Literature DB >> 18728311

Process control analysis of IMRT QA: implications for clinical trials.

Todd Pawlicki1, Sua Yoo, Laurence E Court, Sharon K McMillan, Roger K Rice, J Donald Russell, John M Pacyniak, Milton K Woo, Parminder S Basran, Arthur L Boyer, Claribel Bonilla.   

Abstract

The purpose of this study is two-fold: first is to investigate the process of IMRT QA using control charts and second is to compare control chart limits to limits calculated using the standard deviation (sigma). Head and neck and prostate IMRT QA cases from seven institutions in both academic and community settings are considered. The percent difference between the point dose measurement in phantom and the corresponding result from the treatment planning system (TPS) is used for analysis. The average of the percent difference calculations defines the accuracy of the process and is called the process target. This represents the degree to which the process meets the clinical goal of 0% difference between the measurements and TPS. IMRT QA process ability defines the ability of the process to meet clinical specifications (e.g. 5% difference between the measurement and TPS). The process ability is defined in two ways: (1) the half-width of the control chart limits, and (2) the half-width of +/-3sigma limits. Process performance is characterized as being in one of four possible states that describes the stability of the process and its ability to meet clinical specifications. For the head and neck cases, the average process target across institutions was 0.3% (range: -1.5% to 2.9%). The average process ability using control chart limits was 7.2% (range: 5.3% to 9.8%) compared to 6.7% (range: 5.3% to 8.2%) using standard deviation limits. For the prostate cases, the average process target across the institutions was 0.2% (range: -1.8% to 1.4%). The average process ability using control chart limits was 4.4% (range: 1.3% to 9.4%) compared to 5.3% (range: 2.3% to 9.8%) using standard deviation limits. Using the standard deviation to characterize IMRT QA process performance resulted in processes being preferentially placed in one of the four states. This is in contrast to using control charts for process characterization where the IMRT QA processes were spread over three of the four states with none of the processes in the ideal state. Control charts may be used for IMRT QA in clinical trials to categorize process performance, minimize protocol variation and guide process improvements. For the duration of an institution's participation in a protocol, updated control charts can be periodically sent to the protocol QA center to document continued process performance to protocol specifications.

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Year:  2008        PMID: 18728311     DOI: 10.1088/0031-9155/53/18/023

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  9 in total

1.  Development of a software for quantitative evaluation radiotherapy target and organ-at-risk segmentation comparison.

Authors:  Jayashree Kalpathy-Cramer; Musaddiq Awan; Steven Bedrick; Coen R N Rasch; David I Rosenthal; Clifton D Fuller
Journal:  J Digit Imaging       Date:  2014-02       Impact factor: 4.056

2.  Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors.

Authors:  Benjamin E Nelms; Heming Zhen; Wolfgang A Tomé
Journal:  Med Phys       Date:  2011-02       Impact factor: 4.071

3.  A Risk-Adjusted Control Chart to Evaluate Intensity Modulated Radiation Therapy Plan Quality.

Authors:  Arkajyoti Roy; Dan Cutright; Mahesh Gopalakrishnan; Arthur B Yeh; Bharat B Mittal
Journal:  Adv Radiat Oncol       Date:  2019-12-04

4.  Assessment of Statistical Process Control Based DVH Action Levels for Systematic Multi-Leaf Collimator Errors in Cervical Cancer RapidArc Plans.

Authors:  Hanyin Zhang; Wenli Lu; Haixia Cui; Ying Li; Xin Yi
Journal:  Front Oncol       Date:  2022-05-18       Impact factor: 5.738

5.  Dosimetric validation and clinical implementation of two 3D dose verification systems for quality assurance in volumetric-modulated arc therapy techniques.

Authors:  Francisco Clemente-Gutiérrez; Consuelo Pérez-Vara
Journal:  J Appl Clin Med Phys       Date:  2015-03-08       Impact factor: 2.102

6.  Comprehensive validation of halcyon 2.0 plans and the implementation of patient specific QA with multiple detector platforms.

Authors:  Eric Laugeman; Ana Heermann; Jessica Hilliard; Michael Watts; Marshia Roberson; Robert Morris; Sreekrishna Goddu; Abhishek Sethi; Imran Zoberi; Hyun Kim; Sasa Mutic; Geoffrey Hugo; Bin Cai
Journal:  J Appl Clin Med Phys       Date:  2020-05-05       Impact factor: 2.102

7.  Evaluation of Elekta Agility multi-leaf collimator performance using statistical process control tools.

Authors:  Sandra M Meyers; Michael J Balderson; Daniel Létourneau
Journal:  J Appl Clin Med Phys       Date:  2019-06-14       Impact factor: 2.102

8.  Determination of machine-specific tolerances using statistical process control analysis of long-term uniform scanning proton machine QA results.

Authors:  Suresh Rana; Colton Eckert; Hardev Singh; Yuanshui Zheng; Michael Chacko; Mark Storey; John Chang
Journal:  J Appl Clin Med Phys       Date:  2020-08-01       Impact factor: 2.102

9.  Operational consistency of medical linear accelerators manufactured and commissioned in series.

Authors:  Callistus M Nguyen; Alan H Baydush; James D Ververs; Scott Isom; Charles M Able; Michael T Munley
Journal:  Tech Innov Patient Support Radiat Oncol       Date:  2018-05-29
  9 in total

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