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级相关： 1）总切除术和2）保存患者的功能。立体定向导航，其中大脑的重建磁共振图像（MRI）用于实时术中解剖指导已成为肿瘤切除的必要工具。此外，有新兴的见解是神经胶质瘤 - 大脑功能组织的特定扰动会影响患者的生存。但是，当前的障碍是没有FDA批准的导航系统可以使外科医生可视化大脑的功能架构以及肿瘤对大脑网络组织的影响预后。静止状态功能MRI（RS-FMRI）已成为绘制临床相关的强大工具大脑网络并定义关键的神经胶质瘤 - 神经元相互作用。 RS-FMI高效，任务独立，可以简单地映射多个静止状态网络（RSN）。考虑到这一点，长期目标我们的研究是通过改善脑肿瘤患者的治疗，生存和生活质量识别雄辩的皮质并提供可行的指标来生存预后，以最佳地量身定制病人的护理。在华盛顿大学和Medtronic之间的第一个学术行业伙伴关系中，我们是使用RS-FMRI创建集成的大脑映射导航技术，极其有效。具体来说，我们创建了一个强大的图像采集/分析管道，其中包括原始数据的预处理，质量控制分析和临床验证表明，与基于任务的功能磁共振成像相比，表现出色。我们也去过从RS-FMRI中得出预后的放射线生物标志物的领导者。在这种延续中，我们将以这些为基础成功。总体目标是创建高级RS-FMRI机器学习（ML）工具以更有效并准确定义功能性皮质，并提供术前预后指标作为一种全面的外科/护理导航系统。我们拥有专业知识，基础架构和数据，以提高Rs- fMRI成为神经外科决策支持的强大工具。该提案需要三个具体目标：1）推进ML算法以启用更准确和数据有效的RS-FMRI脑映射软件，2）创建一种RS-FMRI ML算法，用于术前预测胶质母细胞瘤（GBM）患者的存活率，3）验证撞击预期可行性临床试验中临床决策的映射和预后算法。这项工作的预期结果将是使用RS-FMRI的集成成像/外科导航技术临床决策支持，具有定义的性能，临床验证和FDA清除的调节路径。那就是该提案具有创新性获取时间，从而实现更多的宽度采用和2）提供关键的术前生存见解为手术决定提供信息。这项工作很重要，因为它将传播从根本上增强了更多量身定制的方法来改善患者的结果和生活质量。

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.