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Title: SU-D-BRC-02: Application of Six Sigma Approach to Improve the Efficiency of Patient-Specific QA in Proton Therapy

Abstract

Purpose: To show how the Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) can be used for improving and optimizing the efficiency of patient-specific QA process by designing site-specific range tolerances. Methods: The Six Sigma tools (process flow diagram, cause and effect, capability analysis, Pareto chart, and control chart) were utilized to determine the steps that need focus for improving the patient-specific QA process. The patient-specific range QA plans were selected according to 7 treatment site groups, a total of 1437 cases. The process capability index, Cpm was used to guide the tolerance design of patient site-specific range. We also analyzed the financial impact of this project. Results: Our results suggested that the patient range measurements were non-capable at the current tolerance level of ±1 mm in clinical proton plans. The optimized tolerances were calculated for treatment sites. Control charts for the patient QA time were constructed to compare QA time before and after the new tolerances were implemented. It is found that overall processing time was decreased by 24.3% after establishing new site-specific range tolerances. The QA failure for whole process in proton therapy would lead up to a 46% increase in total cost. This result can also predict how costs are affectedmore » by changes in adopting the tolerance design. Conclusion: We often believe that the quality and performance of proton therapy can easily be improved by merely tightening some or all of its tolerance requirements. This can become costly, however, and it is not necessarily a guarantee of better performance. The tolerance design is not a task to be undertaken without careful thought. The Six Sigma DMAIC can be used to improve the QA process by setting optimized tolerances. When tolerance design is optimized, the quality is reasonably balanced with time and cost demands.« less

Authors:
 [1];  [2]; ;  [3]
  1. Myongji Hospital, Goyang-si (Korea, Republic of)
  2. Proton Therapy Center, National Cancer Center, Goyang (Korea, Republic of)
  3. University of California, San Diego, La Jolla, CA (United States)
Publication Date:
OSTI Identifier:
22624373
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; FAILURES; OPTIMIZATION; PATIENTS; PERFORMANCE; PERT METHOD; PROTON BEAMS; RADIOTHERAPY; TOLERANCE

Citation Formats

LAH, J, Shin, D, Manger, R, and Kim, G. SU-D-BRC-02: Application of Six Sigma Approach to Improve the Efficiency of Patient-Specific QA in Proton Therapy. United States: N. p., 2016. Web. doi:10.1118/1.4955621.
LAH, J, Shin, D, Manger, R, & Kim, G. SU-D-BRC-02: Application of Six Sigma Approach to Improve the Efficiency of Patient-Specific QA in Proton Therapy. United States. doi:10.1118/1.4955621.
LAH, J, Shin, D, Manger, R, and Kim, G. 2016. "SU-D-BRC-02: Application of Six Sigma Approach to Improve the Efficiency of Patient-Specific QA in Proton Therapy". United States. doi:10.1118/1.4955621.
@article{osti_22624373,
title = {SU-D-BRC-02: Application of Six Sigma Approach to Improve the Efficiency of Patient-Specific QA in Proton Therapy},
author = {LAH, J and Shin, D and Manger, R and Kim, G},
abstractNote = {Purpose: To show how the Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) can be used for improving and optimizing the efficiency of patient-specific QA process by designing site-specific range tolerances. Methods: The Six Sigma tools (process flow diagram, cause and effect, capability analysis, Pareto chart, and control chart) were utilized to determine the steps that need focus for improving the patient-specific QA process. The patient-specific range QA plans were selected according to 7 treatment site groups, a total of 1437 cases. The process capability index, Cpm was used to guide the tolerance design of patient site-specific range. We also analyzed the financial impact of this project. Results: Our results suggested that the patient range measurements were non-capable at the current tolerance level of ±1 mm in clinical proton plans. The optimized tolerances were calculated for treatment sites. Control charts for the patient QA time were constructed to compare QA time before and after the new tolerances were implemented. It is found that overall processing time was decreased by 24.3% after establishing new site-specific range tolerances. The QA failure for whole process in proton therapy would lead up to a 46% increase in total cost. This result can also predict how costs are affected by changes in adopting the tolerance design. Conclusion: We often believe that the quality and performance of proton therapy can easily be improved by merely tightening some or all of its tolerance requirements. This can become costly, however, and it is not necessarily a guarantee of better performance. The tolerance design is not a task to be undertaken without careful thought. The Six Sigma DMAIC can be used to improve the QA process by setting optimized tolerances. When tolerance design is optimized, the quality is reasonably balanced with time and cost demands.},
doi = {10.1118/1.4955621},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: We present an improved method to calculate patient-specific calibration curves to convert X-ray computed tomography (CT) Hounsfield Unit (HU) to relative stopping powers (RSP) for proton therapy treatment planning. Methods: By optimizing the HU-RSP calibration curve, the difference between a proton radiographic image and a digitally reconstructed X-ray radiography (DRR) is minimized. The feasibility of this approach has previously been demonstrated. This scenario assumes that all discrepancies between proton radiography and DRR originate from uncertainties in the HU-RSP curve. In reality, external factors cause imperfections in the proton radiography, such as misalignment compared to the DRR and unfaithful representationmore » of geometric structures (“blurring”). We analyze these effects based on synthetic datasets of anthropomorphic phantoms and suggest an extended optimization scheme which explicitly accounts for these effects. Performance of the method is been tested for various simulated irradiation parameters. The ultimate purpose of the optimization is to minimize uncertainties in the HU-RSP calibration curve. We therefore suggest and perform a thorough statistical treatment to quantify the accuracy of the optimized HU-RSP curve. Results: We demonstrate that without extending the optimization scheme, spatial blurring (equivalent to FWHM=3mm convolution) in the proton radiographies can cause up to 10% deviation between the optimized and the ground truth HU-RSP calibration curve. Instead, results obtained with our extended method reach 1% or better correspondence. We have further calculated gamma index maps for different acceptance levels. With DTA=0.5mm and RD=0.5%, a passing ratio of 100% is obtained with the extended method, while an optimization neglecting effects of spatial blurring only reach ∼90%. Conclusion: Our contribution underlines the potential of a single proton radiography to generate a patient-specific calibration curve and to improve dose delivery by optimizing the HU-RSP calibration curve as long as all sources of systematic incongruence are properly modeled.« less
  • Purpose: The conversion from Hounsfield units (HU) to stopping powers is a major source of range uncertainty in proton therapy (PT). Our contribution shows how proton radiographs (PR) acquired with a multi-layer ionization chamber in a PT center can be used for accurate patient positioning and subsequently for patient-specific optimization of the conversion from HU to stopping powers. Methods: A multi-layer ionization chamber was used to measure the integral depth-dose (IDD) of 220 MeV pencil beam spots passing through several anthropomorphic phantoms. The whole area of interest was imaged by repositioning the couch and by acquiring a 45×45 mm{sup 2}more » frame for each position. A rigid registration algorithm was implemented to correct the positioning error between the proton radiographs and the planning CT. After registration, the stopping power map obtained from the planning CT with the calibration curve of the treatment planning system was used together with the water equivalent thickness gained from two proton radiographs to generate a phantom-specific stopping power map. Results: Our results show that it is possible to make a registration with submillimeter accuracy from proton radiography obtained by sending beamlets separated by more than 1 mm. This was made possible by the complex shape of the IDD due to the presence of lateral heterogeneities along the path of the beam. Submillimeter positioning was still possible with a 5 mm spot spacing. Phantom specific stopping power maps obtained by minimizing the range error were cross-verified by the acquisition of an additional proton radiography where the phantom was positioned in a random but known manner. Conclusion: Our results indicate that a CT-PR registration algorithm together with range-error based optimization can be used to produce a patient-specific stopping power map. Sylvain Deffet reports financial funding of its PhD thesis by Ion Beam Applications (IBA) during the confines of the study and outside the submitted work. Francois Vander Stappen reports being employed by Ion Beam Applications (IBA) during the confines of the study and outside the submitted work.« less
  • Purpose: To show how tolerance design and tolerancing approaches can be used to predict and improve the site-specific range in patient QA process in implementing the Six Sigma. Methods: In this study, patient QA plans were selected according to 6 site-treatment groups: head &neck (94 cases), spine (76 cases), lung (89 cases), liver (53 cases), pancreas (55 cases), and prostate (121 cases), treated between 2007 and 2013. We evaluated a model of the Six Sigma that determines allowable deviations in design parameters and process variables in patient-specific QA, where possible, tolerance may be loosened, then customized if it necessary tomore » meet the functional requirements. A Six Sigma problem-solving methodology is known as DMAIC phases, which are used stand for: Define a problem or improvement opportunity, Measure process performance, Analyze the process to determine the root causes of poor performance, Improve the process by fixing root causes, Control the improved process to hold the gains. Results: The process capability for patient-specific range QA is 0.65 with only ±1 mm of tolerance criteria. Our results suggested the tolerance level of ±2–3 mm for prostate and liver cases and ±5 mm for lung cases. We found that customized tolerance between calculated and measured range reduce that patient QA plan failure and almost all sites had failure rates less than 1%. The average QA time also improved from 2 hr to less than 1 hr for all including planning and converting process, depth-dose measurement and evaluation. Conclusion: The objective of tolerance design is to achieve optimization beyond that obtained through QA process improvement and statistical analysis function detailing to implement a Six Sigma capable design.« less
  • Through an internally funded project at Oak Ridge National Laboratory, a high-resolution phantom was developed based on the National Library of Medicine`s Visible Human Data. Special software was written using the interactive data language (IDL) visualization language to automatically segment and classify some of the organs and the skeleton of the Visible Male. A high definition phantom consisting of nine hundred 512 x 512 slices was constructed of the entire torso. Computed tomography (CT) images of a patient`s tumor near the spine were scaled and morphed into the phantom model to create a patient-specific phantom. Calculations of dose to themore » tumor and surrounding tissue were then performed using the patient-specific phantom.« less
  • Purpose: To describe our experiences with patient-specific quality assurance (QA) for patients with prostate cancer receiving spot scanning proton therapy (SSPT) using single-field uniform dose (SFUD). Methods and Materials: The first group of 249 patients with prostate cancer treated with SSPT using SFUD was included in this work. The scanning-beam planning target volume and number of monitor units were recorded and checked for consistency. Patient-specific dosimetric measurements were performed, including the point dose for each plan, depth doses, and two-dimensional (2D) dose distribution in the planes perpendicular to the incident beam direction for each field at multiple depths. The {gamma}-indexmore » with 3% dose or 3-mm distance agreement criteria was used to evaluate the 2D dose distributions. Results: We observed a linear relationship between the number of monitor units and scanning-beam planning target volume. The difference between the measured and calculated point doses (mean {+-} SD) was 0.0% {+-} 0.7% (range, -2.9% to 1.8%). In general, the depth doses exhibited good agreement except at the distal end of the spread-out Bragg peak. The pass rate of {gamma}-index (mean {+-} SD) for 2D dose comparison was 96.2% {+-} 2.6% (range, 90-100%). Discrepancies between the measured and calculated dose distributions primarily resulted from the limitation of the model used by the treatment planning system. Conclusions: We have established a patient-specific QA program for prostate cancer patients receiving SSPT using SFUD.« less