Neuroimaging Markers for Predicting Outcome of Brain Tumor Surgery

用于预测脑肿瘤手术结果的神经影像标记物

• 批准号: 10573283
• 负责人: Han Yuan
• 金额: $ 22.88万
• 依托单位: UNIVERSITY OF OKLAHOMA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10573283
• 关键词: Anatomy; Area; Artificial Intelligence; Brain; Brain Neoplasms; Brain imaging; Brain region; Centers of Research Excellence; Clinical; Clinical Markers; Computer software; Custom; Dangerousness; Data; Decision Making; Diffusion Magnetic Resonance Imaging; Excision; Fright; Functional Magnetic Resonance Imaging; Glioma; Goals; Human; Image; Imaging technology; Infiltration; Intelligence; Invaded; Knowledge; Language; Longitudinal Studies; Longterm Follow-up; Maps; Medical Imaging; Medical center; Modality; Motor; Multimodal Imaging; Neocortex; Neurologic; Neurologic Deficit; Neuronal Plasticity; Oklahoma; Operative Surgical Procedures; Outcome; Outcomes Research; Patients; Performance; Postoperative Period; Probability; Progression-Free Survivals; Records; Recovery; Recovery of Function; Research; Research Project Grants; Research Support; Resected; Rest; Risk; Solid; Speech; Structure; Survival Rate; Testing; Time; Training; Transcranial magnetic stimulation; Tumor Cell Invasion; United States National Institutes of Health; Visit; brain tissue; cancer imaging; design; follow-up; functional outcomes; graphical user interface; imaging biomarker; imaging modality; improved; individual patient; innovation; machine learning algorithm; machine learning model; machine learning prediction; multimodal neuroimaging; multimodality; neocortical; neuroimaging; neuroimaging marker; neurological recovery; neurosurgery; novel; novel strategies; outcome prediction; patient prognosis; patient safety; postoperative recovery; predict clinical outcome; predictive modeling; quantitative imaging; success; support tools; surgery outcome; technology development; tool; translational cancer research; tumor

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.

项目1：用于预测脑肿瘤手术结果的神经影像学标记 抽象的 手术切除是人神经膜瘤的主要治疗方法之一，越来越多的研究已 证明了最大安全切除对患者生存的好处。但是，手术的决定 考虑到诱发神经系统缺陷的风险，肿瘤浸润的脑组织的切除通常很难。肿瘤 侵入和/或渗透雄辩区域的界限通常不完全解决或被视为 由于害怕赋予令人衰弱的赤字而无法使用。尽管如此，越来越多地承认 人类新皮层的功能解剖结构是塑料。功能大脑区域的戏剧性重组， 作为语言皮质，在浸润性肿瘤（如神经胶质瘤）的患者中已经看到，这表明这种 肿瘤患者入侵功能性大脑区域实际上可能是手术候选者。因为已经 证明患者的无进展生存率（PFS）和总体存活率（OS）与 手术切除，患者可能会受益于更具侵略性的外科手术策略 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 制定一种智能和多模式策略，以根据大脑连通性图像来识别可塑性 与涉及运动和/或 语言区域。三种成像方式，包括静止状态功能磁共振成像， 扩散张量成像和导航的经颅磁刺激（NTMS）将被使用并整合到 识别新成像标记。该项目具有三个特定的目标。手术后的患者 电机/语音区域神经膜，我们将基于多模式连接映射和 确定可塑性指标与神经系统缺陷之间的关系（AIM 1），并确定是否是否 基线连通图和切除程度可用于预测可塑性（AIM 2）。此外，我们将 开发一个基于机器的智能，基于机器的模型，可预测长期赤字和整体的概率 生存（目标3）。该项目的成功可以证明开发新型多模式的可行性 定量图像标记以预测脑肿瘤手术的临床结果并获得固体初步 支持研究项目负责人（RPL）的数据，以申请更全面的NIH R01项目 进一步优化和验证新的多模式成像技术和预测模型。长期 研究工作的结果将导致对手术后神经可塑性的全面理解 并根据机器学习模型开发新的定量神经影像学标记 PFS或患者OS的预测。了解从该项目获得的神经可塑性的了解将有助于 利用可塑性进入手术计划，我们预计这将通过 增加切除程度，而不会损害患者安全或长期功能结果。