Advanced treatment planning strategies to enhance quality and efficiency of radiotherapy.
作者信息
Cozzi Luca, Heijmen Ben J M, Muren Ludvig P
机构信息
Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy.
Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy.
出版信息
Phys Imaging Radiat Oncol. 2019 Sep 13;11:69-70. doi: 10.1016/j.phro.2019.09.002. eCollection 2019 Jul.
Abstract
摘要
相似文献
1
Advanced treatment planning strategies to enhance quality and efficiency of radiotherapy.提高放射治疗质量和效率的先进治疗计划策略。
Phys Imaging Radiat Oncol. 2019 Sep 13;11:69-70. doi: 10.1016/j.phro.2019.09.002. eCollection 2019 Jul.
2
Challenges in lung and heart avoidance for postmastectomy breast cancer radiotherapy: Is automated planning the answer?乳腺癌根治术后肺和心脏回避放疗的挑战：自动化计划是答案吗？
Med Dosim. 2021;46(3):295-303. doi: 10.1016/j.meddos.2021.03.002. Epub 2021 Apr 6.
3
Optimising the dosimetric quality and efficiency of post-prostatectomy radiotherapy: a planning study comparing the performance of volumetric-modulated arc therapy (VMAT) with an optimised seven-field intensity-modulated radiotherapy (IMRT) technique.优化前列腺切除术后放疗的剂量学质量和效率：一项比较容积调强弧形放疗（VMAT）与优化七野调强放疗（IMRT）技术性能的计划研究。
J Med Imaging Radiat Oncol. 2012 Apr;56(2):211-9. doi: 10.1111/j.1754-9485.2011.02324.x.
4
A study of IMRT planning parameters on planning efficiency, delivery efficiency, and plan quality.调强放疗计划参数对计划效率、实施效率和计划质量的研究。
Med Phys. 2013 Jun;40(6):061704. doi: 10.1118/1.4803460.
5
Development and evaluation of a three-step automatic planning technique for lung SBRT based on performance examination of advanced settings in Pinnacle's auto-planning module.基于 Pinnacle 自动规划模块中高级设置性能检查的肺 SBRT 三步自动规划技术的开发与评估。
Appl Radiat Isot. 2022 Nov;189:110434. doi: 10.1016/j.apradiso.2022.110434. Epub 2022 Aug 29.
6
Automated treatment planning of postmastectomy radiotherapy.乳腺癌根治术后放疗的自动化治疗计划。
Med Phys. 2019 Sep;46(9):3767-3775. doi: 10.1002/mp.13586. Epub 2019 Jul 9.
7
Enhancing Radiotherapy for Locally Advanced Non-Small Cell Lung Cancer Patients with iCE, a Novel System for Automated Multi-Criterial Treatment Planning Including Beam Angle Optimization.利用iCE增强局部晚期非小细胞肺癌患者的放疗，iCE是一种用于自动多标准治疗计划（包括射束角度优化）的新型系统。
Cancers (Basel). 2021 Nov 13;13(22):5683. doi: 10.3390/cancers13225683.
8
Evaluation of automatic VMAT plans in locally advanced nasopharyngeal carcinoma.局部晚期鼻咽癌的自动 VMAT 计划评估。
Strahlenther Onkol. 2021 Mar;197(3):177-187. doi: 10.1007/s00066-020-01631-x. Epub 2020 Jun 2.
9
Optimizing efficiency and safety in external beam radiotherapy using automated plan check (APC) tool and six sigma methodology.使用自动化计划检查（APC）工具和六西格玛方法优化外照射放射治疗的效率和安全性。
J Appl Clin Med Phys. 2019 Aug;20(8):56-64. doi: 10.1002/acm2.12678.
10
Advanced pencil beam scanning Bragg peak FLASH-RT delivery technique can enhance lung cancer planning treatment outcomes compared to conventional multiple-energy proton PBS techniques.与传统的多能质子铅笔束扫描布拉格峰 FLASH-RT 技术相比，先进的铅笔束扫描布拉格峰 FLASH-RT 输送技术可以提高肺癌计划治疗效果。
Radiother Oncol. 2022 Oct;175:238-247. doi: 10.1016/j.radonc.2022.08.005. Epub 2022 Aug 10.
引用本文的文献
1
Synthesizing Efficiency Tools in Radiotherapy to Increase Patient Flow: A Comprehensive Literature Review.综合放疗中的效率工具以增加患者流量：一项全面的文献综述。
Clin Med Insights Oncol. 2024 Dec 13;18:11795549241303606. doi: 10.1177/11795549241303606. eCollection 2024.
2
Remote radiotherapy treatment planning system: An efficiency tool for increasing patient flow in cancer treatment in South Africa.远程放射治疗治疗计划系统：提高南非癌症治疗患者流量的高效工具。
Ann Med Surg (Lond). 2024 Sep 10;86(10):6355-6357. doi: 10.1097/MS9.0000000000002537. eCollection 2024 Oct.
3
Knowledge-based versus deep learning based treatment planning for breast radiotherapy.基于知识与基于深度学习的乳腺癌放疗治疗计划
Phys Imaging Radiat Oncol. 2024 Jan 20;29:100539. doi: 10.1016/j.phro.2024.100539. eCollection 2024 Jan.
4
Automated Radiotherapy Planning for Patient-Specific Exploration of the Trade-Off Between Tumor Dose Coverage and Predicted Radiation-Induced Toxicity-A Proof of Principle Study for Prostate Cancer.用于患者特异性探索肿瘤剂量覆盖与预测的放射诱导毒性之间权衡的自动放射治疗计划——前列腺癌的一项原理验证研究
Front Oncol. 2020 Jun 30;10:943. doi: 10.3389/fonc.2020.00943. eCollection 2020.
本文引用的文献
1
Comparison of dose metrics between automated and manual radiotherapy planning for advanced stage non-small cell lung cancer with volumetric modulated arc therapy.容积调强弧形放疗用于晚期非小细胞肺癌时自动与手动放疗计划的剂量指标比较
Phys Imaging Radiat Oncol. 2019 Mar 18;9:92-96. doi: 10.1016/j.phro.2019.03.003. eCollection 2019 Jan.
2
Real-time interactive planning for radiotherapy of head and neck cancer with volumetric modulated arc therapy.头颈部癌容积调强弧形放疗的实时交互式计划
Phys Imaging Radiat Oncol. 2019 Apr 4;9:83-88. doi: 10.1016/j.phro.2019.03.002. eCollection 2019 Jan.
3
Development of a multicentre automated model to reduce planning variability in radiotherapy of prostate cancer.开发一种多中心自动化模型以减少前列腺癌放射治疗中的计划变异性。
Phys Imaging Radiat Oncol. 2019 Aug 8;11:34-40. doi: 10.1016/j.phro.2019.07.005. eCollection 2019 Jul.
4
Utilisation of Pareto navigation techniques to calibrate a fully automated radiotherapy treatment planning solution.利用帕累托导航技术校准全自动放射治疗计划解决方案。
Phys Imaging Radiat Oncol. 2019 May 16;10:41-48. doi: 10.1016/j.phro.2019.04.005. eCollection 2019 Apr.
5
Adapting automated treatment planning configurations across international centres for prostate radiotherapy.在国际前列腺放射治疗中心之间调整自动治疗计划配置。
Phys Imaging Radiat Oncol. 2019 Apr 24;10:7-13. doi: 10.1016/j.phro.2019.04.007. eCollection 2019 Apr.
6
Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network.利用深度卷积神经网络将剂量学特征纳入 3DVMAT 剂量分布预测中。
Phys Med Biol. 2019 Jun 20;64(12):125017. doi: 10.1088/1361-6560/ab2146.
7
Automated Radiotherapy Treatment Planning.自动化放疗计划。
Semin Radiat Oncol. 2019 Jul;29(3):209-218. doi: 10.1016/j.semradonc.2019.02.003.
8
Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches.基于知识的强度调制放射治疗计划：数据驱动方法综述。
Med Phys. 2019 Jun;46(6):2760-2775. doi: 10.1002/mp.13526. Epub 2019 Apr 24.
9
3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture.基于层次化密集连接 U-Net 深度学习架构对头颈部癌症患者的 3D 放疗剂量预测。
Phys Med Biol. 2019 Mar 18;64(6):065020. doi: 10.1088/1361-6560/ab039b.
10
Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique.基于深度学习技术预测的三维剂量分布的自动治疗计划。
Med Phys. 2019 Jan;46(1):370-381. doi: 10.1002/mp.13271. Epub 2018 Nov 28.
Zotero 插件