Advancing Neurosurgical Neuronavigation Using Resting State MRI and Machine Learning

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相关： 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）用于实时术中解剖 引导已经成为肿瘤切除的重要工具。此外，有新的见解，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 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， 预后静息态功能磁共振成像（rs-fMRI）已成为一个强大的工具，映射临床相关 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静息状态网络（RSN）。考虑到这一点，长期目标 我们研究的一个目的是通过改善脑肿瘤患者的治疗、生存和生活质量， 识别功能区皮质并提供生存预后的可行指标，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次学术行业合作中， 在使用rs-fMRI创建集成脑映射导航技术方面非常富有成效。具体地说， 我们创建了一个强大的图像采集/分析管道，包括原始数据的预处理、质量控制 分析和临床验证，证明优于基于任务的功能磁共振成像的上级性能。我们也一直 在从rs-fMRI获得预后放射性生物标志物方面的领导者。在本续中，我们将建立在这些 成功。总体目标是创建先进的rs-fMRI机器学习（ML）工具， 准确定义功能皮质，并提供术前生存预后指标， 综合手术/护理导航系统。我们有专业知识，基础设施和数据，来推进RS- 功能磁共振成像是一个强大的工具，神经外科决策支持。该提案包含三个具体目标： 推进ML算法，以实现更准确和数据高效的rs-fMRI脑映射软件，2）创建 rs-fMRI ML算法用于术前预测胶质母细胞瘤（GBM）患者的生存率，以及3）肿瘤影响 在前瞻性可行性临床试验中，映射和预后算法对临床决策的影响。的 这项工作的预期成果将是一个集成的成像/手术导航技术，使用rs-fMRI， 临床决策支持，具有明确的性能、临床验证和FDA批准的监管路径。 因此，该提议是创新的，因为1）该软件将用实质上更短的图像映射网络 采集时间，从而能够更广泛地采用; 2）提供关键的术前生存见解 为手术决策提供信息这项工作意义重大，因为它将传播从根本上 增强了更有针对性的方法，以改善患者的治疗效果和生活质量。

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.