"sCT-Feasibility"--基于深度学习的仅磁共振成像脑放射治疗可行性研究。

IF 3.3 2区 医学 Q2 ONCOLOGY
Johanna Grigo, Juliane Szkitsak, Daniel Höfler, Rainer Fietkau, Florian Putz, Christoph Bert
{"title":"\"sCT-Feasibility\"--基于深度学习的仅磁共振成像脑放射治疗可行性研究。","authors":"Johanna Grigo, Juliane Szkitsak, Daniel Höfler, Rainer Fietkau, Florian Putz, Christoph Bert","doi":"10.1186/s13014-024-02428-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process. The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data.</p><p><strong>Methods: </strong>A total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery.</p><p><strong>Discussion: </strong>Compared to CT, MRI offers a superior soft tissue contrast without additional radiation dose to the patients. However, up to now, even though the dosimetrical equivalence of CT and sCT has been shown in several retrospective studies, MRI-only workflows have still not been widely adopted. The present study aims to determine feasibility and safety of deep learning-based MRI-only radiotherapy in a holistic manner incorporating the whole radiotherapy workflow.</p><p><strong>Trial registration: </strong>NCT06106997.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10924348/pdf/","citationCount":"0","resultStr":"{\"title\":\"\\\"sCT-Feasibility\\\" - a feasibility study for deep learning-based MRI-only brain radiotherapy.\",\"authors\":\"Johanna Grigo, Juliane Szkitsak, Daniel Höfler, Rainer Fietkau, Florian Putz, Christoph Bert\",\"doi\":\"10.1186/s13014-024-02428-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process. The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data.</p><p><strong>Methods: </strong>A total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery.</p><p><strong>Discussion: </strong>Compared to CT, MRI offers a superior soft tissue contrast without additional radiation dose to the patients. However, up to now, even though the dosimetrical equivalence of CT and sCT has been shown in several retrospective studies, MRI-only workflows have still not been widely adopted. The present study aims to determine feasibility and safety of deep learning-based MRI-only radiotherapy in a holistic manner incorporating the whole radiotherapy workflow.</p><p><strong>Trial registration: </strong>NCT06106997.</p>\",\"PeriodicalId\":49639,\"journal\":{\"name\":\"Radiation Oncology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10924348/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiation Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13014-024-02428-3\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiation Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13014-024-02428-3","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

背景:放射治疗(RT)是脑部恶性肿瘤患者的一种重要治疗方式。传统上,计算机断层扫描(CT)图像用于制定 RT 治疗计划,而磁共振成像(MRI)图像则用于划定肿瘤范围。因此,MRI 和 CT 需要进行登记,这是一个容易出错的过程。这项临床研究的目的是调查基于深度学习的仅 MRI 工作流程在脑部放疗中的临床可行性,该流程通过计算 MRI 数据合成 CT(sCT)来消除配准的不确定性:共将招募 54 名具有脑部放射治疗和立体定向面罩固定适应症的患者。所有研究患者都将接受标准治疗和成像,包括 CT 和 MRI。所有患者都将在治疗位置接受专用的 RT-MRI 扫描。将使用市售的深度学习解决方案,从获取的磁共振成像 DIXON 序列重建 sCT,并在此基础上执行后续放疗计划。在研究过程中,将通过多种质量保证(QA)措施和审查,全面调查仅磁共振成像工作流程的可行性以及 sCT 和标准 CT 工作流程之间的比较参数。这些质量保证措施包括在直线加速器上使用 sCT 导出的数字重建射线照片进行图像引导(IGRT)的可行性和质量,以及 CT 和 sCT 计划之间潜在的剂量偏差。这项临床研究的目的是建立纯脑部核磁共振成像工作流程,并确定风险和质量保证机制,以确保将基于深度学习的 sCT 安全集成到放疗计划和实施中:与CT相比,核磁共振成像在不增加患者辐射剂量的情况下提供了更好的软组织对比度。然而,迄今为止,尽管已有多项回顾性研究表明 CT 和 sCT 的剂量等效,但只使用 MRI 的工作流程仍未被广泛采用。本研究旨在以整合整个放疗工作流程的整体方式,确定基于深度学习的纯核磁共振放疗的可行性和安全性:NCT06106997.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.

Background: Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process. The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data.

Methods: A total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery.

Discussion: Compared to CT, MRI offers a superior soft tissue contrast without additional radiation dose to the patients. However, up to now, even though the dosimetrical equivalence of CT and sCT has been shown in several retrospective studies, MRI-only workflows have still not been widely adopted. The present study aims to determine feasibility and safety of deep learning-based MRI-only radiotherapy in a holistic manner incorporating the whole radiotherapy workflow.

Trial registration: NCT06106997.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Radiation Oncology
Radiation Oncology ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
6.50
自引率
2.80%
发文量
181
审稿时长
3-6 weeks
期刊介绍: Radiation Oncology encompasses all aspects of research that impacts on the treatment of cancer using radiation. It publishes findings in molecular and cellular radiation biology, radiation physics, radiation technology, and clinical oncology.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信