Neuroimaging Markers for Predicting Outcome of Brain Tumor Surgery

项目1：预测脑肿瘤手术结果的神经成像标记物 摘要 手术切除是人类胶质瘤的主要治疗方法之一，越来越多的研究已经 证明了最大限度的安全切除对患者生存的好处。然而，外科手术的决定 考虑到导致神经功能障碍的风险，切除肿瘤浸润性脑组织通常是困难的。肿瘤 边界模糊的口才区域通常被切除或视为不完全切除或渗入 由于担心带来令人衰弱的赤字而无法操作。尽管如此，人们越来越认识到， 人类大脑皮层的功能解剖是可塑性的。大脑功能区域的戏剧性重组，如 作为语言皮质，已经在浸润性肿瘤(如胶质瘤)的患者中发现，这表明 事实上，肿瘤侵犯大脑功能区的患者可能是手术的候选对象。因为它一直是 研究表明，患者的无进展生存期(PFS)和总生存期(OS)与肿瘤的程度有关 在手术中，患者可能会从更积极的手术策略中受益，这种策略解释了 术后功能恢复信息，即神经可塑性。这项研究项目的重点是 开发一种基于大脑连接图像识别可塑性的智能多模式策略 运动性和/或运动性局灶性脑胶质瘤患者术后神经功能障碍的关系 语言区域。包括静息状态功能磁共振成像的三种成像方式， 将使用扩散张量成像和导航经颅磁刺激(NTMS)并将其集成到 确定新的成像标记。该项目有三个具体目标。在手术后的病人中 运动/语言区域胶质瘤，我们将基于多模式连接映射和 确定可塑性指标和神经缺陷之间的关系(目标1)，并确定 基线连接图和切除范围可用于预测可塑性(目标2)。此外，我们还将 开发基于机器学习的智能模型，以预测长期赤字和总体赤字的概率 生存(目标3)。该项目的成功可以证明开发新型多式联运的可行性 定量图像标记物预测脑肿瘤手术的临床疗效并获得可靠的初步结果 支持研究项目负责人(RPL)申请更全面的NIH R01项目的数据，该项目旨在 以进一步优化和验证新的多模式成像技术和预测模型。长期的 研究的结果将导致对手术后神经可塑性的全面理解。 并开发新的基于机器学习模型的定量神经影像临床标志物来辅助 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 在手术计划中利用可塑性，我们预计这将通过以下方式提高患者的总体存活率 在不影响患者安全或长期功能结果的情况下，增加切除范围。

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.