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左侧乳腺癌患者深吸气屏气技术的稳定性和可重复性比较:一项前瞻性研究。

Stability and reproducibility comparisons between deep inspiration breath-hold techniques for left-sided breast cancer patients: A prospective study.

机构信息

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Department of Radiation Oncology, Inova Fairfax Hospital, Falls Church, Virginia, USA.

出版信息

J Appl Clin Med Phys. 2023 May;24(5):e13906. doi: 10.1002/acm2.13906. Epub 2023 Jan 23.


DOI:10.1002/acm2.13906
PMID:36691339
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10161105/
Abstract

PURPOSE: Deep inspiration breath-hold (DIBH) is crucial in reducing the lung and cardiac dose for treatment of left-sided breast cancer. We compared the stability and reproducibility of two DIBH techniques: Active Breathing Coordinator (ABC) and VisionRT (VRT). MATERIALS AND METHODS: We examined intra- and inter-fraction positional variation of the left lung. Eight left-sided breast cancer patients were monitored with electronic portal imaging during breath-hold (BH) at every fraction. For each patient, half of the fractions were treated using ABC and the other half with VRT, with an equal amount starting with either ABC or VRT. The lung in each portal image was delineated, and the variation of its area was evaluated. Intrafraction stability was evaluated as the mean coefficient of variation (CV) of the lung area for the supraclavicular (SCV) and left lateral (LLat) field over the course of treatment. Reproducibility was the CV for the first image of each fraction. Daily session time and total imaging monitor units (MU) used in patient positioning were recorded. RESULTS: The mean intrafraction stability across all patients for the LLat field was 1.3 ± 0.7% and 1.5 ± 0.9% for VRT and ABC, respectively. Similarly, this was 1.5 ± 0.7% and 1.6 ± 0.8% for VRT and ABC, respectively, for the SCV field. The mean interfraction reproducibility for the LLat field was 11.0 ± 3.4% and 14.9 ± 6.0% for VRT and ABC, respectively. Similarly, this was 13.0 ± 2.5% and 14.8 ± 9% for VRT and ABC, respectively, for the SCV. No difference was observed in the number of verification images required for either technique. CONCLUSIONS: The stability and reproducibility were found to be comparable between ABC and VRT. ABC can have larger interfractional variation with less feedback to the treating therapist compared to VRT as shown in the increase in geometric misses at the matchline.

摘要

目的:深吸气屏气(DIBH)对于减少左侧乳腺癌的肺和心脏剂量至关重要。我们比较了两种 DIBH 技术的稳定性和可重复性:主动呼吸控制器(ABC)和 VisionRT(VRT)。

材料和方法:我们检查了左肺的内部分和间部分位置变化。在每个分次的屏气期间,使用电子门控成像对 8 例左侧乳腺癌患者进行监测。对于每个患者,一半的分次使用 ABC 治疗,另一半使用 VRT 治疗,ABC 或 VRT 各有一半开始治疗。在每个门图像中描绘了肺,并评估了其面积的变化。在整个治疗过程中,通过锁骨上(SCV)和左侧(LLat)区域的肺面积的平均变异系数(CV)评估了分次内稳定性。重复性是每个分次的第一幅图像的 CV。记录了患者定位的每日治疗时间和总成像监测单位(MU)。

结果:所有患者的 LLat 区域的平均分次内稳定性分别为 VRT 和 ABC 的 1.3±0.7%和 1.5±0.9%。同样,SCV 区域分别为 1.5±0.7%和 1.6±0.8%。LLat 区域的平均分次间可重复性分别为 VRT 和 ABC 的 11.0±3.4%和 14.9±6.0%。同样,SCV 区域分别为 13.0±2.5%和 14.8±9%。两种技术都不需要进行更多的验证图像。

结论:与 VRT 相比,ABC 和 VRT 的稳定性和可重复性相当。与 VRT 相比,ABC 可能具有更大的分次间变化,并且对治疗医生的反馈较少,如在匹配线处出现更多的几何漏诊。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/7844fc90749d/ACM2-24-e13906-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/1fa871cc2e06/ACM2-24-e13906-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/b6fd4773b242/ACM2-24-e13906-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/2bf84f7e47fc/ACM2-24-e13906-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/b0c4620488d7/ACM2-24-e13906-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/a18322da9f9a/ACM2-24-e13906-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/7844fc90749d/ACM2-24-e13906-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/1fa871cc2e06/ACM2-24-e13906-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/b6fd4773b242/ACM2-24-e13906-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/2bf84f7e47fc/ACM2-24-e13906-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/b0c4620488d7/ACM2-24-e13906-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/a18322da9f9a/ACM2-24-e13906-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a17f/10161105/7844fc90749d/ACM2-24-e13906-g006.jpg

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引用本文的文献

[1]
A deep learning model to predict dose distributions for breast cancer radiotherapy.

Discov Oncol. 2025-2-12

[2]
Difference between planned and delivered radiotherapy dose to the internal mammary nodes in high-risk breast cancer patients.

Phys Imaging Radiat Oncol. 2023-7-12

本文引用的文献

[1]
Investigation of planar image quality for a novel 2.5 MV diamond target beam from a radiotherapy linear accelerator.

Phys Imaging Radiat Oncol. 2020-11-5

[2]
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Radiat Oncol. 2019-7-11

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Radiat Oncol. 2019-6-18

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J Appl Clin Med Phys. 2019-3

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Radiother Oncol. 2017-10-12

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Pract Radiat Oncol. 2017-4-13

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Int J Radiat Oncol Biol Phys. 2017-4-1

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