• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

MRI引导放疗中自动传播前列腺轮廓与手动绘制轮廓的剂量学比较:迈向无轮廓勾画工作流程的一步?

Dosimetric comparison of automatically propagated prostate contours with manually drawn contours in MRI-guided radiotherapy: A step towards a contouring free workflow?

作者信息

Sritharan Kobika, Dunlop Alex, Mohajer Jonathan, Adair-Smith Gillian, Barnes Helen, Brand Douglas, Greenlay Emily, Hijab Adham, Oelfke Uwe, Pathmanathan Angela, Mitchell Adam, Murray Julia, Nill Simeon, Parker Chris, Sundahl Nora, Tree Alison C

机构信息

The Royal Marsden NHS Foundation Trust, United Kingdom.

The Institute of Cancer Research, United Kingdom.

出版信息

Clin Transl Radiat Oncol. 2022 Aug 6;37:25-32. doi: 10.1016/j.ctro.2022.08.004. eCollection 2022 Nov.

DOI:10.1016/j.ctro.2022.08.004
PMID:36052018
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9424262/
Abstract

BACKGROUND

The prostate demonstrates inter- and intra- fractional changes and thus adaptive radiotherapy would be required to ensure optimal coverage. Daily adaptive radiotherapy for MRI-guided radiotherapy can be both time and resource intensive when structure delineation is completed manually. Contours can be auto-generated on the MR-Linac via a deformable image registration (DIR) based mapping process from the reference image. This study evaluates the performance of automatically generated target structure contours against manually delineated contours by radiation oncologists for prostate radiotherapy on the Elekta Unity MR-Linac.

METHODS

Plans were generated from prostate contours propagated by DIR and rigid image registration (RIR) for forty fractions from ten patients. A two-dose level SIB (simultaneous integrated boost) IMRT plan is used to treat localised prostate cancer; 6000 cGy to the prostate and 4860 cGy to the seminal vesicles. The dose coverage of the PTV 6000 and PTV 4860 created from the manually drawn target structures was evaluated with each plan. If the dose objectives were met, the plan was considered successful in covering the gold standard (clinician-delineated) volume.

RESULTS

The mandatory PTV 6000 dose objective (D98% > 5580 cGy) was met in 81 % of DIR plans and 45 % of RIR plans. The SV were mapped by DIR only and for all the plans, the PTV 4860 dose objective met the optimal target (D98% > 4617 cGy). The plans created by RIR led to under-coverage of the clinician-delineated prostate, predominantly at the apex or the bladder-prostate interface.

CONCLUSION

Plans created from DIR propagation of prostate contours outperform those created from RIR propagation. In approximately 1 in 5 DIR plans, dosimetric coverage of the gold standard PTV was not clinically acceptable. Thus, at our institution, we use a combination of DIR propagation of contours alongside manual editing of contours where deemed necessary for online treatments.

摘要

背景

前列腺在分次放疗期间及分次放疗之间会发生变化,因此需要采用自适应放疗以确保最佳覆盖范围。当手动完成结构勾画时,用于磁共振成像引导放疗的每日自适应放疗在时间和资源方面都较为密集。通过基于可变形图像配准(DIR)的映射过程,可从参考图像在磁共振直线加速器上自动生成轮廓。本研究评估了在医科达Unity磁共振直线加速器上,针对前列腺放疗,自动生成的靶区结构轮廓相对于放射肿瘤学家手动勾画轮廓的性能。

方法

从10例患者的前列腺轮廓通过DIR和刚性图像配准(RIR)传播生成40次分割的计划。采用两剂量水平同步整合加量(SIB)调强放疗计划治疗局限性前列腺癌;前列腺剂量为6000 cGy,精囊剂量为4860 cGy。用每个计划评估由手动绘制的靶区结构创建的PTV 6000和PTV 4860的剂量覆盖情况。如果达到剂量目标,则认为该计划成功覆盖了金标准(临床医生勾画)体积。

结果

81%的DIR计划和45%的RIR计划达到了强制性PTV 6000剂量目标(D98%>5580 cGy)。精囊仅通过DIR映射,对于所有计划,PTV 4860剂量目标达到了最佳靶区(D98%>4617 cGy)。由RIR创建的计划导致临床医生勾画的前列腺覆盖不足,主要在前列腺尖部或膀胱 - 前列腺界面。

结论

由前列腺轮廓的DIR传播创建的计划优于由RIR传播创建的计划。在大约五分之一的DIR计划中,金标准PTV的剂量学覆盖在临床上不可接受。因此,在我们机构,对于在线治疗,我们在必要时将轮廓的DIR传播与轮廓的手动编辑相结合使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb5b/9424262/478883a8f5e1/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb5b/9424262/7134478d723c/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb5b/9424262/c623389c4dbd/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb5b/9424262/925359eacc4b/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb5b/9424262/5adc0944ed6d/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb5b/9424262/478883a8f5e1/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb5b/9424262/7134478d723c/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb5b/9424262/c623389c4dbd/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb5b/9424262/925359eacc4b/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb5b/9424262/5adc0944ed6d/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb5b/9424262/478883a8f5e1/gr5.jpg

相似文献

1
Dosimetric comparison of automatically propagated prostate contours with manually drawn contours in MRI-guided radiotherapy: A step towards a contouring free workflow?MRI引导放疗中自动传播前列腺轮廓与手动绘制轮廓的剂量学比较:迈向无轮廓勾画工作流程的一步?
Clin Transl Radiat Oncol. 2022 Aug 6;37:25-32. doi: 10.1016/j.ctro.2022.08.004. eCollection 2022 Nov.
2
Feasibility of creating a daily adaptive plan using automatic DIR-created target and OARs contours in patients with prostate cancer magnetic-resonance-guided adaptive radiotherapy.在前列腺癌磁共振引导自适应放疗患者中,使用自动DIR创建的靶区和危及器官轮廓创建每日自适应计划的可行性。
J Radiat Res. 2024 Dec 3;65(6):845-850. doi: 10.1093/jrr/rrae088.
3
Evaluation of therapeutic radiographer contouring for magnetic resonance image guided online adaptive prostate radiotherapy.磁共振图像引导在线自适应前列腺放射治疗中治疗放射技师勾画靶区的评估。
Radiother Oncol. 2023 Mar;180:109457. doi: 10.1016/j.radonc.2022.109457. Epub 2023 Jan 3.
4
Evaluation of a commercial DIR platform for contour propagation in prostate cancer patients treated with IMRT/VMAT.评价一个商业的 DIR 平台在接受调强放疗/VMAT 治疗的前列腺癌患者中的靶区勾画。
J Appl Clin Med Phys. 2020 Feb;21(2):14-25. doi: 10.1002/acm2.12787.
5
Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.评估基于深度学习的自动分割在前列腺癌放疗中的剂量学影响:对实际临床应用和观察者间变异性的见解。
J Appl Clin Med Phys. 2025 Mar;26(3):e14569. doi: 10.1002/acm2.14569. Epub 2024 Dec 1.
6
Assessment of contour accuracy in head and neck replanning: Deep learning trained model compared with deformable image registration propagation technique.头颈部再计划中轮廓准确性的评估:深度学习训练模型与可变形图像配准传播技术的比较
Med Dosim. 2025 May 22. doi: 10.1016/j.meddos.2025.04.004.
7
Clinical adequacy assessment of autocontours for prostate IMRT with meaningful endpoints.基于有意义终点的前列腺调强放疗自动轮廓的临床充分性评估。
Med Phys. 2017 Apr;44(4):1525-1537. doi: 10.1002/mp.12158.
8
Development and validation of an MR-driven dose-of-the-day procedure for online adaptive radiotherapy in upper gastrointestinal cancer patients.上消化道癌患者在线自适应放疗中基于磁共振成像驱动的当日剂量程序的开发与验证
Phys Med Biol. 2024 Aug 1;69(16). doi: 10.1088/1361-6560/ad6745.
9
Automatic AI-based contouring of prostate MRI for online adaptive radiotherapy.基于人工智能的前列腺 MRI 自动勾画用于在线自适应放疗。
Z Med Phys. 2024 May;34(2):197-207. doi: 10.1016/j.zemedi.2023.05.001. Epub 2023 May 30.
10
The impact of margin reduction on radiation dose distribution of ultra-hypofractionated prostate radiotherapy utilizing a 1.5-T MR-Linac.利用 1.5TMR-Linac 进行超分次前列腺放射治疗时,边缘减少对放射剂量分布的影响。
J Appl Clin Med Phys. 2024 Jan;25(1):e14179. doi: 10.1002/acm2.14179. Epub 2023 Nov 27.

引用本文的文献

1
Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.评估基于深度学习的自动分割在前列腺癌放疗中的剂量学影响:对实际临床应用和观察者间变异性的见解。
J Appl Clin Med Phys. 2025 Mar;26(3):e14569. doi: 10.1002/acm2.14569. Epub 2024 Dec 1.
2
Feasibility of creating a daily adaptive plan using automatic DIR-created target and OARs contours in patients with prostate cancer magnetic-resonance-guided adaptive radiotherapy.在前列腺癌磁共振引导自适应放疗患者中,使用自动DIR创建的靶区和危及器官轮廓创建每日自适应计划的可行性。
J Radiat Res. 2024 Dec 3;65(6):845-850. doi: 10.1093/jrr/rrae088.
3

本文引用的文献

1
Machine Learning for Auto-Segmentation in Radiotherapy Planning.机器学习在放射治疗计划中的自动分割。
Clin Oncol (R Coll Radiol). 2022 Feb;34(2):74-88. doi: 10.1016/j.clon.2021.12.003. Epub 2022 Jan 5.
2
Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy.深度学习轮廓自动分割在前列腺放射治疗中的临床应用。
Radiother Oncol. 2021 Jun;159:1-7. doi: 10.1016/j.radonc.2021.02.040. Epub 2021 Mar 3.
3
A Patient-Specific Autosegmentation Strategy Using Multi-Input Deformable Image Registration for Magnetic Resonance Imaging-Guided Online Adaptive Radiation Therapy: A Feasibility Study.
Full daily re-optimization improves plan quality during online adaptive radiotherapy.
每日进行全面重新优化可提高在线自适应放疗期间的计划质量。
Phys Imaging Radiat Oncol. 2024 Jan 10;29:100534. doi: 10.1016/j.phro.2024.100534. eCollection 2024 Jan.
4
Artificial Intelligence-Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics.基于人工智能的自动分割:在轮廓描绘、吸收剂量分布及后勤保障方面的优势
Adv Radiat Oncol. 2023 Oct 26;9(3):101394. doi: 10.1016/j.adro.2023.101394. eCollection 2024 Mar.
一种使用多输入可变形图像配准的患者特异性自动分割策略用于磁共振成像引导的在线自适应放射治疗:一项可行性研究。
Adv Radiat Oncol. 2020 May 16;5(6):1350-1358. doi: 10.1016/j.adro.2020.04.027. eCollection 2020 Nov-Dec.
4
Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images.深度学习与基于图谱的模型在头颈部 CT 图像咀嚼肌自动分割中的比较。
Radiat Oncol. 2020 Jul 20;15(1):176. doi: 10.1186/s13014-020-01617-0.
5
Daily adaptive radiotherapy for patients with prostate cancer using a high field MR-linac: Initial clinical experiences and assessment of delivered doses compared to a C-arm linac.使用高场磁共振直线加速器对前列腺癌患者进行每日适应性放疗:初步临床经验及与C臂直线加速器相比的剂量交付评估。
Clin Transl Radiat Oncol. 2020 Apr 29;23:35-42. doi: 10.1016/j.ctro.2020.04.011. eCollection 2020 Jul.
6
Accuracy of automatic deformable structure propagation for high-field MRI guided prostate radiotherapy.高场 MRI 引导前列腺放射治疗中自动变形结构传播的准确性。
Radiat Oncol. 2020 Feb 7;15(1):32. doi: 10.1186/s13014-020-1482-y.
7
Prostate cancer - Advantages and disadvantages of MR-guided RT.前列腺癌——磁共振引导放疗的优缺点
Clin Transl Radiat Oncol. 2019 Apr 1;18:68-73. doi: 10.1016/j.ctro.2019.03.006. eCollection 2019 Sep.
8
Adaptive radiotherapy: The Elekta Unity MR-linac concept.自适应放疗:医科达Unity MR直线加速器概念
Clin Transl Radiat Oncol. 2019 Apr 2;18:54-59. doi: 10.1016/j.ctro.2019.04.001. eCollection 2019 Sep.
9
First clinical experiences with a high field 1.5 T MR linac.首例高场 1.5TMR 直线加速器的临床应用经验
Acta Oncol. 2019 Oct;58(10):1352-1357. doi: 10.1080/0284186X.2019.1627417. Epub 2019 Jun 26.
10
A review of cone-beam CT applications for adaptive radiotherapy of prostate cancer.锥形束 CT 在前列腺癌自适应放疗中的应用综述。
Phys Med. 2019 Mar;59:13-21. doi: 10.1016/j.ejmp.2019.02.014. Epub 2019 Feb 22.