Variation of heart and lung radiation doses according to setup uncertainty in left breast cancer

RADIATION ONCOLOGY(2021)

引用 3|浏览1
暂无评分
摘要
Purpose Breast radiotherapy set-up is often uncertain. Actual dose distribution to normal tissues could be different from planned dose distribution. The objective of this study was to investigate such difference in dose distribution according to the extent of set-up error in breast radiotherapy. Materials and methods A total of 50 Gy with fraction size of 2 Gy was given to 30 left breasts with different set-ups applying a deep inspiration breath holding (DIBH) or a free breathing (FB) technique. Under the assumption that errors might come from translational axes of deep or caudal directions, the isocenter was shifted from the original tangential alignment every 2.5 mm to simulate uncertainty of deep and caudal tangential set-up in DIBH and FB. Changes were evaluated for dosimetric parameters for the heart, the left ventricle (LV), the left anterior descending coronary artery (LAD), and the ipsilateral lung. Results On the original plan, mean doses of heart and ipsilateral lung were 2.0 ± 1.1 Gy and 3.7 ± 1.4 Gy in DIBH and 8.4 ± 1.3 Gy and 7.8 ± 1.5 Gy in FB, respectively. The change of dose distribution for the heart in DIBH was milder than that in FB. The deeper the tangential set-up, the worse the heart, LV, LAD, and ipsilateral lung doses, showing as much as 49.4%, 56.4%, 90.3%, and 26.1% shifts, respectively, in 5 mm DIBH setup. The caudal set-up did not show significant dose difference. In multiple comparison of DIBH, differences of mean dose occurred in all 7.5 mm deep set-ups for the heart ( p = 0.025), the LV ( p = 0.049), and LAD ( p = 0.025) in DIBH. Conclusions To correct set-up error over indicated limitation for deep tangential set-up in DIBH at 5 mm action level, mean heart and ipsilateral lung doses are expected to increase approximately 50% and 25%, respectively.
更多
查看译文
关键词
Breast cancer,Radiotherapy,Set-up uncertainty,Deep inspiration breath holding,Heart
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要