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

Abstract

Purpose: To automate the daily verification of each patient’s 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 Varian’s 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:more » 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.« less

Authors:
; ; ;  [1]
  1. The Ohio State University, Columbus, OH (United States)
Publication Date:
OSTI Identifier:
22649471
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; COMPUTER CODES; ERRORS; LINEAR ACCELERATORS; PATIENTS; RADIOTHERAPY; TOLERANCE; VERIFICATION

Citation Formats

DiCostanzo, D, Ayan, A, Woollard, J, and Gupta, N. SU-G-TeP4-08: Automating the Verification of Patient Treatment Parameters. United States: N. p., 2016. Web. doi:10.1118/1.4957133.
DiCostanzo, D, Ayan, A, Woollard, J, & Gupta, N. SU-G-TeP4-08: Automating the Verification of Patient Treatment Parameters. United States. doi:10.1118/1.4957133.
DiCostanzo, D, Ayan, A, Woollard, J, and Gupta, N. Wed . "SU-G-TeP4-08: Automating the Verification of Patient Treatment Parameters". United States. doi:10.1118/1.4957133.
@article{osti_22649471,
title = {SU-G-TeP4-08: Automating the Verification of Patient Treatment Parameters},
author = {DiCostanzo, D and Ayan, A and Woollard, J and Gupta, N},
abstractNote = {Purpose: To automate the daily verification of each patient’s 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 Varian’s 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.},
doi = {10.1118/1.4957133},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • Purpose: Physics second-checks for external beam radiation therapy are performed, in-part, to verify that the machine parameters in the Record-and-Verify (R&V) system that will ultimately be sent to the LINAC exactly match the values initially calculated by the Treatment Planning System (TPS). While performing the second-check, a large portion of the physicists’ time is spent navigating and arranging display windows to locate and compare the relevant numerical values (MLC position, collimator rotation, field size, MU, etc.). Here, we describe the development of a software tool that guides the physicist by aggregating and succinctly displaying machine parameter data relevant to themore » physics second-check process. Methods: A data retrieval software tool was developed using Python to aggregate data and generate a list of machine parameters that are commonly verified during the physics second-check process. This software tool imported values from (i) the TPS RT Plan DICOM file and (ii) the MOSAIQ (R&V) Structured Query Language (SQL) database. The machine parameters aggregated for this study included: MLC positions, X&Y jaw positions, collimator rotation, gantry rotation, MU, dose rate, wedges and accessories, cumulative dose, energy, machine name, couch angle, and more. Results: A GUI interface was developed to generate a side-by-side display of the aggregated machine parameter values for each field, and presented to the physicist for direct visual comparison. This software tool was tested for 3D conformal, static IMRT, sliding window IMRT, and VMAT treatment plans. Conclusion: This software tool facilitated the data collection process needed in order for the physicist to conduct a second-check, thus yielding an optimized second-check workflow that was both more user friendly and time-efficient. Utilizing this software tool, the physicist was able to spend less time searching through the TPS PDF plan document and the R&V system and focus the second-check efforts on assessing the patient-specific plan-quality.« less
  • Purpose: We developed a method to calculate patient doses corresponding to IMRT QA measurements in order to determine and assess the actual dose delivered for plans with failed (or borderline) IMRT QA. This work demonstrates the feasibility of automatically computing delivered patient dose from portal dosimetry measurements in the Varian TPS system, which would provide a valuable and clinically viable IMRT QA tool for physicists and physicians. Methods: IMRT QA fluences were measured using portal dosimetry, processed using in-house matlab software, and imported back into Eclipse to calculate dose on the planning CT. To validate the proposed workflow, the Eclipsemore » calculated portal dose for a 5-field sliding window prostate boost plan was processed as described above. The resulting dose was compared to the planned dose and found to be within 0.5 Gy. Two IMRT QA results for the prostate boost plan (one that failed and one that passed) were processed and the resulting patient doses were evaluated. Results: The max dose difference between IMRT QA #1 and the original planned and approved dose is 4.5 Gy, while the difference between the planned and IMRT QA #2 dose is 4.0 Gy. The inferior portion of the PTV is slightly underdosed in both plans, and the superior portion is slightly overdosed. The patient dose resulting from IMRT QA #1 and #2 differs by only 0.5 Gy. With this new information, it may be argued that the evaluated plan alteration to obtain passing gamma analysis produced clinically irrelevant differences. Conclusion: Evaluation of the delivered QA dose on the planning CT provides valuable information about the clinical relevance of failed or borderline IMRT QAs. This particular workflow demonstrates the feasibility of pushing the measured IMRT QA portal dosimetry results directly back onto the patient planning CT within the Varian system.« less
  • Purpose: To implement a comprehensive non-measurement-based verification program for patient-specific IMRT QA Methods: Based on published guidelines, a robust IMRT QA program should assess the following components: 1) accuracy of dose calculation, 2) accuracy of data transfer from the treatment planning system (TPS) to the record-and-verify (RV) system, 3) treatment plan deliverability, and 4) accuracy of plan delivery. Results: We have implemented an IMRT QA program that consist of four components: 1) an independent re-calculation of the dose distribution in the patient anatomy with a commercial secondary dose calculation program: Mobius3D (Mobius Medical Systems, Houston, TX), with dose accuracy evaluationmore » using gamma analysis, PTV mean dose, PTV coverage to 95%, and organ-at-risk mean dose; 2) an automated in-house-developed plan comparison system that compares all relevant plan parameters such as MU, MLC position, beam iso-center position, collimator, gantry, couch, field size settings, and bolus placement, etc. between the plan and the RV system; 3) use of the RV system to check the plan deliverability and further confirm using “mode-up” function on treatment console for plans receiving warning; and 4) implementation of a comprehensive weekly MLC QA, in addition to routine accelerator monthly and daily QA. Among 1200 verifications, there were 9 cases of suspicious calculations, 5 cases of delivery failure, no data transfer errors, and no failure of weekly MLC QA. These 9 suspicious cases were due to the PTV extending to the skin or to heterogeneity correction effects, which would not have been caught using phantom measurement-based QA. The delivery failure was due to the rounding variation of MLC position between the planning system and RV system. Conclusion: A very efficient, yet comprehensive, non-measurement-based patient-specific QA program has been implemented and used clinically for about 18 months with excellent results.« less
  • Purpose: With electronic medical records, patient information for the treatment planning process has become disseminated across multiple applications with limited quality control and many associated failure modes. We present the development of a single application with a centralized database to manage the planning process. Methods: The system was designed to replace current functionalities of (i) static directives representing the physician intent for the prescription and planning goals, localization information for delivery, and other information, (ii) planning objective reports, (iii) localization and image guidance documents and (iv) the official radiation therapy prescription in the medical record. Using the Eclipse Scripting Applicationmore » Programming Interface, a plug-in script with an associated domain-specific SQL Server database was created to manage the information in (i)–(iv). The system’s user interface and database were designed by a team of physicians, clinical physicists, database experts, and software engineers to ensure usability and robustness for clinical use. Results: The resulting system has been fully integrated within the TPS via a custom script and database. Planning scenario templates, version control, approvals, and logic-based quality control allow this system to fully track and document the planning process as well as physician approval of tradeoffs while improving the consistency of the data. Multiple plans and prescriptions are supported along with non-traditional dose objectives and evaluation such as biologically corrected models, composite dose limits, and management of localization goals. User-specific custom views were developed for the attending physician review, physicist plan checks, treating therapists, and peer review in chart rounds. Conclusion: A method was developed to maintain cohesive information throughout the planning process within one integrated system by using a custom treatment planning management application that interfaces directly with the TPS. Future work includes quantifying the improvements in quality, safety and efficiency that are possible with the routine clinical use of this system. Supported in part by NIH-P01-CA-059827.« less
  • Purpose: To investigate inter-fraction differences of dose delivery by analyzing portal images acquired during treatment and implement an automated system to generate a report for each fraction. Large differences in images between fractions can alert the physicist of possible machine performance issues or patient set-up errors. Methods: A Varian Novalis Tx equipped with a HD120 MLC and aS1000 electronic portal imaging device (EPID) was used in our study. EPID images are acquired in continuous acquisition mode for 32 volumetric arc therapy (VMAT) patients. The images are summed to create an image for each arc and a single image for eachmore » fraction. The first fraction is designated as the reference unless a machine error prevented acquisition of all images. The images for each beam as well as the fraction image are compared using gamma analysis at 1%/1mm, 2%/2mm and 3%/3mm. A report is then generated using an in house MatLab program containing the comparison for the current fraction as well as a history of previous fractions. The reports are automatically sent via email to the physicist for review. Fractions in which the total number of images was not within 5% of the reference number of images were not included in the results. Results: 91 of the 182 fractions recorded an image count within 5% of the reference. Gamma averages over all fractions and patients were 96.2% ±0.8% at 3%/3mm, 92.9% ±1% at 2%/2mm and 80.6% ±1.8% at 1%/1mm. The SD between fractions for each patient ranged from .004% to 10.4%. Of the 91 fractions 3 flagged due to low gamma values. After further investigation no significant errors were found. Conclusion: This toolkit can be used for in-vivo monitoring of treatment plan delivery an alert the physics staff of any inter-fraction discrepancies that may require further investigation.« less