D DiCostanzo

SU‐E‐T‐564: Validation of Photon Dose Calculation Using Mobius3D System Compared to AAA and Acuros XB Systems

PURPOSE To study the Collapsed Cone Convolution/Superposition algorithm in Mobius 3D system for dose calculations in comparison to Acuros XB and AAA dose calculation algorithms in Eclipse treatment planning system. METHODS Dose modeling or correction at density heterogeneity (lung/tissue or bone/tissue interfaces) remains an area of maximum discord amongst treatment planning systems. Thus, four phantoms were constructed and CT scanned with both horizontal and vertical density heterogeneity interfaces. Treatment plans were created with varying field sizes (3×3 cm2 , 5×5 cm2 , and 10×1 0 cm2 ) and energies (6FFF, 6MV, and 15 MV). All plans were created in Eclipse TPS with one single AP field, 100 cm SSD, 1 mm grid size for improved resolution, and 200 MU. Plans were calculated with AAA and Acuros XB algorithms and exported to Mobius3D for recalculation and comparison. Percent depth dose (PDD) and horizontal profiles at multiple depths through density heterogeneity interfaces were compared and analyzed. RPC Lung phantom was also used for complex plan (3DCRT and IMRT) dose comparisons. RESULTS Examination of PDD and horizontal dose profiles were reported graphically and numerically. Highest conformality was noted between AcurosXB and Mobius3D in homogenous sites. CCC in Mobius3D generally matches closer with AcurosXB, especially for large field sizes, compared to AAA. In PDDs, Mobius3D tends to under-predict dose compared to AcurosXB at the tissue-to-water interface up to 3%, while over-predicting dose in and beyond the lung-to-tissue interface compared to AcurosXB up to 7%. In 10×10 profiles with vertical heterogeneity interface, Mobius3D agrees with AcurosXB within 3%/3mm for 6MV and 15MV, but higher difference was seen for 6X-FFF. CONCLUSION The recently released Mobius3D program offers physicist and physician ease in evaluation, rapid plan review, and dose second check to TPS calculations. Future research directions include confirmatory clinical dose calculations and additional evaluation with patient geometry.

SU‐F‐T‐106: A Dosimetric Study of Intensity Modulated Radiation Therapy to Decrease Radiation Dose to the Thoracic Vertebral Bodies in Patients Receiving Concurrent Chemoradiation for Lung Cancer

PURPOSE Recent clinical studies have shown a correlation between radiation dose to the thoracic vertebral bodies (TVB) and the development of hematologic toxicity (HT) in patients receiving chemoradiation (CRT) for lung cancer (LuCa). The feasibility of a bone-marrow sparing (BMS) approach in this group of patients is unknown. We hypothesized that radiation dose to the TVB can be reduced with an intensity modulated radiation therapy(IMRT)/volumetric modulated arc radiotherapy(VMAT) without affecting plan quality. METHODS We identified LuCa cases treated with curative intent CRT using IMRT/VMAT from 4/2009 to 2/2015. The TVBs from T1-T10 were retrospectively contoured. No constraints were placed on the TVB structure initially. A subset were re-planned with BMS-IMRT/VMAT with an objective or reducing the mean TVB dose to <23 Gy. The following data were collected on the initial and BMS plans: mean dose to planning target volume (PTV), lungs-PTV, esophagus, heart; lung V20; cord max dose. Pairwise comparisons were performed using the signed rank test. RESULTS 94 cases received CRT with IMRT/VMAT. We selected 11 cases (7 IMRT, 4 VMAT) with a range of initial mean TVB doses (median 35.7 Gy, range 18.9-41.4 Gy). Median prescription dose was 60 Gy. BMS-IMRT/VMAT significantly reduced the mean TVB dose by a median of 10.2 Gy (range, 1.0-16.7 Gy, p=0.001) and reduced the cord max dose by 2.9 Gy (p=0.014). BMS-IMRT/VMAT had no impact on lung mean (median +17 cGy, p=0.700), lung V20 (median +0.5%, p=0.898), esophagus mean (median +13 cGy, p=1.000) or heart mean (median +16 cGy, p=0.365). PTV-mean dose was not affected by BMS-IMRT/VMAT (median +13 cGy, p=0.653). CONCLUSION BMS-IMRT/VMAT was able to significantly reduce radiation dose to the TVB without compromising plan quality. Prospective evaluation of BMS-IMRT/VMAT in patients receiving CRT for LuCa is warranted to determine if this approach results in clinically significant reductions in HT.

MO‐F‐CAMPUS‐T‐03: Data Driven Approaches for Determination of Treatment Table Tolerance Values for Record and Verification Systems

Purpose: To determine appropriate couch tolerance values for modern radiotherapy linac R&V systems with indexed patient setup. Methods: Treatment table tolerance values have been the most difficult to lower, due to many factors including variations in patient positioning and differences in table tops between machines. We recently installed nine linacs with similar tables and started indexing every patient in our clinic. In this study we queried our R&V database and analyzed the deviation of couch position values from the acquired values at verification simulation for all patients treated with indexed positioning. Mean and standard deviations of daily setup deviations were computed in the longitudinal, lateral and vertical direction for 343 patient plans. The mean, median and standard error of the standard deviations across the whole patient population and for some disease sites were computed to determine tolerance values. Results: The plot of our couch deviation values showed a gaussian distribution, with some small deviations, corresponding to setup uncertainties on non-imaging days, and SRS/SRT/SBRT patients, as well as some large deviations which were spot checked and found to be corresponding to indexing errors that were overriden. Setting our tolerance values based on the median + 1 standard error resulted in tolerance values of 1cm lateral and longitudinal, and 0.5 cm vertical for all non- SRS/SRT/SBRT cases. Re-analizing the data, we found that about 92% of the treated fractions would be within these tolerance values (ignoring the mis-indexed patients). We also analyzed data for disease site based subpopulations and found no difference in the tolerance values that needed to be used. Conclusion: With the use of automation, auto-setup and other workflow efficiency tools being introduced into radiotherapy workflow, it is very essential to set table tolerances that allow safe treatments, but flag setup errors that need to be reassessed before treatments.

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-624: Portal Dosimetry Commissioning of Multiple (6) Varian TrueBeam Linacs Equipped with PortalVision DMI MV Imager

Purpose: To show that a single model for Portal Domisetry (PD) can be established for beam-matched TrueBeam™ linacs that are equipped with the DMI imager (43×43cm effective area). Methods: Our department acquired 6 new TrueBeam™s, 4 “Slim” and 2 “Edge” models. The Slims were equipped with 6 and 10MV photons, and the Edges with 6MV. MLCs differed between the Slims and Edges (Millennium 120 vs HD-MLC respectively). PD model was created from data acquired using a single linac (Slim). This includes maximum field size profile, as well as output factors and acquired measured fluence using the DMI imager. All identical linacs were beam-matched, profiles were within 1% at maximum field size at a variety of depths. The profile correction file was generated from 40×40 profile acquired at 5cm depth, 95cm SSD, and was adjusted for deviation at the field edges and corners. The PD model and profile correction was applied to all six TrueBeam™s and imagers. A variety of jaw only and sliding window (SW) MLC test fields, as well as TG-119 and clinical SW and VMAT plans were run on each linac to validate the model. Results: For 6X and 10X, field by field comparison using 3mm/3% absolute gamma criteria passed 90% or better for all cases. This was also true for composite comparisons of TG-199 and clinical plans, matching our current department criteria. Conclusion: Using a single model per photon energy for PD for the TrueBeam™ equipped with a DMI imager can produce clinically acceptable results across multiple identical and matched linacs. It is also possible to use the same PD model despite different MLCs. This can save time during commissioning and software updates.

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.

SU‐E‐T‐676: Reproducibility and Consistency of Two SunNuclear 3D Scanning Tanks

Purpose: To determine if two Sun Nuclear 3D Scanning (SNC 3DS) tanks collect reproducible and consistent data and test the precision of the SNC Dosimetry auto-setup. Methods: Percent depth doses (PDDs) and profiles were collected on two SNC 3DS tanks with a Varian TrueBeam linear accelerator. SNC Dosimetry auto-setup application was used with CC13 ionization chambers. After auto-setup, collimator light field was checked manually against the position of the chamber. Comparing measured data for repeated measurements with tank 1 allowed evaluation of SNC-3DS auto-setup and tank reproducibility. Comparing measured data between tanks 1 and 2 allowed evaluation of consistency between tanks. Results: Preliminary results showed reproducibility of depth of maximum dose (Dmax) of 0.38mm for a 10cmx10cm field and 0.67mm for 30cmx30cm on a single tank. PDD values at 5cm, 10cm, and 20cm depths were reproducible within 0.26%. Consistency of Dmax between tanks was 0.17mm for a 10cmx10cm field and 0.44mm for 30cmx30cm. PDD values at 5cm, 10cm, and 20cmwere consistent within 0.06%. Profiles showed reproducibility in field width within 0.4mm for a 10cmx10cm field and 0.7mm for a 30cmx30cm field. Profiles showed consistency in field width within 0.2mm for 10cmx10cm and 30cmx30cm field sizes. Penumbra width was reproducible and consistent to under 0.5mm except for 30cmx30cm field size at 30cm depth where the reproducibility was 2.2mm and the consistency was 2.6mm. Conclusion: In conclusion, the SNC 3DS tank shows good reproducibility in measured data. Since the tank to tank variation in measured data is within the uncertainty of repeated single tank measurements the tanks also perform consistently.