Literature DB >> 23819494

Using a novel dose QA tool to quantify the impact of systematic errors otherwise undetected by conventional QA methods: clinical head and neck case studies.

Maria F Chan1, Jingdong Li, Karen Schupak, Chandra Burman.   

Abstract

Recent studies have demonstrated that per-beam planar intensity-modulated radiation therapy (IMRT) quality assurance (QA) passing rates may not predict clinically relevant patient dose errors. This work is to evaluate the effect of dose variations introduced in dynamic multi-leaf collimator (DMLC) modeling and delivery processes on clinically relevant metrics for IMRT. Ten head and neck (HN) IMRT plans were randomly selected for this study. The conventional per-beam IMRT QA was performed for each plan by 2 different methods: (1) with gantry angle of 0 (gantry pointing downward) for all IMRT fields and (2) with gantry at specific angles as designed in the IMRT plan. For each patient, a batch analysis was done for each scenario and then imported to the 3DVH (Sun Nuclear Corp.) for processing. A "corrected DVH" was generated and compared to the DVH from the treatment plan. Their differences represented errors introduced from the combination of the treatment planning system (TPS) dose calculation algorithm and beam-delivery. The dose metrics from the two scenarios were compared with the corresponding calculated doses, and then their differences were analyzed. Although all per-beam planar IMRT QA had high Gamma passing rates 99.3 ± 1.3% (92.3-100%) for "2%/3 mm" criteria, there were significant errors in some of the calculated clinical dose metrics. Such as, for all the plans studied, there were as much as 3.2%, 5.7%, 5.6%, 2.3%, 4.1%, and 23.8% errors found in max cord dose, max brainstem dose, mean parotid dose, larynx dose, oral cavity dose, and PTV(D95) dose, respectively. The differences in errors for clinical metrics obtained between the two scenarios (zero gantry angle vs. true gantry angles) can also be significant: max cord dose (2.9% vs. 0.2%), max brainstem dose (3.8% vs. 0.4%), mean parotid dose (2.3% vs. 4.5%), mean larynx dose (3.9% vs. 2.0%), mean oral cavity dose (1.6% vs. 3.9%), and PTV(D95) dose (-0.4% vs. -2.6%). However, in the two scenarios, a strong and clear correlation between the dose differences for each of the organ structures was observed. This study confirms that conventional IMRT QA performance metrics are not predictive of dose errors in PTV and organs-at-risk. The clinically-relevant-dose QA has allowed us to predict the patient dose-volume relationships.

Entities:  

Mesh:

Year:  2013        PMID: 23819494     DOI: 10.7785/tcrt.2012.500353

Source DB:  PubMed          Journal:  Technol Cancer Res Treat        ISSN: 1533-0338


  9 in total

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2.  A method for quantitative evaluations of scanning-proton dose distributions.

Authors:  Bryce C Allred; Jie Shan; Daniel G Robertson; Todd A DeWees; Jiajian Shen; Wei Liu; Joshua B Stoker
Journal:  J Appl Clin Med Phys       Date:  2021-03-29       Impact factor: 2.102

3.  Optimizing the Region for Evaluation of Global Gamma Analysis for Nasopharyngeal Cancer (NPC) Pretreatment IMRT QA by COMPASS: A Retrospective Study.

Authors:  Wenli Lu; Ying Li; Wei Huang; Haixia Cui; Hanyin Zhang; Xin Yi
Journal:  Front Oncol       Date:  2022-06-14       Impact factor: 5.738

4.  Dosimetric verification by using the ArcCHECK system and 3DVH software for various target sizes.

Authors:  Jin Ho Song; Hun-Joo Shin; Chul Seung Kay; Seok Hyun Son
Journal:  PLoS One       Date:  2015-03-25       Impact factor: 3.240

5.  Impact of delivery characteristics on dose delivery accuracy of volumetric modulated arc therapy for different treatment sites.

Authors:  Jiaqi Li; Xile Zhang; Jun Li; Rongtao Jiang; Jing Sui; Maria F Chan; Ruijie Yang
Journal:  J Radiat Res       Date:  2019-10-23       Impact factor: 2.724

6.  Using eclipse scripting to fully automate in-vivo image analysis to improve treatment quality and safety.

Authors:  Ananta Raj Chalise; Casey Bojechko
Journal:  J Appl Clin Med Phys       Date:  2022-03-22       Impact factor: 2.243

7.  Curtailing patient-specific IMRT QA procedures from 2D dose error distribution.

Authors:  Keita Kurosu; Iori Sumida; Hirokazu Mizuno; Yuki Otani; Michio Oda; Fumiaki Isohashi; Yuji Seo; Osamu Suzuki; Kazuhiko Ogawa
Journal:  J Radiat Res       Date:  2015-12-09       Impact factor: 2.724

8.  Accuracy of one algorithm used to modify a planned DVH with data from actual dose delivery.

Authors:  Tianjun Ma; Matthew B Podgorsak; Lalith K Kumaraswamy
Journal:  J Appl Clin Med Phys       Date:  2016-09-08       Impact factor: 2.102

9.  Composite QA for intensity-modulated radiation therapy using individual volume-based 3D gamma indices.

Authors:  Ce Han; Wenliang Yu; Xiaomin Zheng; Yongqiang Zhou; Changfei Gong; Congying Xie; Xiance Jin
Journal:  J Radiat Res       Date:  2018-09-01       Impact factor: 2.724

  9 in total

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