Diffusion-Weighted Imaging-Based Adaptive Replanning for the MR-Linac
MR-Linac 基于扩散加权成像的自适应重新规划
基本信息
- 批准号:9908595
- 负责人:
- 金额:$ 2.98万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-09-01 至 2022-08-31
- 项目状态:已结题
- 来源:
- 关键词:AffectAlgorithmsAnatomyClinicClinicalClinical TrialsComplicationComputer SimulationDataData SetDeglutitionDentalDevelopmentDevicesDiffusion Magnetic Resonance ImagingDoseFeedbackFoundationsFunctional ImagingFunctional Magnetic Resonance ImagingGeometryGoalsHead and neck structureHealthHuman PapillomavirusHybridsImageImaging TechniquesInjuryInstitutionJawKnowledgeLinear Accelerator Radiotherapy SystemsMagnetic Resonance ImagingMalignant NeoplasmsMeasuresMethodsModelingMonitorMouth SoreNormal tissue morphologyOralOral healthOral mucous membrane structureOrganOutcomePatient imagingPatient-Focused OutcomesPatientsPhysiologic pulseProcessQuality of lifeRadiationRadiation ToleranceRadiation therapyRiskSchemeSignal TransductionStatistical ModelsTestingTissuesToxic effectTumor TissueTumor VolumeUnited StatesVendorWorkXerostomiaanatomic imagingbasecraniofacialexperienceexperimental studyimage guided radiation therapymalignant mouth neoplasmmalignant oropharynx neoplasmnovelpatient responseprototyperadiation responseradiation-induced injuryradioresistantresponseside effectsurvivorshiptreatment planningtreatment responsetreatment strategytumor
项目摘要
Project Summary
Patients undergoing radiation therapy (RT) for oral and craniofacial cancers such as human papillomavirus-
positive oropharyngeal cancer (HPV+ OPC) experience a host of side effects caused by radiation-induced
injury to healthy tissues. Although RT is highly curative for HPV+ OPC, radiation-induced sequelae can persist
for decades of survivorship, significantly degrading a patient's oral health and quality of life. Toxicity to healthy
tissues can be reduced by adaptive replanning, in which the geometry of the radiation beams is re-optimized
periodically during a multi-week course of RT to account for tumor shrinkage and normal tissue deformation.
Adaptive replanning is now clinically feasible for oral and craniofacial cancers due the recent development of a
hybrid MRI/linear accelerator device (MR-Linac). Adaptive treatments have used only basic anatomical MRI
pulse sequences to monitor the tumor volume. However, we propose an adaptive treatment strategy that uses
a functional MRI technique called diffusion-weighted imaging (DWI), which can assess normal tissue function,
identify radioresistant sub-volumes within tumors, and predict patient response to RT. The hypothesis of this
study is that the functional information from DWI can be implemented into the adaptive replanning process for
oral and craniofacial cancers such that it is clinically feasible (with the new MR-Linac device) and will reduce
side effects. To test this hypothesis, we will first develop a multivariate regression model relating changes in
ADC values of the tumor and healthy tissues to HPV+ OPC patient outcomes. This information will be
integrated into a DWI-based adaptive replanning workflow for the MR-Linac (Specific Aim 1). Next, the DWI-
based adaptive replanning approach will be modeled retrospectively on daily patient images. A dose
accumulation algorithm compatible with the MR-Linac's MRI-based dose calculation method will be developed
and employed to measure cumulative doses to organs at risk. Cumulative doses will be related to normal
tissue complication probability models to determine whether this approach lowers the risk of side effects
(Specific Aim 2). The expected outcome of these specific aims is the development of an adaptive RT approach
that uses functional data from DWI. The clinical feasibility and benefit of this treatment scheme will be
demonstrated through in silico and statistical modeling so that it may eventually be used in the clinic. This
project will positively impact patients with HPV+ OPC by enabling the delivery of personalized, targeted RT to
the tumor while sparing normal tissues and reducing side effects. Further, this work will have a broader impact
on the field of oral, dental, and craniofacial health by introducing a novel treatment paradigm that directly
monitors and reacts to normal tissue injury without compromising tumor control.
项目摘要
接受放射治疗(RT)的口腔和颅面部癌症患者,如人乳头瘤病毒-
阳性口咽癌(HPV+OPC)经历了许多放射诱导的副作用
对健康组织的伤害。尽管RT对HPV+OPC有很高的疗效,但辐射引起的后遗症可能会持续存在
几十年的生存,大大降低了患者的口腔健康和生活质量。对健康的毒性
组织可以通过自适应重新规划来减少,在该规划中,辐射束的几何形状被重新优化
在为期数周的放疗过程中定期进行,以考虑肿瘤缩小和正常组织变形。
适应性再计划目前在口腔和颅面肿瘤的临床上是可行的,因为最近发展了一种
核磁共振/直线加速器混合装置(MR-LINAC)。适应性治疗只使用了基本的解剖学核磁共振
用于监测肿瘤体积的脉冲序列。然而,我们提出了一种适应性治疗策略,使用
一种名为扩散加权成像(DWI)的功能磁共振技术,可以评估正常组织功能,
确定肿瘤内放射抵抗的亚体积,并预测患者对RT的反应。这是一个假设
研究表明,来自DWI的功能信息可以被实施到自适应重新规划过程中
口腔和颅面部癌症在临床上是可行的(使用新的MR-LINAC设备),并将减少
副作用。为了检验这一假设,我们将首先开发一个多元回归模型,该模型将
肿瘤和健康组织的ADC值对HPV+OPC患者预后的影响。这一信息将是
整合到MR-LINAC基于DWI的适应性重新规划工作流程中(具体目标1)。接下来,酒后驾车-
基于自适应再规划的方法将在日常患者图像上进行回溯建模。一剂
将开发与MR-Linac基于MRI的剂量计算方法兼容的累积算法
并被用来测量危险器官的累积剂量。累积剂量将与正常剂量相关
组织并发症概率模型以确定这种方法是否降低了副作用的风险
(具体目标2)。这些特定目标的预期结果是开发自适应RT方法
使用来自DWI的函数数据。这一治疗方案的临床可行性和益处将是
通过计算机和统计建模进行了论证,使其最终可以应用于临床。这
Project将通过提供个性化、有针对性的RT来对HPV+OPC患者产生积极影响
在保留正常组织和减少副作用的同时治疗肿瘤。此外,这项工作将产生更广泛的影响
在口腔、牙齿和颅面健康领域,通过引入一种新的治疗范例,直接
在不影响肿瘤控制的情况下监控并对正常组织损伤做出反应。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Brigid Anne McDonald其他文献
Brigid Anne McDonald的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Brigid Anne McDonald', 18)}}的其他基金
Diffusion-Weighted Imaging-Based Adaptive Replanning for the MR-Linac
MR-Linac 基于扩散加权成像的自适应重新规划
- 批准号:
10060718 - 财政年份:2019
- 资助金额:
$ 2.98万 - 项目类别:
Diffusion-Weighted Imaging-Based Adaptive Replanning for the MR-Linac
MR-Linac 基于扩散加权成像的自适应重新规划
- 批准号:
10237410 - 财政年份:2019
- 资助金额:
$ 2.98万 - 项目类别:
相似海外基金
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 2.98万 - 项目类别:
Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 2.98万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 2.98万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 2.98万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 2.98万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 2.98万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 2.98万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 2.98万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 2.98万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 2.98万 - 项目类别:
Continuing Grant