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Advancing Neurosurgical Neuronavigation Using Resting State MRI and Machine Learning

利用静息态 MRI 和机器学习推进神经外科神经导航

基本信息

批准号：
10685402
负责人：
Eric CLAUDE Leuthardt
金额：
$ 55.35万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-01-17 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10685402
关键词：
3-Dimensional Adoption Algorithms Anatomy Architecture Biological Markers Biopsy Brain Brain Mapping Brain Neoplasms Brain imaging Brain region Caring Child Clinical Clinical Trials Cognitive Computer software Craniotomy Data Development Devices Diagnosis Excision Failure Functional Magnetic Resonance Imaging Funding Glioblastoma Glioma Goals Image Imaging technology Infrastructure Investigation Knowledge Lesion Machine Learning Magnetic Resonance Imaging Malignant Glioma Maps Methods Navigation System Neuroanatomy Neuronavigation Neurons Neurosurgeon Operative Surgical Procedures Outcome Output Pathologic Patient Care Patient Participation Patient imaging Patient-Focused Outcomes Patients Performance Population Procedures Productivity Prognosis Progression-Free Survivals Public Health Quality Control Quality of life Research Rest Sedation procedure Surgeon System Systems Integration Techniques Technology Time United States Food and Drug Administration Universities Validation Visualization Washington Work analysis pipeline brain tumor resection clinical decision support clinical decision-making clinically relevant convolutional neural network effective therapy experience functional status improved individualized medicine industry partner innovation insight machine learning algorithm multilayer perceptron neurosurgery next generation operation personalized approach preservation prognostic prognostic algorithm prognostic of survival prospective radiomics success survival prediction tool treatment optimization treatment planning tumor

项目摘要

Abstract. Long-term survival of patients with glioblastomas (GBM) are associated with two competing priorities: 1) gross total resection and 2) preservation of the patient’s function. Stereotactic navigation, in which reconstructed magnetic resonance images (MRI) of the brain are used for real-time intraoperative anatomic guidance, has become an essential tool for tumor resection. Further, there are emerging insights that glioma- specific perturbations of the functional organization of the brain impact the patient’s survival. However, the current barrier is that there is no FDA approved navigation system that enables the surgeon to visualize the functional architecture of the brain and the impact a tumor has on the brain’s network organization to inform prognosis. Resting state functional MRI (rs-fMRI) has emerged as a powerful tool for mapping clinically relevant brain networks and defining critical glioma-neuronal interactions. rs-fMRI is highly efficient, task independent, and multiple resting state networks (RSNs) can be mapped simultaneously. With this in mind, the long-term goal of our research is to improve treatment, survival, and quality of life for patients with brain tumors by improving the identification of eloquent cortex and providing actionable metrics for survival prognosis to best tailor a patient’s care. In our first Academic Industry Partnership between Washington University and Medtronic we were extremely productive in creating an integrated brain-mapping navigation technology using rs-fMRI. Specifically, we created a robust image acquisition/analysis pipeline that includes pre-processing of raw data, quality control analytics, and clinical validation demonstrating superior performance over task-based fMRI. We have also been leaders in deriving prognostic radiomic biomarkers from rs-fMRI. In this continuation, we will build on these successes. The overall objective is to create advanced rs-fMRI machine learning (ML) tools to more efficiently and accurately define functional cortex and provide preoperative prognostic metrics of survival as a comprehensive surgical/care navigation system. We have the expertise, infrastructure, and data, to advance rs- fMRI to be a powerful tool for neurosurgical decision support. The proposal entails three specific aims: 1) Advance an ML algorithm to enable more accurate and data efficient rs-fMRI brain-mapping software, 2) Create an rs-fMRI ML algorithm to preoperatively predict survival in glioblastoma (GBM) patients, and 3) Validate impact of mapping and prognostic algorithms on clinical decision making in prospective feasibility clinical trial. The expected outcome of this work will be an integrated imaging/surgical navigation technology using rs-fMRI for clinical decision support with defined performance, clinical validation, and a regulatory path for FDA clearance. Thus, this proposal is innovative because 1) the software will map networks with substantially shorter image acquisition times, thus enabling more widespread adoption and 2) provide critical pre-operative survival insights to inform surgical decisions. This work is significant because it will disseminate technology that fundamentally enhances more tailored approaches to improving patient outcomes and quality of life.

抽象的。胶质母细胞瘤(GBM)患者的长期生存与两个相互竞争的优先事项有关： 1)大体全切除和2)保留患者的功能。立体定向导航，其中脑的重建磁共振图像(MRI)用于术中实时解剖导引，已成为肿瘤切除必不可少的工具。此外，有新的见解认为，胶质瘤- 大脑功能组织的特定扰动会影响患者的生存。然而，目前的障碍是，没有FDA批准的导航系统，使外科医生能够可视化大脑的功能结构和肿瘤对大脑网络组织的影响预后。摘要静息状态功能磁共振成像(rs-fmri)已成为临床相关标测的有力工具。大脑网络和定义关键的神经胶质瘤-神经元相互作用。RS-fMRI是高效的，独立于任务的，并且可以同时映射多个休眠状态网络(RSN)。考虑到这一点，长期目标是我们研究的重点是通过改善脑瘤患者的治疗，提高患者的存活率和生活质量口才皮质的识别和为生存预后提供可操作的指标以最好地定制病人的护理。在华盛顿大学和美敦力之间的第一个学术行业合作伙伴关系中，我们在使用rs-fmri创建集成脑图导航技术方面非常有成效。具体来说，我们创建了一个强大的图像采集/分析流水线，包括原始数据的预处理、质量控制分析和临床验证表明，其性能优于基于任务的功能磁共振成像。我们也一直在从rs-fmri中获得预后放射性生物标记物的领先者。在接下来的文章中，我们将在这些基础上成功。总体目标是创建高级的rs-fmri机器学习(ML)工具，以更有效地并准确定义功能性皮质，并提供术前生存预后指标全面的手术/护理导航系统。我们拥有专业知识、基础设施和数据，可以推动- 功能磁共振成像将成为神经外科决策支持的有力工具。该提案涉及三个具体目标：1) 提出ML算法，以实现更准确和数据效率更高的RS-fMRI脑图软件，2)创建 RS-fMRI ML算法用于胶质母细胞瘤(GBM)患者的术前生存预测，以及3)验证影响前瞻性可行性临床试验中临床决策的绘图和预测算法的研究。这个这项工作的预期成果将是一种使用RS-fMRI的综合成像/外科导航技术具有明确的绩效、临床验证和FDA批准的监管路径的临床决策支持。因此，这一建议是创新的，因为1)该软件将以显著较短的图像绘制网络地图获取时间，从而实现更广泛的采用；2)提供关键的术前生存洞察力为手术决定提供信息。这项工作意义重大，因为它将从根本上传播技术加强更多量身定做的方法，以改善患者的预后和生活质量。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Structural gray matter alterations in glioblastoma and high-grade glioma-A potential biomarker of survival.

DOI：
10.1093/noajnl/vdad034
发表时间：
2023-01
期刊：
Neuro-oncology advances
影响因子：
0
作者：
通讯作者：

Resting State Functional MR Imaging of Language Function.

DOI：
10.1016/j.nic.2020.09.005
发表时间：
2021-03
期刊：
Neuroimaging clinics of North America
影响因子：
2.3
作者：
Lee JJ;Luckett P;Fakhri MM;Leuthardt EC;Shimony JS
通讯作者：
Shimony JS

Preoperative functional connectivity by magnetic resonance imaging for refractory neocortical epilepsy.

通过磁共振成像对难治性新皮质癫痫进行术前功能连接。

DOI：
10.1101/2023.01.10.23284374
发表时间：
2023
期刊：
medRxiv : the preprint server for health sciences
影响因子：
0
作者：
Johnson,EmilyA;Lee,JohnJ;Hacker,CarlD;Park,KiYun;Rustamov,Nabi;Daniel,AndyGS;Shimony,JoshuaS;Leuthardt,EricC
通讯作者：
Leuthardt,EricC

Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients.