Automated Non-coplanar Volumetric Modulated Arc Therapy Planning for Maxillary Sinus Carcinoma.

来自 PUBMED

作者:

Ohira SInui SKanayama NUeda YMiyazaki MKoizumi MKonishi K

展开

摘要:

Dosimetric parameters in volumetric modulated arc therapy (VMAT), non-coplanar VMAT (NC-VMAT), and automated NC-VMAT (HyperArc, HA) were compared for patients with maxillary sinus carcinoma (MSC). Twenty HA plans were generated to deliver 70.4, 64, and 46 Gy for planning target volumes with high (PTV1), intermediate (PTV2), and low risk (PTV3), respectively. The VMAT and NC-VMAT plans were retrospectively generated using the same optimized parameters as those used in the HA plans. For PTV1, the three treatment plans provided comparable target coverages. For PTV2, the D95%, D99%, and Dmin in the HA plans (64.7±1.2, 62.7±2.1 and 54.6±6.2 Gy, respectively) were significantly higher (p<0.05) than those in the VMAT plans (64.3±1.7, 61.9±2.4 and 52.9±6.4 Gy, respectively). The NC-VMAT and HA plans provided significantly higher (p<0.05) dosimetric parameters for PTV3 than those in the VMAT plans, and D99% in the HA was significantly higher than that in the NC-VMAT plans (52.5±3.0 vs. 51.8±2.7 Gy, p<0.05). The doses to the brain and brainstem were lowest in the HA plans (p<0.05). Moreover, dosimetric parameters of the contralateral organs (lens, optic nerve, retina, and parotid) were lower in the HA plans than in the VMAT and NC-VMAT plans. The HA plans provided the best target coverage and OAR sparing compared with VMAT and NC-VMAT plans for patients with MSC.

收起

展开

DOI:

10.21873/invivo.13094

被引量:

2

年份:

2023

SCI-Hub (全网免费下载) 发表链接

通过 文献互助 平台发起求助,成功后即可免费获取论文全文。

查看求助

求助方法1:

知识发现用户

每天可免费求助50篇

求助

求助方法1:

关注微信公众号

每天可免费求助2篇

求助方法2:

求助需要支付5个财富值

您现在财富值不足

您可以通过 应助全文 获取财富值

求助方法2:

完成求助需要支付5财富值

您目前有 1000 财富值

求助

我们已与文献出版商建立了直接购买合作。

你可以通过身份认证进行实名认证,认证成功后本次下载的费用将由您所在的图书馆支付

您可以直接购买此文献,1~5分钟即可下载全文,部分资源由于网络原因可能需要更长时间,请您耐心等待哦~

身份认证 全文购买

相似文献(105)

参考文献(34)

引证文献(2)

来源期刊

-

影响因子:暂无数据

JCR分区: 暂无

中科院分区:暂无

研究点推荐

关于我们

zlive学术集成海量学术资源,融合人工智能、深度学习、大数据分析等技术,为科研工作者提供全面快捷的学术服务。在这里我们不忘初心,砥砺前行。

友情链接

联系我们

合作与服务

©2024 zlive学术声明使用前必读