Neuroimaging Markers for Predicting Outcome of Brain Tumor Surgery

项目1：预测脑肿瘤手术结果的神经影像学标志物 摘要 手术切除是人类胶质瘤的主要治疗方法之一，越来越多的研究表明， 证明了最大安全切除对患者生存的益处。然而，外科手术的决定 考虑到诱发神经缺陷的风险，切除肿瘤浸润的脑组织通常是困难的。肿瘤 边界不清，侵入和/或浸润功能区的肿瘤通常不能完全切除或视为 因为害怕造成虚弱的缺陷而不能手术。然而，人们越来越认识到， 人类大脑皮层的功能解剖学是可塑的。大脑功能区域的戏剧性重组，例如 作为语言皮质，在神经胶质瘤等浸润性肿瘤患者中发现了这种现象，这表明 肿瘤侵入功能性脑区的患者实际上可能是手术候选者。因为它已被 研究表明，患者的无进展生存期（PFS）和总生存期（OS）与疾病进展程度相关。 手术切除，患者可能受益于更积极的手术策略， 手术后功能恢复的信息，即神经可塑性。该研究项目的重点是 开发一种智能和多模式的策略，用于基于大脑连接的图像来识别可塑性， 与涉及运动和/或神经功能障碍的局灶性脑胶质瘤患者手术后的神经功能缺损相关 语言区域。三种成像模式，包括静息态功能磁共振成像， 将使用并整合弥散张量成像和导航经颅磁刺激（nTMS）， 识别新的成像标记。该项目有三个具体目标。在手术后的患者中， 运动/语言区胶质瘤，我们将根据多模态连接映射确定可塑性指标， 确定可塑性指标和神经功能缺损之间的关系（目标1），并确定是否 基线连接图和切除范围可用于预测可塑性（目标2）。此外，我们将 开发一个智能的、基于机器学习的模型，预测长期赤字的概率， 生存（目标3）。该项目的成功可以证明开发一种新型的多模态基 定量图像标记物用于预测脑肿瘤手术的临床结果并获得可靠的初步结果 数据支持研究项目负责人（RPL）申请一个更全面的NIH R 01项目， 进一步优化和验证新的多模态成像技术和预测模型。长期 这项研究的成果将使人们对手术后的神经可塑性有一个全面的了解 并基于机器学习模型开发新的定量神经成像临床标记物， 预测患者的PFS或OS。从这个项目中获得的神经可塑性知识将有助于 将可塑性用于手术计划，我们预计这将提高患者的总体生存率， 增加切除范围，而不影响患者安全或长期功能结局。

Project 1: Neuroimaging Markers for Predicting Outcome of Brain Tumor Surgery ABSTRACT Surgical resection is one of the primary treatments for human gliomas, and a growing number of studies have demonstrated the benefits of maximal safe resection for patient survival. However, the decision of surgical resection of tumor-infiltrated brain tissue is often difficult given the risk of inducing neurological deficits. Tumors with ill-defined boundaries that invade and/or infiltrate eloquent areas are often incompletely resected or deemed inoperable for fear of conferring a debilitating deficit. Nonetheless, it is increasingly acknowledged that the functional anatomy of the human neocortex is plastic. Dramatic reorganization of functional brain regions, such as language cortices, have been seen in patients with infiltrating tumors such as gliomas, suggesting such patients with tumors invading functional brain areas may in fact be surgical candidates. Because it has been demonstrated that progression free survival (PFS) and overall survival (OS) of patients correlate with extent of resection in surgery, patients may benefit from a more aggressive surgical strategy that accounts for the information of functional recovery after surgery, i.e. neural plasticity. The focus of this research project is to develop an intelligent and multimodal strategy for identifying plasticity based on images of brain connectivity that relates to the neurological deficits after surgery in patients with focal brain gliomas involving motor and/or language regions. Three imaging modalities including resting-state functional magnetic resonance imaging, diffusion tensor imaging and navigated transcranial magnetic stimulation (nTMS) will be used and integrated to identify new imaging markers. The project has three Specific Aims. In patients following surgery for motor/speech area gliomas, we will identify plasticity metrics based on multimodal connectivity mapping and determine the relationship between plasticity metrics and neurological deficits (Aim 1) and determine whether baseline connectivity maps and extent of resection can be used to predict plasticity (Aim 2). In addition, we will develop an intelligent, machine learning based model that predicts the probability of long-term deficits and overall survival (Aim 3). The success of this project can demonstrate feasibility of developing a novel multimodal-based quantitative image marker to predict clinical outcome of brain tumor surgery and acquire the solid preliminary data to support the research project leader (RPL) to apply for a more comprehensive NIH R01 project that aims to further optimize and validate the new multimodality imaging technology and prediction model. The long-term outcomes of the research effort will lead to a comprehensive understanding of neural plasticity after surgery and develop new quantitative neuroimaging clinical markers based on the machine learning models to assist prediction of PFS or OS of patients. Knowledge of the neural plasticity obtained from this project will serve to leverage the plasticity into surgery planning, which we expect will improve overall survival of patients by increasing the extent of resection, without compromising patient safety or long-term functional outcomes.