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本文引用的文献

1
An online adaptive plan library approach for intensity modulated proton therapy for head and neck cancer.在线自适应计划库方法在头颈部癌症调强质子治疗中的应用。
Radiother Oncol. 2022 Nov;176:68-75. doi: 10.1016/j.radonc.2022.09.011. Epub 2022 Sep 20.
2
Assessment of semi-automated stereotactic treatment planning for online adaptive radiotherapy in ethos.评估 Ethos 在线自适应放疗中的半自动立体定向治疗计划
Med Dosim. 2022;47(4):342-347. doi: 10.1016/j.meddos.2022.08.001. Epub 2022 Sep 18.
3
Personalized mid-course FDG-PET based adaptive treatment planning for non-small cell lung cancer using machine learning and optimization.基于机器学习和优化的非小细胞肺癌个体化中程 FDG-PET 自适应治疗计划。
Phys Med Biol. 2022 Sep 13;67(18). doi: 10.1088/1361-6560/ac88b3.
4
Attention-aware 3D U-Net convolutional neural network for knowledge-based planning 3D dose distribution prediction of head-and-neck cancer.基于注意力的 3D U-Net 卷积神经网络在头颈部癌症知识引导的 3D 剂量分布预测中的应用。
J Appl Clin Med Phys. 2022 Jul;23(7):e13630. doi: 10.1002/acm2.13630. Epub 2022 May 9.
5
Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy.利用深度学习生成的适形辐射治疗宫颈癌的通量图来加速治疗计划流程。
Med Phys. 2022 Apr;49(4):2631-2641. doi: 10.1002/mp.15530. Epub 2022 Feb 25.
6
Assessment of efficacy in automated plan generation for Varian Ethos intelligent optimization engine.评估瓦里安 Ethos 智能优化引擎自动计划生成的疗效。
J Appl Clin Med Phys. 2022 Apr;23(4):e13539. doi: 10.1002/acm2.13539. Epub 2022 Jan 27.
7
A hybrid optimization strategy for deliverable intensity-modulated radiotherapy plan generation using deep learning-based dose prediction.基于深度学习剂量预测的可交付强度调制放疗计划生成的混合优化策略。
Med Phys. 2022 Mar;49(3):1344-1356. doi: 10.1002/mp.15462. Epub 2022 Feb 9.
8
Online adaptive radiotherapy potentially reduces toxicity for high-risk prostate cancer treatment.在线自适应放疗可能降低高危前列腺癌治疗的毒性。
Radiother Oncol. 2022 Feb;167:165-171. doi: 10.1016/j.radonc.2021.12.013. Epub 2021 Dec 16.
9
CT-on-Rails Versus In-Room CBCT for Online Daily Adaptive Proton Therapy of Head-and-Neck Cancers.用于头颈癌在线每日自适应质子治疗的轨道CT与室内CBCT对比
Cancers (Basel). 2021 Nov 28;13(23):5991. doi: 10.3390/cancers13235991.
10
Transfer learning for fluence map prediction in adrenal stereotactic body radiation therapy.基于迁移学习的肾上腺立体定向体部放射治疗射野分布预测
Phys Med Biol. 2021 Dec 6;66(24). doi: 10.1088/1361-6560/ac3c14.

在线自适应调强放疗计划方法。

Online adaptive planning methods for intensity-modulated radiotherapy.

机构信息

Department of Business Analytics, University of Amsterdam, The Netherlands.

Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, United States of America.

出版信息

Phys Med Biol. 2023 May 11;68(10). doi: 10.1088/1361-6560/accdb2.

DOI:10.1088/1361-6560/accdb2
PMID:37068488
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10637515/
Abstract

Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current anatomy to account for inter-fraction variations before daily treatment delivery. As this process needs to be accomplished while the patient is immobilized on the treatment couch, it requires time-efficient adaptive planning methods to generate a quality daily treatment plan rapidly. The conventional planning methods do not meet the time requirement of online adaptive radiation therapy because they often involve excessive human intervention, significantly prolonging the planning phase. This article reviews the planning strategies employed by current commercial online adaptive radiation therapy systems, research on online adaptive planning, and artificial intelligence's potential application to online adaptive planning.

摘要

在线自适应放射治疗旨在根据每日治疗前分次间的变化,调整患者的治疗计划以适应其当前的解剖结构。由于该过程需要在患者固定在治疗台上时完成,因此需要高效的自适应规划方法来快速生成高质量的每日治疗计划。传统的规划方法无法满足在线自适应放射治疗的时间要求,因为它们通常需要大量的人工干预,从而显著延长了规划阶段的时间。本文回顾了当前商业在线自适应放射治疗系统所采用的规划策略、在线自适应规划方面的研究以及人工智能在在线自适应规划中的潜在应用。