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Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations

PURPOSE: This study proposes a method to identify plans that failed patient-specific quality assurance (QA) and attempts to establish a criterion to prescreen treatment plans for patient-specific QA measurements with independent Monte Carlo dose calculations. MATERIALS AND METHODS: Patient-specific...

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Autores principales: Xu, Yuan, Zhang, Ke, Liu, Zhiqiang, Liang, Bin, Ma, Xiangyu, Ren, Wenting, Men, Kuo, Dai, Jianrong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676489/
https://www.ncbi.nlm.nih.gov/pubmed/36419878
http://dx.doi.org/10.3389/fonc.2022.1051110
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author Xu, Yuan
Zhang, Ke
Liu, Zhiqiang
Liang, Bin
Ma, Xiangyu
Ren, Wenting
Men, Kuo
Dai, Jianrong
author_facet Xu, Yuan
Zhang, Ke
Liu, Zhiqiang
Liang, Bin
Ma, Xiangyu
Ren, Wenting
Men, Kuo
Dai, Jianrong
author_sort Xu, Yuan
collection PubMed
description PURPOSE: This study proposes a method to identify plans that failed patient-specific quality assurance (QA) and attempts to establish a criterion to prescreen treatment plans for patient-specific QA measurements with independent Monte Carlo dose calculations. MATERIALS AND METHODS: Patient-specific QA results measured with an ArcCHECK diode array of 207 patients (head and neck: 25; thorax: 61; abdomen: 121) were retrospectively analyzed. All patients were treated with the volumetric modulated arc therapy (VMAT) technique and plans were optimized with a Pinnacle v16.2 treatment planning system using an analytical algorithm-based dose engine. Afterwards, phantom verification plans were designed and recalculated by an independent GPU-accelerated Monte Carlo (MC) dose engine, ArcherQA. Moreover, sensitivity and specificity analyzes of gamma passing rates between measurements and MC calculations were carried out to show the ability of MC to monitor failing plans (ArcCHECK 3%/3 mm,<90%), and attempt to determine the appropriate threshold and gamma passing rate criterion utilized by ArcherQA to prescreen treatment plans for ArcCHECK measurements. The receiver operator characteristic (ROC) curve was also utilized to characterize the performance of different gamma passing rate criterion used by ArcherQA. RESULTS: The thresholds for 100% sensitivity to detect plans that failed patient-specific QA by independent calculation were 97.0%, 95.4%, and 91.0% for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively, which corresponded to specificities of 0.720, 0.528, and 0.585, respectively. It was shown that the 3%/3 mm criterion with 97% threshold for ArcherQA demonstrated perfect sensitivity and the highest specificity compared with other criteria, which may be suitable for prescreening treatment plans treated with the investigated machine to implement measurement-based patient-specific QA of patient plans. In addition, the area under the curve (AUC) calculated from ROC analysis for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm used by ArcherQA were 0.948, 0.924, and 0.929, respectively. CONCLUSIONS: Independent dose calculation with the MC-based program ArcherQA has potential as a prescreen treatment for measurement-based patient-specific QA. AUC values (>0.9) showed excellent classification accuracy for monitoring failing plans with independent MC calculations.
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spelling pubmed-96764892022-11-22 Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations Xu, Yuan Zhang, Ke Liu, Zhiqiang Liang, Bin Ma, Xiangyu Ren, Wenting Men, Kuo Dai, Jianrong Front Oncol Oncology PURPOSE: This study proposes a method to identify plans that failed patient-specific quality assurance (QA) and attempts to establish a criterion to prescreen treatment plans for patient-specific QA measurements with independent Monte Carlo dose calculations. MATERIALS AND METHODS: Patient-specific QA results measured with an ArcCHECK diode array of 207 patients (head and neck: 25; thorax: 61; abdomen: 121) were retrospectively analyzed. All patients were treated with the volumetric modulated arc therapy (VMAT) technique and plans were optimized with a Pinnacle v16.2 treatment planning system using an analytical algorithm-based dose engine. Afterwards, phantom verification plans were designed and recalculated by an independent GPU-accelerated Monte Carlo (MC) dose engine, ArcherQA. Moreover, sensitivity and specificity analyzes of gamma passing rates between measurements and MC calculations were carried out to show the ability of MC to monitor failing plans (ArcCHECK 3%/3 mm,<90%), and attempt to determine the appropriate threshold and gamma passing rate criterion utilized by ArcherQA to prescreen treatment plans for ArcCHECK measurements. The receiver operator characteristic (ROC) curve was also utilized to characterize the performance of different gamma passing rate criterion used by ArcherQA. RESULTS: The thresholds for 100% sensitivity to detect plans that failed patient-specific QA by independent calculation were 97.0%, 95.4%, and 91.0% for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively, which corresponded to specificities of 0.720, 0.528, and 0.585, respectively. It was shown that the 3%/3 mm criterion with 97% threshold for ArcherQA demonstrated perfect sensitivity and the highest specificity compared with other criteria, which may be suitable for prescreening treatment plans treated with the investigated machine to implement measurement-based patient-specific QA of patient plans. In addition, the area under the curve (AUC) calculated from ROC analysis for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm used by ArcherQA were 0.948, 0.924, and 0.929, respectively. CONCLUSIONS: Independent dose calculation with the MC-based program ArcherQA has potential as a prescreen treatment for measurement-based patient-specific QA. AUC values (>0.9) showed excellent classification accuracy for monitoring failing plans with independent MC calculations. Frontiers Media S.A. 2022-11-07 /pmc/articles/PMC9676489/ /pubmed/36419878 http://dx.doi.org/10.3389/fonc.2022.1051110 Text en Copyright © 2022 Xu, Zhang, Liu, Liang, Ma, Ren, Men and Dai https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Xu, Yuan
Zhang, Ke
Liu, Zhiqiang
Liang, Bin
Ma, Xiangyu
Ren, Wenting
Men, Kuo
Dai, Jianrong
Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations
title Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations
title_full Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations
title_fullStr Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations
title_full_unstemmed Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations
title_short Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations
title_sort treatment plan prescreening for patient-specific quality assurance measurements using independent monte carlo dose calculations
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676489/
https://www.ncbi.nlm.nih.gov/pubmed/36419878
http://dx.doi.org/10.3389/fonc.2022.1051110
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