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在使用0.35T磁共振直线加速器进行在线自适应放疗期间,用于治疗质量保证的每日剂量自动累积工作流程。

Automated daily dose accumulation workflow for treatment quality assurance during online adaptive radiotherapy with a 0.35T MR-linac.

作者信息

Behzadipour Mojtaba, Ma Tianjun, Datsang Rabten K, Lee Brandon, Pittock Dane, Weiss Elisabeth, Song William Y

机构信息

Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA.

Clinical Science, MIM Software Inc., Cleveland, Ohio, USA.

出版信息

J Appl Clin Med Phys. 2025 Mar;26(3):e14594. doi: 10.1002/acm2.14594. Epub 2024 Dec 20.

Abstract

PURPOSE

This study assesses a novel, automated dose accumulation process during MR-guided online adaptive radiotherapy (MRgART) for prostate cancer, focusing on inter-fractional anatomical changes and discrepancies between delivered and planned doses.

METHODS

A retrospective analysis was conducted on seven prostate cancer patients treated with a five-fraction stereotactic body radiation therapy (SBRT), using a 0.35T MRIdian MR-LINAC system. Daily plans were adapted when dose thresholds were exceeded. Planning MRI (pMRI) and daily MRIs (dMRIs) were imported into MIM software for automated and manual dose accumulation procedures. Rigid and deformable image registrations were followed by dose accumulation to compare delivered and planned doses. Manual and automated image registrations were compared by calculating the Hausdorff distance (HD), Jaccard, and DICE metrics.

RESULTS

Moderate discrepancies in dosimetric parameters for the planning target volume (PTV) were observed between auto-accumulated and planned doses, such as and , with average differences of  Gy and  Gy, respectively. Volume differences of and indicated that auto-accumulated doses consistently had lower numbers compared to planned doses, with mean discrepancies of and , respectively. Organs at risk (OAR) dosimetric parameters exhibited higher dose volumes in auto-accumulated doses, with moderate differences (planned [cc] vs. auto-accumulated [cc]) observed in parameters such as urethra PRV at , rectum at and rectum at . The comparison between manually and auto-accumulated doses revealed negligible variations, as also indicated by strong concordance in geometric indices and t-test p-values above 0.7.

CONCLUSION

The automated workflow, developed in collaboration with MIM Software Inc., demonstrates high accuracy compared to manual accumulation. The moderate differences observed between planned and accumulated doses emphasize the need for accurate dose accumulation for adaptive plans.

摘要

目的

本研究评估了一种用于前列腺癌的磁共振引导在线自适应放射治疗(MRgART)期间的新型自动剂量累积过程,重点关注分次间的解剖结构变化以及实际 delivered 剂量与计划剂量之间的差异。

方法

对7例接受五分次立体定向体部放射治疗(SBRT)的前列腺癌患者进行回顾性分析,使用0.35T MRIdian MR直线加速器系统。当超过剂量阈值时调整每日计划。将计划MRI(pMRI)和每日MRI(dMRI)导入MIM软件进行自动和手动剂量累积程序。在进行刚性和可变形图像配准后进行剂量累积,以比较实际 delivered 剂量和计划剂量。通过计算豪斯多夫距离(HD)、杰卡德系数和骰子系数来比较手动和自动图像配准。

结果

在计划靶体积(PTV)的剂量学参数方面,自动累积剂量与计划剂量之间观察到中等差异,例如[具体参数1]和[具体参数2],平均差异分别为[X1]Gy和[X2]Gy。体积差异[具体体积差异1]和[具体体积差异2]表明,自动累积剂量与计划剂量相比数量始终较低,平均差异分别为[Y1]和[Y2]。危及器官(OAR)剂量学参数在自动累积剂量中显示出更高的剂量体积,在诸如尿道PRV在[具体剂量水平1]时为[具体体积1]、直肠在[具体剂量水平2]时为[具体体积2]以及直肠在[具体剂量水平3]时为[具体体积3]等参数中观察到中等差异(计划[cc]与自动累积[cc])。手动剂量与自动累积剂量之间的比较显示差异可忽略不计,几何指数的高度一致性以及t检验p值高于0.7也表明了这一点。

结论

与MIM软件公司合作开发的自动工作流程与手动累积相比显示出高精度。计划剂量与累积剂量之间观察到的中等差异强调了自适应计划中准确剂量累积的必要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a55/11905248/5aa5b63b6496/ACM2-26-e14594-g003.jpg

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本文引用的文献

1
Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation.
Front Oncol. 2023 Jan 26;12:1086258. doi: 10.3389/fonc.2022.1086258. eCollection 2022.
4
Quantifying the dose accumulation uncertainty after deformable image registration in head-and-neck radiotherapy.
Radiother Oncol. 2020 Feb;143:117-125. doi: 10.1016/j.radonc.2019.12.009. Epub 2020 Feb 14.
5
Automatic reconstruction of the delivered dose of the day using MR-linac treatment log files and online MR imaging.
Radiother Oncol. 2020 Apr;145:88-94. doi: 10.1016/j.radonc.2019.12.010. Epub 2020 Jan 10.
8
A new methodology for inter- and intrafraction plan adaptation for the MR-linac.
Phys Med Biol. 2015 Oct 7;60(19):7485-97. doi: 10.1088/0031-9155/60/19/7485. Epub 2015 Sep 15.
9
The residual setup errors of different IGRT alignment procedures for head and neck IMRT and the resulting dosimetric impact.
Int J Radiat Oncol Biol Phys. 2013 May 1;86(1):170-6. doi: 10.1016/j.ijrobp.2012.10.040. Epub 2012 Dec 11.
10
Parotid gland dose in intensity-modulated radiotherapy for head and neck cancer: is what you plan what you get?
Int J Radiat Oncol Biol Phys. 2007 Nov 15;69(4):1290-6. doi: 10.1016/j.ijrobp.2007.07.2345.

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