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Title: TU-FG-201-03: Automatic Pre-Delivery Verification Using Statistical Analysis of Consistencies in Treatment Plan Parameters by the Treatment Site and Modality

Abstract

Purpose: A novel computer software system, namely APDV (Automatic Pre-Delivery Verification), has been developed for verifying patient treatment plan parameters right prior to treatment deliveries in order to automatically detect and prevent catastrophic errors. Methods: APDV is designed to continuously monitor new DICOM plan files on the TMS computer at the treatment console. When new plans to be delivered are detected, APDV checks the consistencies of plan parameters and high-level plan statistics using underlying rules and statistical properties based on given treatment site, technique and modality. These rules were quantitatively derived by retrospectively analyzing all the EBRT treatment plans of the past 8 years at authors’ institution. Therapists and physicists will be notified with a warning message displayed on the TMS computer if any critical errors are detected, and check results, confirmation, together with dismissal actions will be saved into database for further review. Results: APDV was implemented as a stand-alone program using C# to ensure required real time performance. Mean values and standard deviations were quantitatively derived for various plan parameters including MLC usage, MU/cGy radio, beam SSD, beam weighting, and the beam gantry angles (only for lateral targets) per treatment site, technique and modality. 2D-based rules of combinedmore » MU/cGy ratio and averaged SSD values were also derived using joint probabilities of confidence error ellipses. The statistics of these major treatment plan parameters quantitatively evaluate the consistency of any treatment plans which facilitates automatic APDV checking procedures. Conclusion: APDV could be useful in detecting and preventing catastrophic errors immediately before treatment deliveries. Future plan including automatic patient identify and patient setup checks after patient daily images are acquired by the machine and become available on the TMS computer. This project is supported by the Agency for Healthcare Research and Quality (AHRQ) under award 1R01HS0222888. The senior author received research grants from ViewRay Inc. and Varian Medical System.« less

Authors:
; ; ; ;  [1]
  1. Washington University School of Medicine, St. Louis, MO (United States)
Publication Date:
OSTI Identifier:
22653985
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; BEAMS; COMPUTER CODES; ERRORS; PATIENTS; STATISTICS; VERIFICATION

Citation Formats

Liu, S, Wu, Y, Chang, X, Li, H, and Yang, D. TU-FG-201-03: Automatic Pre-Delivery Verification Using Statistical Analysis of Consistencies in Treatment Plan Parameters by the Treatment Site and Modality. United States: N. p., 2016. Web. doi:10.1118/1.4957526.
Liu, S, Wu, Y, Chang, X, Li, H, & Yang, D. TU-FG-201-03: Automatic Pre-Delivery Verification Using Statistical Analysis of Consistencies in Treatment Plan Parameters by the Treatment Site and Modality. United States. doi:10.1118/1.4957526.
Liu, S, Wu, Y, Chang, X, Li, H, and Yang, D. 2016. "TU-FG-201-03: Automatic Pre-Delivery Verification Using Statistical Analysis of Consistencies in Treatment Plan Parameters by the Treatment Site and Modality". United States. doi:10.1118/1.4957526.
@article{osti_22653985,
title = {TU-FG-201-03: Automatic Pre-Delivery Verification Using Statistical Analysis of Consistencies in Treatment Plan Parameters by the Treatment Site and Modality},
author = {Liu, S and Wu, Y and Chang, X and Li, H and Yang, D},
abstractNote = {Purpose: A novel computer software system, namely APDV (Automatic Pre-Delivery Verification), has been developed for verifying patient treatment plan parameters right prior to treatment deliveries in order to automatically detect and prevent catastrophic errors. Methods: APDV is designed to continuously monitor new DICOM plan files on the TMS computer at the treatment console. When new plans to be delivered are detected, APDV checks the consistencies of plan parameters and high-level plan statistics using underlying rules and statistical properties based on given treatment site, technique and modality. These rules were quantitatively derived by retrospectively analyzing all the EBRT treatment plans of the past 8 years at authors’ institution. Therapists and physicists will be notified with a warning message displayed on the TMS computer if any critical errors are detected, and check results, confirmation, together with dismissal actions will be saved into database for further review. Results: APDV was implemented as a stand-alone program using C# to ensure required real time performance. Mean values and standard deviations were quantitatively derived for various plan parameters including MLC usage, MU/cGy radio, beam SSD, beam weighting, and the beam gantry angles (only for lateral targets) per treatment site, technique and modality. 2D-based rules of combined MU/cGy ratio and averaged SSD values were also derived using joint probabilities of confidence error ellipses. The statistics of these major treatment plan parameters quantitatively evaluate the consistency of any treatment plans which facilitates automatic APDV checking procedures. Conclusion: APDV could be useful in detecting and preventing catastrophic errors immediately before treatment deliveries. Future plan including automatic patient identify and patient setup checks after patient daily images are acquired by the machine and become available on the TMS computer. This project is supported by the Agency for Healthcare Research and Quality (AHRQ) under award 1R01HS0222888. The senior author received research grants from ViewRay Inc. and Varian Medical System.},
doi = {10.1118/1.4957526},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: Adaptive radiation therapy for prostate cancer using online reoptimization provides an improved control of interfractional anatomy variations. However, the clinical implementation of online reoptimization is currently limited by the low efficiency of current strategies and the difficulties associated with integration into the current treatment planning system. This study investigates the strategies for performing fast (∼2 min) automatic online reoptimization with a clinical fluence-map-based treatment planning system; and explores the performance with different input parameters settings: dose-volume histogram (DVH) objective settings, starting stage, and iteration number (in the context of real time planning).Methods: Simulated treatments of 10 patients were reoptimizedmore » daily for the first week of treatment (5 fractions) using 12 different combinations of optimization strategies. Options for objective settings included guideline-based RTOG objectives, patient-specific objectives based on anatomy on the planning CT, and daily-CBCT anatomy-based objectives adapted from planning CT objectives. Options for starting stages involved starting reoptimization with and without the original plan's fluence map. Options for iteration numbers were 50 and 100. The adapted plans were then analyzed by statistical modeling, and compared both in terms of dosimetry and delivery efficiency.Results: All online reoptimized plans were finished within ∼2 min with excellent coverage and conformity to the daily target. The three input parameters, i.e., DVH objectives, starting stage, and iteration number, contributed to the outcome of optimization nearly independently. Patient-specific objectives generally provided better OAR sparing compared to guideline-based objectives. The benefit in high-dose sparing from incorporating daily anatomy into objective settings was positively correlated with the relative change in OAR volumes from planning CT to daily CBCT. The use of the original plan fluence map as the starting stage reduced OAR dose at the mid-dose region, but increased the monitor units by 17%. Differences of only 2cc or less in OAR V50%/V70Gy/V76Gy were observed between 100 and 50 iterations.Conclusions: It is feasible to perform automatic online reoptimization in ∼2 min using a clinical treatment planning system. Selecting optimal sets of input parameters is the key to achieving high quality reoptimized plans, and should be based on the individual patient's daily anatomy, delivery efficiency, and time allowed for plan adaptation.« less
  • Purpose: Statistical process control (SPC) is a quality control method used to ensure that a process is well controlled and operates with little variation. This study determined whether SPC was a viable technique for evaluating the proper operation of a high-dose-rate (HDR) brachytherapy treatment delivery system. Methods and Materials: A surrogate prostate patient was developed using Vyse ordnance gelatin. A total of 10 metal oxide semiconductor field-effect transistors (MOSFETs) were placed from prostate base to apex. Computed tomography guidance was used to accurately position the first detector in each train at the base. The plan consisted of 12 needles withmore » 129 dwell positions delivering a prescribed peripheral dose of 200 cGy. Sixteen accurate treatment trials were delivered as planned. Subsequently, a number of treatments were delivered with errors introduced, including wrong patient, wrong source calibration, wrong connection sequence, single needle displaced inferiorly 5 mm, and entire implant displaced 2 mm and 4 mm inferiorly. Two process behavior charts (PBC), an individual and a moving range chart, were developed for each dosimeter location. Results: There were 4 false positives resulting from 160 measurements from 16 accurately delivered treatments. For the inaccurately delivered treatments, the PBC indicated that measurements made at the periphery and apex (regions of high-dose gradient) were much more sensitive to treatment delivery errors. All errors introduced were correctly identified by either the individual or the moving range PBC in the apex region. Measurements at the urethra and base were less sensitive to errors. Conclusions: SPC is a viable method for assessing the quality of HDR treatment delivery. Further development is necessary to determine the most effective dose sampling, to ensure reproducible evaluation of treatment delivery accuracy.« less
  • Purpose: Mobius3D/MobiusFX (M3D/MFX), a commercial DICOM-RT based plan and delivery verification system, was used to compare calculated and delivered volumetric modulated arc therapy (VMAT) dose distributions using TrueBeam delivery log files (TrajectoryLogs). Methods: M3D/MFX utilizes measured linac commissioning data to generate institution specific beam models for evaluating planned and delivered dose distributions. 30 RapidArc prostate plans and 30 head and neck SmartArc plans were used in this study. For every plan, CT images, contoured structure sets, RT-plan, and RT-dose files were exported to M3D, which recalculated the patients’ planning CT dose distributions using a collapsed-cone-convolutionsuperposition algorithm. MFX utilized the acquiredmore » TrajectoryLogs to compute patients’ delivered dose distributions based on actual treatment delivery parameters. The agreement between computed and delivered dose distributions was evaluated utilizing a (3%, 3mm) global 3D-gamma analysis and dose-volume histogram changes for targets and organs at risk. Results: Excellent 3D-gamma agreements were observed for all VMAT plans. On average, for computed and delivered RapidArc and SmartArc plans the gamma passing rates were (99.0%±1.4%) and (96.8%±1.8%), respectively. The average difference for primary target prescription dose percent-coverage between calculated and delivered plans was (− 0.09%±2.52%) for RapidArc and (−2.71%±4.62%) for SmartArc cases. Similarly, the planning target mean dose differences were (1.38%±0.96%) for RapidArc and (1.17%±0.72%) for SmartArc plans. For the prostate plans, the calculated and delivered variations of the maximum dose for a 2cc volume for bladder and rectum were (1.32%±1.26%) and (0.65%±1.44%), respectively. The spinal-cord 2cc maximum dose differences of (3.26%±1.68%) were observed for the SmartArc plans. Conclusions: Clinical quality assurance practice based on linac treatment log files for verification of delivered 3D dose distributions in the patients’ geometries represents a paradigm shift from dose measurements in a phantom. This approach captures treatment planning beam modeling differences as well as the linac uncertainties during treatment delivery of the plan.« less
  • Purpose: Verification of high dose rate (HDR) brachytherapy treatment delivery is an important step, but is generally difficult to achieve. A technique is required to monitor the treatment as it is delivered, allowing comparison with the treatment plan and error detection. In this work, we demonstrate a method for monitoring the treatment as it is delivered and directly comparing the delivered treatment with the treatment plan in the clinical workspace. This treatment verification system is based on a flat panel detector (FPD) used for both pre-treatment imaging and source tracking. Methods: A phantom study was conducted to establish the resolutionmore » and precision of the system. A pretreatment radiograph of a phantom containing brachytherapy catheters is acquired and registration between the measurement and treatment planning system (TPS) is performed using implanted fiducial markers. The measured catheter paths immediately prior to treatment were then compared with the plan. During treatment delivery, the position of the {sup 192}Ir source is determined at each dwell position by measuring the exit radiation with the FPD and directly compared to the planned source dwell positions. Results: The registration between the two corresponding sets of fiducial markers in the TPS and radiograph yielded a registration error (residual) of 1.0 mm. The measured catheter paths agreed with the planned catheter paths on average to within 0.5 mm. The source positions measured with the FPD matched the planned source positions for all dwells on average within 0.6 mm (s.d. 0.3, min. 0.1, max. 1.4 mm). Conclusions: We have demonstrated a method for directly comparing the treatment plan with the delivered treatment that can be easily implemented in the clinical workspace. Pretreatment imaging was performed, enabling visualization of the implant before treatment delivery and identification of possible catheter displacement. Treatment delivery verification was performed by measuring the source position as each dwell was delivered. This approach using a FPD for imaging and source tracking provides a noninvasive method of acquiring extensive information for verification in HDR prostate brachytherapy.« less
  • Purpose: A novel optimization technique was developed for field-in-field (FIF) chestwall radiotherapy using bolus every other day. The dosimetry was compared to currently used optimization. Methods: The prior five patients treated at our clinic to the chestwall and supraclavicular nodes with a mono-isocentric four-field arrangement were selected for this study. The prescription was 5040 cGy in 28 fractions, 5 mm bolus every other day on the tangent fields, 6 and/or 10 MV x-rays, and multileaf collimation.Novelly, tangents FIF segments were forward planned optimized based on the composite bolus and non-bolus dose distribution simultaneously. The prescription was spilt into 14 fractionsmore » for both bolus and non-bolus tangents. The same segments and monitor units were used for the bolus and non-bolus treatment. The plan was optimized until the desired coverage was achieved, minimized 105% hotspots, and a maximum dose of less than 108%. Each tangential field had less than 5 segments.Comparison plans were generated using FIF optimization with the same dosimetric goals, but using only the non-bolus calculation for FIF optimization. The non-bolus fields were then copied and bolus was applied. The same segments and monitor units were used for the bolus and non-bolus segments. Results: The prescription coverage of the chestwall, as defined by RTOG guidelines, was on average 51.8% for the plans that optimized bolus and non-bolus treatments simultaneous (SB) and 43.8% for the plans optimized to the non-bolus treatments (NB). Chestwall coverage of 90% prescription averaged to 80.4% for SB and 79.6% for NB plans. The volume receiving 105% of the prescription was 1.9% for SB and 0.8% for NB plans on average. Conclusion: Simultaneously optimizing for bolus and non-bolus treatments noticeably improves prescription coverage of the chestwall while maintaining similar hotspots and 90% prescription coverage in comparison to optimizing only to non-bolus treatments.« less