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Statistical process control to monitor use of a web‐based autoplanning tool

PURPOSE: To investigate the use of statistical process control (SPC) for quality assurance of an integrated web‐based autoplanning tool, Radiation Planning Assistant (RPA). METHODS: Automatically generated plans were downloaded and imported into two treatment planning systems (TPSs), RayStation and...

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Autores principales: Mehrens, Hunter, Douglas, Raphael, Gronberg, Mary, Nealon, Kelly, Zhang, Joy, Court, Laurence
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9797174/
https://www.ncbi.nlm.nih.gov/pubmed/36300872
http://dx.doi.org/10.1002/acm2.13803
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author Mehrens, Hunter
Douglas, Raphael
Gronberg, Mary
Nealon, Kelly
Zhang, Joy
Court, Laurence
author_facet Mehrens, Hunter
Douglas, Raphael
Gronberg, Mary
Nealon, Kelly
Zhang, Joy
Court, Laurence
author_sort Mehrens, Hunter
collection PubMed
description PURPOSE: To investigate the use of statistical process control (SPC) for quality assurance of an integrated web‐based autoplanning tool, Radiation Planning Assistant (RPA). METHODS: Automatically generated plans were downloaded and imported into two treatment planning systems (TPSs), RayStation and Eclipse, in which they were recalculated using fixed monitor units. The recalculated plans were then uploaded back to the RPA, and the mean dose differences for each contour between the original RPA and the TPSs plans were calculated. SPC was used to characterize the RPA plans in terms of two comparisons: RayStation TPS versus RPA and Eclipse TPS versus RPA for three anatomical sites, and variations in the machine parameters dosimetric leaf gap (DLG) and multileaf collimator transmission factor (MLC‐TF) for two algorithms (Analytical Anisotropic Algorithm [AAA]) and Acuros in the Eclipse TPS. Overall, SPC was used to monitor the process of the RPA, while clinics would still perform their routine patient‐specific QA. RESULTS: For RayStation, the average mean percent dose differences across all contours were 0.65% ± 1.05%, −2.09% ± 0.56%, and 0.28% ± 0.98% and average control limit ranges were 1.89% ± 1.32%, 2.16% ± 1.31%, and 2.65% ± 1.89% for the head and neck, cervix, and chest wall, respectively. In contrast, Eclipse's average mean percent dose differences across all contours were −0.62% ± 0.34%, 0.32% ± 0.23%, and −0.91% ± 0.98%, while average control limit ranges were 1.09% ± 0.77%, 3.69% ± 2.67%, 2.73% ± 1.86%, respectively. Averaging all contours and removing outliers, a 0% dose difference corresponded with a DLG value of 0.202 ± 0.019 cm and MLC‐TF value of 0.020 ± 0.001 for Acuros and a DLG value of 0.135 ± 0.031 cm and MLC‐TF value of 0.015 ± 0.001 for AAA. CONCLUSIONS: Differences in mean dose and control limits between RPA and two separately commissioned TPSs were determined. With varying control limits and means, SPC provides a flexible and useful process quality assurance tool for monitoring a complex automated system such as the RPA.
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spelling pubmed-97971742022-12-30 Statistical process control to monitor use of a web‐based autoplanning tool Mehrens, Hunter Douglas, Raphael Gronberg, Mary Nealon, Kelly Zhang, Joy Court, Laurence J Appl Clin Med Phys Radiation Oncology Physics PURPOSE: To investigate the use of statistical process control (SPC) for quality assurance of an integrated web‐based autoplanning tool, Radiation Planning Assistant (RPA). METHODS: Automatically generated plans were downloaded and imported into two treatment planning systems (TPSs), RayStation and Eclipse, in which they were recalculated using fixed monitor units. The recalculated plans were then uploaded back to the RPA, and the mean dose differences for each contour between the original RPA and the TPSs plans were calculated. SPC was used to characterize the RPA plans in terms of two comparisons: RayStation TPS versus RPA and Eclipse TPS versus RPA for three anatomical sites, and variations in the machine parameters dosimetric leaf gap (DLG) and multileaf collimator transmission factor (MLC‐TF) for two algorithms (Analytical Anisotropic Algorithm [AAA]) and Acuros in the Eclipse TPS. Overall, SPC was used to monitor the process of the RPA, while clinics would still perform their routine patient‐specific QA. RESULTS: For RayStation, the average mean percent dose differences across all contours were 0.65% ± 1.05%, −2.09% ± 0.56%, and 0.28% ± 0.98% and average control limit ranges were 1.89% ± 1.32%, 2.16% ± 1.31%, and 2.65% ± 1.89% for the head and neck, cervix, and chest wall, respectively. In contrast, Eclipse's average mean percent dose differences across all contours were −0.62% ± 0.34%, 0.32% ± 0.23%, and −0.91% ± 0.98%, while average control limit ranges were 1.09% ± 0.77%, 3.69% ± 2.67%, 2.73% ± 1.86%, respectively. Averaging all contours and removing outliers, a 0% dose difference corresponded with a DLG value of 0.202 ± 0.019 cm and MLC‐TF value of 0.020 ± 0.001 for Acuros and a DLG value of 0.135 ± 0.031 cm and MLC‐TF value of 0.015 ± 0.001 for AAA. CONCLUSIONS: Differences in mean dose and control limits between RPA and two separately commissioned TPSs were determined. With varying control limits and means, SPC provides a flexible and useful process quality assurance tool for monitoring a complex automated system such as the RPA. John Wiley and Sons Inc. 2022-10-27 /pmc/articles/PMC9797174/ /pubmed/36300872 http://dx.doi.org/10.1002/acm2.13803 Text en © 2022 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The 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
Mehrens, Hunter
Douglas, Raphael
Gronberg, Mary
Nealon, Kelly
Zhang, Joy
Court, Laurence
Statistical process control to monitor use of a web‐based autoplanning tool
title Statistical process control to monitor use of a web‐based autoplanning tool
title_full Statistical process control to monitor use of a web‐based autoplanning tool
title_fullStr Statistical process control to monitor use of a web‐based autoplanning tool
title_full_unstemmed Statistical process control to monitor use of a web‐based autoplanning tool
title_short Statistical process control to monitor use of a web‐based autoplanning tool
title_sort statistical process control to monitor use of a web‐based autoplanning tool
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9797174/
https://www.ncbi.nlm.nih.gov/pubmed/36300872
http://dx.doi.org/10.1002/acm2.13803
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