research Hospital. Methods: Sixty-three CSI patient CT scans were involved in the study. Preplanning scripts were used to automatically verify anatomical compatibility with the autoplanning tool. The autoplanning pipeline generated 15 contours and a composite CSI treatment plan for each of the compatible test patients (n=51). Plan quality was evaluated quantitatively with target coverage and dose to normal tissue metrics and qualitatively with physician review, using a 5-point Likert scale. Three pediatric radiation oncologists from 3 institutions reviewed and scored 15 contours and a corresponding composite CSI plan for the final 51 test patients. One patient was scored by 3 physicians, resulting in 53 plans scored total. Results: The algorithm automatically detected 12 incompatible patients due to insufficient junction spacing or head tilt and removed them from the study. Of the 795 autosegmented contours reviewed, 97% were scored as clinically acceptable, with 92% requiring no edits. Of the 53 plans scored, all 51 brain dose distributions were scored as clinically acceptable. For the spine dose distributions, 92%, 100%, and 68% of single, extended, and multiple-field cases, respectively, were scored as clinically acceptable. In all cases (major or minor edits), the physicians noted that they would rather edit the autoplan than create a new plan. Conclusions: We successfully validated an autoplanning pipeline on 51 patients from another institution, indicating that our algorithm is robust in its adjustment to differing patient populations. We automatically generated 15 contours and a comprehensive CSI treatment plan for each patient without physician intervention, indicating the potential for increased treatment planning efficiency and global access to high-quality radiation therapy. [ABSTRACT FROM AUTHOR]

Purpose: Target delineation for radiation therapy is a time‐consuming and complex task. Autocontouring gross tumor volumes (GTVs) has been shown to increase efficiency. However, there is limited literature on post‐operative target delineation, particularly for CT‐based studies. To this end, we trained a CT‐based autocontouring model to contour the post‐operative GTV of pediatric patients with medulloblastoma. Methods: One hundred four retrospective pediatric CT scans were used to train a GTV auto‐contouring model. Eighty patients were then preselected for contour visibility, continuity, and location to train an additional model. Each GTV was manually annotated with a visibility score based on the number of slices with a visible GTV (1 = < 25%, 2 = 25–50%, 3 = > 50–75%, and 4 = > 75–100%). Contrast and the contrast‐to‐noise ratio (CNR) were calculated for the GTV contour with respect to a cropped background image. Both models were tested on the original and pre‐selected testing sets. The resulting surface and overlap metrics were calculated comparing the clinical and autocontoured GTVs and the corresponding clinical target volumes (CTVs). Results: Eighty patients were pre‐selected to have a continuous GTV within the posterior fossa. Of these, 7, 41, 21, and 11 were visibly scored as 4, 3, 2, and 1, respectively. The contrast and CNR removed an additional 11 and 20 patients from the dataset, respectively. The Dice similarity coefficients (DSC) were 0.61 ± 0.29 and 0.67 ± 0.22 on the models without pre‐selected training data and 0.55 ± 13.01 and 0.83 ± 0.17 on the models with pre‐selected data, respectively. The DSC on the CTV expansions were 0.90 ± 0.13. Conclusion: We successfully automatically contoured continuous GTVs within the posterior fossa on scans that had contrast > ± 10 HU. CT‐Based auto‐contouring algorithms have potential to positively impact centers with limited MRI access. [ABSTRACT FROM AUTHOR]

Background: Successful generation of biomechanical‐model‐based deformable image registration (BM‐DIR) relies on user‐defined parameters that dictate surface mesh quality. The trial‐and‐error process to determine the optimal parameters can be labor‐intensive and hinder DIR efficiency and clinical workflow. Purpose: To identify optimal parameters in surface mesh generation as boundary conditions for a BM‐DIR in longitudinal liver and lung CT images to facilitate streamlined image registration processes. Methods: Contrast‐enhanced CT images of 29 colorectal liver cancer patients and end‐exhale four‐dimensional CT images of 26 locally advanced non‐small cell lung cancer patients were collected. Different combinations of parameters that determine the triangle mesh quality (voxel side length and triangle edge length) were investigated. The quality of DIRs generated using these parameters was evaluated with metrics for geometric accuracy, robustness, and efficiency. Metrics for geometric accuracy included target registration error (TRE) of internal vessel bifurcations, dice similar coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) for organ contours, and number of vertices in the triangle mesh. American Association of Physicists in Medicine Task Group 132 was used to ensure parameters met TRE, DSC, MDA recommendations before the comparison among the parameters. Robustness was evaluated as the success rate of DIR generation, and efficiency was evaluated as the total time to generate boundary conditions and compute finite element analysis. Results: Voxel side length of 0.2 cm and triangle edge length of 3 were found to be the optimal parameters for both liver and lung, with success rate of 1.00 and 0.98 and average DIR computation time of 100 and 143 s, respectively. For this combination, the average TRE, DSC, MDA, and HD were 0.38–0.40, 0.96–0.97, 0.09–0.12, and 0.87–1.17 mm, respectively. Conclusion: The optimal parameters were found for the analyzed patients. The decision‐making process described in this study serves as a recommendation for BM‐DIR algorithms to be used for liver and lung. These parameters can facilitate consistence in the evaluation of published studies and more widespread utilization of BM‐DIR in clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: Re‐planning for four‐dimensional computed tomography (4DCT)‐based lung adaptive radiotherapy commonly requires deformable dose mapping between the planning average‐intensity image (AVG) and the newly acquired AVG. However, such AVG‐AVG deformable image registration (DIR) lacks accuracy assessment. The current work quantified and compared geometric accuracies of AVG‐AVG DIR and corresponding phase‐phase DIRs, and subsequently investigated the clinical impact of such AVG‐AVG DIR on deformable dose mapping. Methods and Materials: Hybrid intensity‐based AVG‐AVG and phase‐phase DIRs were performed between the planning and mid‐treatment 4DCTs of 28 non‐small cell lung cancer patients. An automated landmark identification algorithm detected vessel bifurcation pairs in both lungs. Target registration error (TRE) of these landmark pairs was calculated for both DIR types. The correlation between TRE and respiratory‐induced landmark motion in the planning 4DCT was analyzed. Global and local dose metrics were used to assess the clinical implications of AVG‐AVG deformable dose mapping with both DIR types. Results: TRE of AVG‐AVG and phase‐phase DIRs averaged 3.2 ± 1.0 and 2.6 ± 0.8 mm respectively (p < 0.001). Using AVG‐AVG DIR, TREs for landmarks with <10 mm motion averaged 2.9 ± 2.0 mm, compared to 3.1 ± 1.9 mm for the remaining landmarks (p < 0.01). Comparatively, no significant difference was demonstrated for phase‐phase DIRs. Dosimetrically, no significant difference in global dose metrics was observed between doses mapped with AVG‐AVG DIR and the phase‐phase DIR, but a positive linear relationship existed (p = 0.04) between the TRE of AVG‐AVG DIR and local dose difference. Conclusions: When the region of interest experiences <10 mm respiratory‐induced motion, AVG‐AVG DIR may provide sufficient geometric accuracy; conversely, extra attention is warranted, and phase‐phase DIR is recommended. Dosimetrically, the differences in geometric accuracy between AVG‐AVG and phase‐phase DIRs did not impact global lung‐based metrics. However, as more localized dose metrics are needed for toxicity assessment, phase‐phase DIR may be required as its lower mean TRE improved voxel‐based dosimetry. [ABSTRACT FROM AUTHOR]

Purpose: Treatment planning system (TPS) dose calculations have previously been shown to be sensitive to modeling errors, especially when treating with complex strategies like intensity‐modulated radiation therapy (IMRT). This work investigates the dosimetric impact of several dosimetric and nondosimetric beam modeling parameters, based on their distribution in the radiotherapy community, in two commercial TPSs in order to understand the realistic potential for dose deviations and their clinical effects. Methods and materials: Beam models representing standard 120‐leaf Varian Clinac‐type machines were developed in Eclipse 13.5 (AAA algorithm) and RayStation 9A (v8.99, collapsed‐cone algorithm) based upon median values of dosimetric measurements from Imaging and Radiation Oncology Core (IROC) Houston site visit data and community beam modeling parameter survey data in order to represent a baseline linear accelerator. Five clinically acceptable treatment plans (three IMRT, two VMAT) were developed for the IROC head and neck phantom. Dose distributions for each plan were recalculated after individually modifying parameters of interest (e.g., MLC transmission, percent depth doses [PDDs], and output factors) according to the 2.5th to 97.5th percentiles of community survey and machine performance data to encompass the realistic extent of variance in the radiotherapy community. The resultant dose distributions were evaluated by examining relative changes in average dose for thermoluminescent dosimeter (TLD) locations across the two target volumes and organ at risk (OAR). Interplay was also examined for parameters generating changes in target dose greater than 1%. Results: For Eclipse, dose calculations were sensitive to changes in the dosimetric leaf gap (DLG), which resulted in differences from −5% to +3% to the targets relative to the baseline beam model. Modifying the MLC transmission factor introduced differences up to ± 1%. For RayStation, parameters determining MLC behaviors likewise contributed substantially; the MLC offset introduced changes in dose from −4% to +7%, and the MLC transmission caused changes of −4% to +2%. Among the dosimetric qualities examined, changes in PDD implementation resulted in the most substantial changes, but these were only up to ±1%. Other dosimetric factors had <1% impact on dose accuracy. Interplay between impactful parameters was found to be minimal. Conclusion: Factors related to the modeling of the MLC, particularly relating to the leaf offset, can cause clinically significant changes in the calculated dose for IMRT and VMAT plans. This should be of concern to the radiotherapy community because the clinical effects of poor TPS commissioning were based on reported data from clinically implemented beam models. These results further reinforce that dose errors caused by poor TPS calculations are often involved in IROC phantom failures. [ABSTRACT FROM AUTHOR]

Purpose: Between July 2013 and August 2019, 22% of the imaging and radiation oncology core (IROC) spine, and 15% of the moving lung phantom irradiations have failed to meet established acceptability criteria. The spine phantom simulates a highly modulated stereotactic body radiation therapy (SBRT) case, whereas the lung phantom represents a low‐to‐none modulation moving target case. In this study, we assessed the contribution of dose calculation errors to these phantom results and evaluated their effects on failure rates. Methods: We evaluated dose calculation errors by comparing the calculation accuracy of various institutions' treatment planning systems (TPSs) vs IROC‐Houston's previously established independent dose recalculation system (DRS). Each calculation was compared with the measured dose actually delivered to the phantom; cases in which the recalculation was more accurate were interpreted as a deficiency in the institution's TPS. A total of 258 phantom irradiation plans (172 lung and 86 spine) were recomputed. Results: Overall, the DRS performed better than the TPSs in 47% of the spine phantom cases. However, the DRS was more accurate in 93% of failing spine phantom cases (with an average improvement of 2.35%), indicating a deficiency in the institution's treatment planning system. Deficiencies in dose calculation accounted for 60% of the overall discrepancy between measured and planned doses among spine phantoms. In contrast, lung phantom DRS calculations were more accurate in only 35% and 42% of all and failing lung phantom cases respectively, indicating that dose calculation errors were not substantially present. These errors accounted for only 30% of the overall discrepancy between measured and planned doses. Conclusions: Dose calculation errors are common and substantial in IROC spine phantom irradiations, highlighting a major failure mode in this phantom and in clinical treatment management of these cases. In contrast, dose calculation accuracy had only a minimal contribution to failing lung phantom results, indicating that other failure modes drive problems with this phantom and similar clinical treatments. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this work was to provide a novel description of how the radiotherapy community configures treatment planning system (TPS) radiation beam models for clinically used treatment machines. Here we describe the results of a survey of self‐reported TPS beam modeling parameter values across different C‐arm linear accelerators, beam energies, and multileaf collimator (MLC) configurations. Acquisition and validation methods: Beam modeling data were acquired via electronic survey implemented through the Imaging and Radiation Oncology Core (IROC) Houston Quality Assurance Center's online facility questionnaire. The survey was open to participation from January 2018 through January 2019 for all institutions monitored by IROC. After quality control, 2818 beam models were collected from 642 institutions. This survey, designed for Eclipse, Pinnacle, and RayStation, instructed physicists to report parameter values used to model the radiation source and MLC for each treatment machine and beam energy used clinically for intensity‐modulated radiation therapy. Parameters collected included the effective source/spot size, MLC transmission, dosimetric leaf gap, tongue and groove effect, and other nondosimetric parameters specific to each TPS. To facilitate survey participation, instructions were provided on how to identify requested beam modeling parameters within each TPS environment. Data format and usage notes: Numeric values of the beam modeling parameters are compiled and tabulated according to TPS and calculation algorithm, linear accelerator model class, beam energy, and MLC configuration. Values are also presented as distributions, ranging from the 2.5th to the 97.5th percentile. Potential applications: These data provide an independent guide describing how the radiotherapy community mathematically represents its clinical radiation beams. These distributions may be used by the community for comparison during the commissioning or verification of their TPS beam models. Ultimately, we hope that the current work will allow institutions to spot potentially suspicious parameter values and help ensure more accurate radiotherapy delivery. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Strong magnetic fields affect radiation dose deposition in MRI-guided radiation therapy systems, particularly at interfaces between tissues of differing densities such as those in the thorax. In this study, we evaluated the impact of a 1.5 T magnetic field on radiation-induced lung damage in C57L/J mice. We irradiated 140 mice to the whole thorax with parallel-opposed Co-60 beams to doses of 0, 9.0, 10.0, 10.5, 11.0, 12.0, or 13.0 Gy (20 mice per dose group). Ten mice per dose group were irradiated while a 1.5 T magnetic field was applied transverse to the radiation beam and ten mice were irradiated with the magnetic field set to 0 T. We compared survival and noninvasive assays of radiation-induced lung damage, namely respiratory rate and metrics derived from thoracic cone-beam CTs, between the two sets of mice. We report two main results. First, the presence of a transverse 1.5 T field during irradiation had no impact on survival of C57L/J mice. Second, there was a small but statistically significant effect on noninvasive assays of radiation-induced lung damage. These results provide critical safety data for the clinical introduction of MRI-guided radiation therapy systems. [ABSTRACT FROM AUTHOR]

Recent reports have shown that very high dose rate radiation (35–100 Gy/second) referred to as FLASH tends to spare the normal tissues while retaining the therapeutic effect on tumor. We undertook a series of experiments to assess if ultra-high dose rate of 35 Gy/second can spare the immune system in models of radiation induced lymphopenia. We compared the tumoricidal potency of ultra-high dose rate and conventional dose rate radiation using a classical clonogenic assay in murine pancreatic cancer cell lines. We also assessed the lymphocyte sparing potential in cardiac and splenic irradiation models of lymphopenia and assessed the severity of radiation-induced gastrointestinal toxicity triggered by the two dose rate regimes in vivo. Ultra-high dose rate irradiation more potently induces clonogenic cell death than conventional dose rate irradiation with a dose enhancement factor at 10% survival (DEF10) of 1.310 and 1.365 for KPC and Panc02 cell lines, respectively. Ultra-high dose rate was equally potent in depleting CD3, CD4, CD8, and CD19 lymphocyte populations in both cardiac and splenic irradiation models of lymphopenia. Radiation-induced gastrointestinal toxicity was more pronounced and mouse survival (7 days vs. 15 days, p = 0.0001) was inferior in the ultra-high dose rate arm compared to conventional dose rate arm. These results suggest that, contrary to published data in other models of radiation-induced acute and chronic toxicity, dose rates of 35 Gy/s do not protect mice from the detrimental side effects of irradiation in our models of cardiac and splenic radiation-induced lymphopenia or gastrointestinal mucosal injury. [ABSTRACT FROM AUTHOR]