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Imaging across the Life Span: Innovations in Imaging and Therapy for Gynecologic Cancer

The focus of this article is radiation therapy for gynecologic cancers, with emphasis on imaging-based treatment planning and delivery. For the various gynecologic cancers, radiation oncologists rely on essential clinical information to triage treatment options, and various imaging studies are performed for treatment planning and radiation therapy delivery. A practical approach is provided to help radiologists tailor their reports for the needs of their radiation oncology and gynecologic oncology colleagues, to optimize multidisciplinary care for patients with gynecologic cancer. Template radiology reports are proposed to address the specific information needs of oncologists at each phase-before, during, and after treatment. Fueled by the rapid progress in engineering and computer sciences during the past 2 decades, remarkable advances have been made in anatomic, functional, and molecular imaging and in radiation treatment planning and delivery in patients with gynecologic cancer. Radiation therapy has evolved from a nontargeted approach to a precisely targeted, highly conformal treatment modality, to further improve treatment outcomes and reduce morbidity. High-quality imaging has become essential for staging of the disease, delineation of tumor extent for treatment planning and delivery, and monitoring therapy response. Anatomic and functional imaging has also been shown to provide prognostic information that allows clinicians to tailor therapy on the basis of personalized patient information. This field is an area of active research, and future clinical trials are warranted to validate preliminary results in the field.

Inter-Fraction Tumor Volume Response during Lung Stereotactic Body Radiation Therapy Correlated to Patient Variables

Purpose Analyze inter-fraction volumetric changes of lung tumors treated with stereotactic body radiation therapy (SBRT) and determine if the volume changes during treatment can be predicted and thus considered in treatment planning. Methods and Materials Kilo-voltage cone-beam CT (kV-CBCT) images obtained immediately prior to each fraction were used to monitor inter-fraction volumetric changes of 15 consecutive patients (18 lung nodules) treated with lung SBRT at our institution (45–54 Gy in 3–5 fractions) in the year of 2011–2012. Spearmans (ρ) correlation and Spearmans partial correlation analysis was performed with respect to patient/tumor and treatment characteristics. Multiple hypothesis correction was performed using False Discovery Rate (FDR) and q-values were reported. Results All tumors studied experienced volume change during treatment. Tumor increased in volume by an average of 15% and regressed by an average of 11%. The overall volume increase during treatment is contained within the planning target volume (PTV) for all tumors. Larger tumors increased in volume more than smaller tumors during treatment (q = 0.0029). The volume increase on CBCT was correlated to the treatment planning gross target volume (GTV) as well as internal target volumes (ITV) (q = 0.0085 and q = 0.0039 respectively) and could be predicted for tumors with a GTV less than 22 mL. The volume increase was correlated to the integral dose (ID) in the ITV at every fraction (q = 0.0049). The peak inter-fraction volume occurred at an earlier fraction in younger patients (q = 0.0122). Conclusions We introduced a new analysis method to follow inter-fraction tumor volume changes and determined that the observed changes during lung SBRT treatment are correlated to the initial tumor volume, integral dose (ID), and patient age. Furthermore, the volume increase during treatment of tumors less than 22mL can be predicted during treatment planning. The volume increase remained significantly less than the overall PTV expansion, and radiation re-planning was therefore not required for the purpose of tumor control. The presence of the studied correlations suggests that the observed volumetric changes may reflect some underlying biologic process rather than random fluctuations.

Quality assurance for a six degrees‐of‐freedom table using a 3D printed phantom

Abstract Purpose To establish a streamlined end‐to‐end test of a 6 degrees‐of‐freedom (6DoF) robotic table using a 3D printed phantom for periodic quality assurance. Methods A 3D printed phantom was fabricated with translational and rotational offsets and an imbedded central ball‐bearing (BB). The phantom underwent each step of the radiation therapy process: CT simulation in a straight orientation, plan generation using the treatment planning software, setup to offset marks at the linac, registration and corrected 6DoF table adjustments via hidden target test, delivery of a Winston‐Lutz test to the BB, and verification of table positioning via field and laser lights. The registration values, maximum total displacement of the combined Winston‐Lutz fields, and a pass or fail criterion of the laser and field lights were recorded. The quality assurance process for each of the three linacs were performed for the first 30 days. Results Within a 95% confidence interval, the overall uncertainty values for both translation and rotation were below 1.0 mm and 0.5° for each linac respectively. When combining the registration values and other uncertainties for all three linacs, the average deviations were within 2.0 mm and 1.0° of the designed translation and rotation offsets of the 3D print respectively. For all three linacs, the maximum total deviation for the Winston‐Lutz test did not exceed 1.0 mm. Laser and light field verification was within tolerance every day for all three linacs given the latest guidance documentation for table repositioning. Conclusion The 3D printer is capable of accurately fabricating a quality assurance phantom for 6DoF positioning verification. The end‐to‐end workflow allows for a more efficient test of the 6DoF mechanics while including other important tests needed for routine quality assurance.

Validating kQ=1.0 assumption in TG51 with PTW 30013 farmer chamber for Varian TrueBeam's 2.5 MV imaging beam

Abstract AAPM Report 142 recommends and the State of Ohio requires that the imaging dose be quantified in radiotherapy applications. Using the TG51 dose calibration protocol for MV Imaging dose measurement requires knowledge of the kQ parameter for the beam quality and the ionization chamber type under investigation. The %dd(10)x of the Varian TrueBeam 2.5 MV imaging beam falls outside the range of the available data for the calculation of the kQ value. Due to the similarities of the 2.5 MV imaging beam and the 60Co beam, we and others made the assumption that kQ = 1.0 in TG51 calculations. In this study, we used the TG21 and TG51 calibration protocols in conjunction to validate that kQ = 1.0 for the 2.5 MV imaging beam using a PTW 30013 farmer chamber. Standard measurements for TG51 absolute dosimetry QA were performed at 100 cm SSD, 10 cm depth, 10 × 10 field size, delivering 100 Monitor Units to a waterproof Farmer Chamber (PTW TN30013) for both 2.5 and 6 MV. Both the TG21 and TG51 formalisms were used to calculate the dose to water per MU at dmax (Dw/MU) for the 6 MV beam. The calculated outputs were 1.0005 and 1.0004 cGy/MU respectively. The TG21 formalism was then used to calculate (Dw/MU) for the 2.5 MV imaging beam. This value was then used in the TG51 formalism to find kQ for the 2.5 MV imaging beam. A kQ value of 1.00 ± 0.01 was calculated for 2.5 MV using this method.

SU-F-R-12: Prediction of TrueBeam Hardware Issues Using Trajectory Log Analysis

PURPOSE To predict potential failures of hardware within the Varian TrueBeam linear accelerator in order to proactively replace parts and decrease machine downtime. METHODS Machine downtime is a problem for all radiation oncology departments and vendors. Most often it is the result of unexpected equipment failure, and increased due to lack of in-house clinical engineering support. Preventative maintenance attempts to assuage downtime, but often is ineffective at preemptively preventing many failure modes such as MLC motor failures, the need to tighten a gantry chain, or the replacement of a jaw motor, among other things. To attempt to alleviate downtime, software was developed in house that determines the maximum value of each axis enumerated in the Truebeam trajectory log files. After patient treatments, this data is stored in a SQL database. Microsoft Power BI is used to plot the average maximum error of each day of each machine as a function of time. The results are then correlated with actual faults that occurred at the machine with the help of Varian service engineers. RESULTS Over the course of six months, 76,312 trajectory logs have been written into the database and plotted in Power BI. Throughout the course of analysis MLC motors have been replaced on three machines due to the early warning of the trajectory log analysis. The service engineers have also been alerted to possible gantry issues on one occasion due to the aforementioned analysis. CONCLUSION Analyzing the trajectory log data is a viable and effective early warning system for potential failures of the TrueBeam linear accelerator. With further analysis and tightening of the tolerance values used to determine a possible imminent failure, it should be possible to pinpoint future issues more thoroughly and for more axes of motion.

SU-F-P-43: Use of Freeware Business Intelligence Software to Trend TG-142 Compliant Linac QA Parameters.

PURPOSE To develop a software platform to track linac QA parameter trends using readily available freeware business intelligence (BI) software METHODS: Spreadsheets were used to collect QA data by many, if not all, institutions; but the trending analysis with data from separate spreadsheets could be a cumbersome task. A freeware version of the Microsoft Power BI software was adapted to trend linac QA parameters to allow us to maintain nine dosimetrically equivalent linacs. A monthly QA spreadsheet (Microsoft Excel) has been used in our institution to collect QA data. We developed a C# computer program to mine QA data from spreadsheets in an automated way and store in a SQL database. The program runs every night automatically, crawls down a predetermined directory in a network hard-drive where all data spreadsheets are saved and searches for new data. If new data are found, they are written into the database. The implemented BI software reads QA data from the database and provides users with dynamic data trending interface as dashboards. The BI dashboard was configured to dynamically filter and drill down to specific parameters and conditions. This enables users to see the existence or missing data for specific tests and trends of linac parameters potentially alerting actions to be taken. RESULTS The developed software platform has been in use since 2015. More than 7680 data points corresponding to TG-142 compliant monthly QAs for nine linacs were automatically retrieved and stored in the database. The developed dashboard has provided access to data for physicists enabling them to dissect the data to observe trends in different parameters in just a few mouse clicks. CONCLUSION The developed BI dashboard has been an invaluable tool providing a common data analysis platform for a quick and easy access to all linac QA parameters and their trends.

SU-G-TeP4-08: Automating the Verification of Patient Treatment Parameters

PURPOSE To automate the daily verification of each patients treatment by utilizing the trajectory log files (TLs) written by the Varian TrueBeam linear accelerator while reducing the number of false positives including jaw and gantry positioning errors, that are displayed in the Treatment History tab of Varians Chart QA module. METHODS Small deviations in treatment parameters are difficult to detect in weekly chart checks, but may be significant in reducing delivery errors, and would be critical if detected daily. Software was developed in house to read TLs. Multiple functions were implemented within the software that allow it to operate via a GUI to analyze TLs, or as a script to run on a regular basis. In order to determine tolerance levels for the scripted analysis, 15,241 TLs from seven TrueBeams were analyzed. The maximum error of each axis for each TL was written to a CSV file and statistically analyzed to determine the tolerance for each axis accessible in the TLs to flag for manual review. The software/scripts developed were tested by varying the tolerance values to ensure veracity. After tolerances were determined, multiple weeks of manual chart checks were performed simultaneously with the automated analysis to ensure validity. RESULTS The tolerance values for the major axis were determined to be, 0.025 degrees for the collimator, 1.0 degree for the gantry, 0.002cm for the y-jaws, 0.01cm for the x-jaws, and 0.5MU for the MU. The automated verification of treatment parameters has been in clinical use for 4 months. During that time, no errors in machine delivery of the patient treatments were found. CONCLUSION The process detailed here is a viable and effective alternative to manually checking treatment parameters during weekly chart checks.

SU-E-T-234: Daily Quality Assurance for a Six Degrees of Freedom Couch Using a Novel Phantom

Purpose: To test the accuracy and reproducibility of both translational and rotational movements for a couch with six degrees of freedom (6DoF) using a novel phantom design Methods: An end-to-end test was carried out using two different phantoms. A 6 cm3 cube with a central fiducial BB (WL-QA Sun Nuclear) and a custom fabricated rectangular prism (31 cm x 8 cm x 8 cm), placed on a baseplate with known angular offsets for pitch, roll and yaw with a central fiducial BB and unique surface structures for registration purposes, were used. The end-to-end test included an initial CT simulation for a reference study, setup to an offset mark on each phantom, registration of the reference CT to the acquired cone-beam CT, and final Winston-Lutz delivery at four cardinal gantry angles. Results for both translational and rotational movements were recorded and compared for both phantoms. Results: Translational and rotational measurements were performed with a PerfectPitch (Varian) couch for 10 trials for both phantoms. Distinct translational shifts were [−5.372±0.384mm, −10.183±0.137mm, 14.028±0.155mm] for the cube and [7.520±0.159mm, −9.117±0.101mm, 16.273±0.115mm] for the prototype phantom for lateral, longitudinal, and vertical shifts, respectively. Distinct rotational adjustments were [1.121±0.102o, −1.067±0.235o, −2.662±0.380o] for the cube and [2.534±0.059o, 1.994±0.025o, 2.094±0.076o] for the prototype for pitch, roll, and yaw, respectively. Winston-Lutz test results performed after 6DoF couch correction from each cardinal gantry angle ranged from 0.26–0.72mm for the cube and 0.55–0.86mm for the prototype. Conclusion: The prototype phantom is more precise for both translational and rotational adjustments compared to a commercial phantom. The design of the prototype phantom allows for a more discernible visual confirmation of correct translational and rotational adjustments with the prototype phantom. Winston-Lutz results are more accurate for the commercial phantom but are still within tolerance for the prototype phantom.

SU‐E‐T‐211: Comparison of Seven New TrueBeam Linacs with Enhanced Beam Data Conformance Using a Beam Comparison Software Tool

Purpose: To develop an independent software tool to assist in commissioning linacs with enhanced beam conformance, as well as perform ongoing QA for dosimetrically equivalent linacs. Methods: Linac manufacturers offer enhanced beam conformance as an option to allow for clinics to complete commissioning efficiently, as well as implement dosimetrically equivalent linacs. The specification for enhanced conformance includes PDD as well as profiles within 80% FWHM. Recently, we commissioned seven Varian TrueBeam linacs with enhanced beam conformance. We developed a software tool in Visual Basic to allow us to load the reference beam data and compare our beam data during commissioning to evaluate enhanced beam conformance. This tool also allowed us to upload our beam data used for commissioning our dosimetrically equivalent beam models to compare and tweak each of our linac beams to match our modelled data in Varian’s Eclipse TPS. This tool will also be used during annual QA of the linacs to compare our beam data to our baseline data, as required by TG-142. Results: Our software tool was used to check beam conformance for seven TrueBeam linacs that we commissioned in the past six months. Using our tool we found that the factory conformed linacs showed up to 3.82% difference in their beam profile data upon installation. Using our beam comparison tool, we were able to adjust the energy and profiles of our beams to accomplish a better than 1.00% point by point data conformance. Conclusion: The availability of quantitative comparison tools is essential to accept and commission linacs with enhanced beam conformance, as well as to beam match multiple linacs. We further intend to use the same tool to ensure our beam data conforms to the commissioning beam data during our annual QA in keeping with the requirements of TG-142.

SU-E-T-468: Implementation of the TG-142 QA Process for Seven Linacs with Enhanced Beam Conformance

Purpose: To develop a TG-142 compliant QA process for 7 Varian TrueBeam linear accelerators (linacs) with enhanced beam conformance and dosimetrically matched beam models. To ensure consistent performance of all 7 linacs, the QA process should include a common set of baseline values for use in routine QA on all linacs. Methods: The TG 142 report provides recommended tests, tolerances and frequencies for quality assurance of medical accelerators. Based on the guidance provided in the report, measurement tests were developed to evaluate each of the applicable parameters listed for daily, monthly and annual QA. These tests were then performed on each of our 7 new linacs as they came on line at our institution. Results: The tolerance values specified in TG-142 for each QA test are either absolute tolerances (i.e. ±2mm) or require a comparison to a baseline value. The results of our QA tests were first used to ensure that all 7 linacs were operating within the suggested tolerance values provided in TG −142 for those tests with absolute tolerances and that the performance of the linacs was adequately matched. The QA test results were then used to develop a set of common baseline values for those QA tests that require comparison to a baseline value at routine monthly and annual QA. The procedures and baseline values were incorporated into a spreadsheets for use in monthly and annual QA. Conclusion: We have developed a set of procedures for daily, monthly and annual QA of our linacs that are consistent with the TG-142 report. A common set of baseline values was developed for routine QA tests. The use of this common set of baseline values for comparison at monthly and annual QA will ensure consistent performance of all 7 linacs.