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Application programming interface guided QA plan generation and analysis automation

PURPOSE: Linear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standardized QA plan generation and analysis system in t...

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Autores principales: Schmidt, Matthew C., Raman, Caleb A., Wu, Yu, Yaqoub, Mahmoud M., Hao, Yao, Mahon, Rebecca Nichole, Riblett, Matthew J., Knutson, Nels C., Sajo, Erno, Zygmanski, Piotr, Jandel, Marian, Reynoso, Francisco J., Sun, Baozhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200500/
https://www.ncbi.nlm.nih.gov/pubmed/34036736
http://dx.doi.org/10.1002/acm2.13288
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author Schmidt, Matthew C.
Raman, Caleb A.
Wu, Yu
Yaqoub, Mahmoud M.
Hao, Yao
Mahon, Rebecca Nichole
Riblett, Matthew J.
Knutson, Nels C.
Sajo, Erno
Zygmanski, Piotr
Jandel, Marian
Reynoso, Francisco J.
Sun, Baozhou
author_facet Schmidt, Matthew C.
Raman, Caleb A.
Wu, Yu
Yaqoub, Mahmoud M.
Hao, Yao
Mahon, Rebecca Nichole
Riblett, Matthew J.
Knutson, Nels C.
Sajo, Erno
Zygmanski, Piotr
Jandel, Marian
Reynoso, Francisco J.
Sun, Baozhou
author_sort Schmidt, Matthew C.
collection PubMed
description PURPOSE: Linear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standardized QA plan generation and analysis system in the Oncology Information System (OIS) to streamline the QA process. METHODS: Automating the QA process includes two software components: the AutoQA Builder to generate daily, monthly, quarterly, and miscellaneous periodic linear accelerator QA plans within the Treatment Planning System (TPS) and the AutoQA Analysis to analyze images collected on the Electronic Portal Imaging Device (EPID) allowing for a rapid analysis of the acquired QA images. To verify the results of the automated QA analysis, results were compared to the current standard for QA assessment for the jaw junction, light‐radiation coincidence, picket fence, and volumetric modulated arc therapy (VMAT) QA plans across three linacs and over a 6‐month period. RESULTS: The AutoQA Builder application has been utilized clinically 322 times to create QA patients, construct phantom images, and deploy common periodic QA tests across multiple institutions, linear accelerators, and physicists. Comparing the AutoQA Analysis results with our current institutional QA standard the mean difference of the ratio of intensity values within the field‐matched junction and ball‐bearing position detection was 0.012 ± 0.053 (P = 0.159) and is 0.011 ± 0.224 mm (P = 0.355), respectively. Analysis of VMAT QA plans resulted in a maximum percentage difference of 0.3%. CONCLUSION: The automated creation and analysis of quality assurance plans using multiple APIs can be of immediate benefit to linear accelerator quality assurance efficiency and standardization. QA plan creation can be done without following tedious procedures through API assistance, and analysis can be performed inside of the clinical OIS in an automated fashion.
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spelling pubmed-82005002021-06-15 Application programming interface guided QA plan generation and analysis automation Schmidt, Matthew C. Raman, Caleb A. Wu, Yu Yaqoub, Mahmoud M. Hao, Yao Mahon, Rebecca Nichole Riblett, Matthew J. Knutson, Nels C. Sajo, Erno Zygmanski, Piotr Jandel, Marian Reynoso, Francisco J. Sun, Baozhou J Appl Clin Med Phys Radiation Oncology Physics PURPOSE: Linear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standardized QA plan generation and analysis system in the Oncology Information System (OIS) to streamline the QA process. METHODS: Automating the QA process includes two software components: the AutoQA Builder to generate daily, monthly, quarterly, and miscellaneous periodic linear accelerator QA plans within the Treatment Planning System (TPS) and the AutoQA Analysis to analyze images collected on the Electronic Portal Imaging Device (EPID) allowing for a rapid analysis of the acquired QA images. To verify the results of the automated QA analysis, results were compared to the current standard for QA assessment for the jaw junction, light‐radiation coincidence, picket fence, and volumetric modulated arc therapy (VMAT) QA plans across three linacs and over a 6‐month period. RESULTS: The AutoQA Builder application has been utilized clinically 322 times to create QA patients, construct phantom images, and deploy common periodic QA tests across multiple institutions, linear accelerators, and physicists. Comparing the AutoQA Analysis results with our current institutional QA standard the mean difference of the ratio of intensity values within the field‐matched junction and ball‐bearing position detection was 0.012 ± 0.053 (P = 0.159) and is 0.011 ± 0.224 mm (P = 0.355), respectively. Analysis of VMAT QA plans resulted in a maximum percentage difference of 0.3%. CONCLUSION: The automated creation and analysis of quality assurance plans using multiple APIs can be of immediate benefit to linear accelerator quality assurance efficiency and standardization. QA plan creation can be done without following tedious procedures through API assistance, and analysis can be performed inside of the clinical OIS in an automated fashion. John Wiley and Sons Inc. 2021-05-26 /pmc/articles/PMC8200500/ /pubmed/34036736 http://dx.doi.org/10.1002/acm2.13288 Text en © 2021 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Schmidt, Matthew C.
Raman, Caleb A.
Wu, Yu
Yaqoub, Mahmoud M.
Hao, Yao
Mahon, Rebecca Nichole
Riblett, Matthew J.
Knutson, Nels C.
Sajo, Erno
Zygmanski, Piotr
Jandel, Marian
Reynoso, Francisco J.
Sun, Baozhou
Application programming interface guided QA plan generation and analysis automation
title Application programming interface guided QA plan generation and analysis automation
title_full Application programming interface guided QA plan generation and analysis automation
title_fullStr Application programming interface guided QA plan generation and analysis automation
title_full_unstemmed Application programming interface guided QA plan generation and analysis automation
title_short Application programming interface guided QA plan generation and analysis automation
title_sort application programming interface guided qa plan generation and analysis automation
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200500/
https://www.ncbi.nlm.nih.gov/pubmed/34036736
http://dx.doi.org/10.1002/acm2.13288
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