Open RT Structures: A Solution for TG-263 Accessibility.

PURPOSE: Consistency of nomenclature within radiation oncology is increasingly important as big data efforts and data sharing become more feasible. Automation of radiation oncology workflows depends on standardized contour nomenclature that enables toxicity and outcomes research, while also reducing medical errors and facilitating quality improvement activities. Recommendations for standardized nomenclature have been published in the American Association of Physicists in Medicine (AAPM) report from Task Group 263 (TG-263). Transitioning to TG-263 requires creation and management of structure template libraries and retraining of staff, which can be a considerable burden on clinical resources. Our aim is to develop a program that allows users to create TG-263-compliant structure templates in English, Spanish, or French to facilitate data sharing. METHODS AND MATERIALS: Fifty-three premade structure templates were arranged by treated organ based on an American Society for Radiation Oncology (ASTRO) consensus paper. Templates were further customized with common target structures, relevant organs at risk (OARs) (eg, spleen for anatomically relevant sites such as the gastroesophageal junction or stomach), subsite- specific templates (eg, partial breast, whole breast, intact prostate, postoperative prostate, etc) and brachytherapy templates. An informal consensus on OAR and target coloration was also achieved, although color selections are fully customizable within the program. RESULTS: The resulting program is usable on any Windows system and generates template files in practice-specific Digital Imaging and Communications In Medicine (DICOM) or XML formats, extracting standardized structure nomenclature from an online database maintained by members of the TG-263U1, which ensures continuous access to up-to-date templates. CONCLUSIONS: We have developed a tool to easily create and name DICOM radiation therapy (DICOM-RT) structures sets that are TG-263-compliant for all planning systems using the DICOM standard. The program and source code are publicly available via GitHub to encourage feedback from community users for improvement and guide further development.

The application of gradient dose segmented analysis of in-vivo EPID images for patients undergoing VMAT in a resource-constrained environment.

The gamma analysis metric is a commonly used metric for VMAT plan evaluation. The major drawback of this is the lack of correlation between gamma passing rates and DVH values. The novel GDSAmean metric was developed by Steers et al. to quantify changes in the PTV mean dose (Dmean ) for VMAT patients. The aim of this work is to apply the GDSA retrospectively on head-and-neck cancer patients treated on the newly acquired Varian Halcyon, to assess changes in GDSAmean , and to evaluate the cause of day-to-day changes in the time-plot series. In-vivo EPID transmission images of head-and-neck cancer patients treated between August 2019 and July 2020 were analyzed retrospectively. The GDSAmean was determined for all patients treated. The changes in patient anatomy and rotational errors were quantified using the daily CBCT images and added to a time-plot with the daily change in GDSAmean . Over 97% of the delivered treatment fractions had a GDSAmean  < 3%. Thirteen of the patients received at least one treatment fraction where the GDSAmean  > 3%. Most of these deviations occurred for the later fractions of radiotherapy treatment. Additionally, 92% of these patients were treated for malignancies involving the larynx and oropharynx. Notable deviations in the effective separation diameters were observed for 62% of the patients where the change in GDSAmean  > 3%. For the other five cases with GDSAmean  < 3%, the mean pitch, roll, and yaw rotational errors were 0.90°, 0.45°, and 0.43°, respectively. A GDSAmean  > 3% was more likely due to a change in separation, whereas a GDSAmean  < 3% was likely caused by rotational errors. Pitch errors were shown to be the most dominant. The GDSAmean is easily implementable and can aid in scheduling new CT scans for patients before significant deviations in dose delivery occur.

Using eclipse scripting to fully automate in-vivo image analysis to improve treatment quality and safety.

PURPOSE: An automated, in-vivo system to detect patient anatomy changes and machine output was developed using novel analysis of in-vivo electronic portal imaging device (EPID) images for every fraction of treatment on a Varian Halcyon. In-vivo approach identifies errors that go undetected by routine quality assurance (QA) to compliment daily machine performance check (MPC), with minimal physicist workload. METHODS: Images for all fractions treated on a Halcyon were automatically downloaded and analyzed at the end of treatment day. For image analysis, compared to first fraction, the mean difference of high-dose region of interest is calculated. This metric has shown to predict changes in planning treatment volume (PTV) mean dose. Flags are raised for: (Type-A) treatment fraction whose mean difference exceeds 10%, to protect against large errors, and (Type-B) patients with three consecutive fractions with mean exceeding ±3%, to protect against systematic trends. If a threshold is exceeded, a physicist is e-mailed, a report for flagged patients, for investigation. To track machine output changes, for all patients treated on a day, the average and standard deviations are uploaded to a QA portal, along with the reviewed MPC, ensuring comprehensive QA for the Halcyon. To guide clinical implementation, a retrospective study from November 2017 till December 2020 was conducted, which grouped errors by treatment site. This framework has been used prospectively since January 2021. RESULTS: From retrospective data of 1633 patients (35 759 fractions), no Type-A errors were found and only 45 patients (2.76%) had Type-B errors. These Type-B deviations were due to head-and-neck weight loss. For 6 months of prospective use (345 patients), 13 patients (3.7%) had Type-B errors and no Type-A errors. CONCLUSIONS: This automated system protects against errors that can occur in vivo to provide a more comprehensive QA. This fully automated framework can be implemented in other centers with a Halcyon, requiring a desktop computer and analysis scripts.

Adaptive replanning using cone beam CT for deformation of original CT simulation.

BACKGROUND: During a course of radiation therapy, anatomical changes such as a decrease in tumour size or weight loss can trigger the need for repeating a computed tomography (CT) simulation scan in order to generate a new treatment plan. This adaptive approach requires a separate appointment for an additional CT scan which generates additional burden, cost, and radiation exposure for patients. CASE PRESENTATION: Here, we present a case of a head and neck cancer patient who required palliative radiation for a large neck mass. During treatment, he had a remarkable response which required a replan due to rapid tumour downsizing. In this case, we used a novel technique to avoid repeating the planning CT simulation by using a mid-treatment high-quality cone beam CT (CBCT) to deform the secondary image (plan CT) of the original planning CT and generate a new adapted treatment plan. CONCLUSION: This is the first report to our knowledge using a Halcyon CBCT to deform the original planning CT in order to generate a new radiation treatment plan, and this novel technique represents a new potential method of adaptive replanning for select patients.

Assessment of predictive indicators of acute gastrointestinal toxicity using in vivo transmission images.

PURPOSE: For pelvic and abdominal treatments, excess dose to the bowel can result in acute toxicities. Current estimates of bowel toxicity are based on pre-treatment dose-volume histogram data. However, the actual dose the bowel receives depends on interfraction variations, such as patient anatomy changes. We propose a method to model bowel toxicities, incorporating in vivo patient information using transit electronic portal imaging device (EPID) images. METHODS AND MATERIALS: For 63 patients treated to the lower thorax, abdomen, or pelvis on the Varian Halcyon, weekly chart review was performed to obtain incidences of grade 2 or higher toxicity, RTOG scale. Twenty patients presented with acute gastrointestinal (GI) toxicity. All patients were treated with conventional fractionation. For each treatment plan, the absolute volume dose-volume histogram of the bowel was exported and analyzed. Additionally, for each fraction of treatment, in vivo EPID images were collected and used to estimate the change in radiation transmission during the course of treatment. A logistic model was used to test correlations between acute GI toxicity and bowel dosimetric parameters as well as metrics obtained from in vivo image measurements. After performing the fit to the in vivo EPID data, the bootstrap resampling method was used to create confidence intervals. In vivo EPID image metrics from an additional 42 patients treated to the lower thorax, abdomen, or pelvis were used to validate the logistic model fit. RESULTS: The incidence of toxicity versus the volume of 40 Gy to the bowel space was fitted with a logistic function, which was superior to an average model (p < 0.0001) and agrees with previously published models. For the initial in vivo EPID data, the incidence of toxicity versus the sum of in vivo transmission measurements showed marginal significance after 15 fractions (p = 0.10) of treatment and a significance of p = 0.038 is seen at the 20th fraction, when compared to an average model. For the validation data set, the logistic model of the in vivo transmission measurement after 20 fractions was superior to the average model (p = 0.043), with the model falling within the 68% confidence interval of the fit of the initial data set. CONCLUSIONS: Dose-volume constraints to reduce the incidence of acute GI toxicity have been validated. The presented novel EPID transmission-based metric can be used to identify GI toxicity as patients progress through treatment.

Using in vivo EPID images to detect and quantify patient anatomy changes with gradient dose segmented analysis.

PURPOSE: To investigate the utility of gradient dose segmented analysis (GDSA) in combination with in vivo electronic portal imaging device (EPID) images to predict changes in the PTV mean dose for patient cases. Also, we use the GDSA to retrospectively analyze patients treated in our clinic to assess deviations for different treatment sites and use time-series data to observe any day-to-day changes. METHODS: In vivo EPID transit images acquired on the Varian Halcyon were analyzed for simulated errors in a phantom, including gas bubbles, weight loss, patient shifts, and an arm erroneously in the field. GDSA threshold parameters were tuned to maximize the coefficient of determination (R2 ) between GDSA metrics and the change in the PTV mean dose (Dmean ) as estimated in a treatment planning system (TPS). Similarly for a gamma analysis, the gamma criteria were adjusted to maximize R2 between gamma pass rate and the change in the PTV Dmean from the TPS. The predictive accuracy of these models was tested on patient data measuring the mean and standard deviation of the difference in the predicted change in PTV Dmean and the change in PTV Dmean measured in the TPS. This analysis was extended retrospectively for every patient treated over a 23-month period (n = 852 patients) to assess the range of expected deviations that occurred during routine clinical operation, as well as to assess any differences between treatment sites. Grouping patients treated on the same day, a time-series analysis was performed to determine if GDSA metrics could add value in tracking machine behavior over time. RESULTS: For the phantom data, analyzing the errors, except for shifts, and comparing the change in PTV Dmean and GDSA mean, a maximal R2  = 0.90 was found for a dose threshold of 5% and gradient threshold of 3 mm. For the gamma approach a linear fit between the gamma pass rate for change in the PTV Dmean was assessed for different criteria, using the same image data. A maximal, R2  = 0.84 was found for a gamma criteria of 3%/3 mm, 45% lower dose threshold. For patient data, the predictive accuracy of the change in the PTV Dmean using the GDSA approach and the gamma approach was 0.09 ± 0.98 % and - 0.65 ± 2.21%, respectively. Comparing the two approaches the accuracy did not significantly differ (P = 0.38), whereas the precision of the GDSA prediction is significantly less (P < 0.001). The dosimetric impact of shifts was not detectable with either the GDSA or gamma approach. Analysis of all patients treated over 23 months showed that over 95% of fractions treated deviated from the first fraction by 2% or less. Deviations> 2% occurred most frequently for the later fractions of head-and-neck and lung treatments. Additionally, averaging the GDSA mean metric over all patients on a given treatment day showed that changes in the machine output on the order of 1% could be identified. CONCLUSIONS: GDSA of in vivo EPID images is a useful technique for monitoring patient changes during the course of treatment, particularly weight loss and tumor shrinkage. The GDSA mean provides a quantitative estimate of the change in the PTV Dmean , giving a simple, quantitative metric by which to flag patients with clinically meaningful deviations in treatment. Averaging the GDSA metric over all patients treated on a given day and tracking daily variations can also provide a flag for any systematic deviations in treatment due to machine performance.

Low-Dose Image-Guided Pediatric CNS Radiation Therapy: Final Analysis From a Prospective Low-Dose Cone-Beam CT Protocol From a Multinational Pediatrics Consortium.

BACKGROUND: Lower-dose cone-beam computed tomography protocols for image-guided radiotherapy may permit target localization while minimizing radiation exposure. We prospectively evaluated a lower-dose cone-beam protocol for central nervous system image-guided radiotherapy across a multinational pediatrics consortium. METHODS: Seven institutions prospectively employed a lower-dose cone-beam computed tomography central nervous system protocol (weighted average dose 0.7 mGy) for patients ≤21 years. Treatment table shifts between setup with surface lasers versus cone-beam computed tomography were used to approximate setup accuracy, and vector magnitudes for these shifts were calculated. Setup group mean, interpatient, interinstitution, and random error were estimated, and clinical factors were compared by mixed linear modeling. RESULTS: Among 96 patients, with 2179 pretreatment cone-beam computed tomography acquisitions, median age was 9 years (1-20). Setup parameters were 3.13, 3.02, 1.64, and 1.48 mm for vector magnitude group mean, interpatient, interinstitution, and random error, respectively. On multivariable analysis, there were no significant differences in mean vector magnitude by age, gender, performance status, target location, extent of resection, chemotherapy, or steroid or anesthesia use. Providers rated >99% of images as adequate or better for target localization. CONCLUSIONS: A lower-dose cone-beam computed tomography protocol demonstrated table shift vector magnitude that approximate clinical target volume/planning target volume expansions used in central nervous system radiotherapy. There were no significant clinical predictors of setup accuracy identified, supporting use of this lower-dose cone-beam computed tomography protocol across a diverse pediatric population with brain tumors.

Evaluating the sensitivity of Halcyon's automatic transit image acquisition for treatment error detection: A phantom study using static IMRT.

PURPOSE: The Varian Halcyon™ electronic portal imaging detector is always in-line with the beam and automatically acquires transit images for every patient with full-field coverage. These images could be used for "every patient, every monitor unit" quality assurance (QA) and eventually adaptive radiotherapy. This study evaluated the imager's sensitivity to potential clinical errors and day-to-day variations from clinical exit images. METHODS: Open and modulated fields were delivered for each potential error. To evaluate output changes, monitor units were scaled by 2%-10% and delivered to solid water slabs and a homogeneous CIRS phantom. To mimic weight changes, 0.5-5.0 cm of buildup was added to the solid water. To evaluate positioning changes, a homogeneous and heterogeneous CIRS phantom were shifted 2-10 cm and 0.2-1.5 cm, respectively. For each test, mean relative differences (MRDs) and standard deviations in the pixel-difference histograms (σRD ) between test and baseline images were calculated. Lateral shift magnitudes were calculated using cross-correlation and edge-detection filtration. To assess patient variations, MRD and σRD were calculated from six prostate patients' daily exit images and compared between fractions with and without gas present. RESULTS: MRDs responded linearly to output and buildup changes with a standard deviation of 0.3%, implying a 1% output change and 0.2 cm changes in buildup could be detected with 2.5σ confidence. Shifting the homogenous phantom laterally resulted in detectable MRD and σRD changes, and the cross-correlation function calculated the shift to within 0.5 mm for the heterogeneous phantom. MRD and σRD values were significantly associated with the presence of gas for five of the six patients. CONCLUSIONS: Rapid analyses of automatically acquired Halcyon™ exit images could detect mid-treatment changes with high sensitivity, though appropriate thresholds will need to be set. This study presents the first steps toward developing effortless image evaluation for all aspects of every patient's treatment.

A quantification of the effectiveness of EPID dosimetry and software-based plan verification systems in detecting incidents in radiotherapy.

PURPOSE: Complex treatments in radiation therapy require robust verification in order to prevent errors that can adversely affect the patient. For this purpose, the authors estimate the effectiveness of detecting errors with a "defense in depth" system composed of electronic portal imaging device (EPID) based dosimetry and a software-based system composed of rules-based and Bayesian network verifications. METHODS: The authors analyzed incidents with a high potential severity score, scored as a 3 or 4 on a 4 point scale, recorded in an in-house voluntary incident reporting system, collected from February 2012 to August 2014. The incidents were categorized into different failure modes. The detectability, defined as the number of incidents that are detectable divided total number of incidents, was calculated for each failure mode. RESULTS: In total, 343 incidents were used in this study. Of the incidents 67% were related to photon external beam therapy (EBRT). The majority of the EBRT incidents were related to patient positioning and only a small number of these could be detected by EPID dosimetry when performed prior to treatment (6%). A large fraction could be detected by in vivo dosimetry performed during the first fraction (74%). Rules-based and Bayesian network verifications were found to be complimentary to EPID dosimetry, able to detect errors related to patient prescriptions and documentation, and errors unrelated to photon EBRT. Combining all of the verification steps together, 91% of all EBRT incidents could be detected. CONCLUSIONS: This study shows that the defense in depth system is potentially able to detect a large majority of incidents. The most effective EPID-based dosimetry verification is in vivo measurements during the first fraction and is complemented by rules-based and Bayesian network plan checking.