Imaging-based tumor forecasting to predict brain tumor progression and response to therapy

基于成像的肿瘤预测可预测脑肿瘤进展和治疗反应

• 批准号: 10706461
• 负责人: Christopher Chad Quarles
• 金额: $ 64.7万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10706461
• 关键词: 3-Dimensional; Address; Area; Biological; Biological Process; Blood Vessels; Brain; Brain Neoplasms; Calibration; Cellularity; Characteristics; Clinical; Communities; Data; Disease; Evolution; Failure; Family; Future; Genetic; Glioblastoma; Glioma; Goals; Human; Hypoxia; Image; Individual; Infrastructure; Invaded; Magnetic Resonance Imaging; Malignant neoplasm of brain; Mathematics; Methods; Modeling; Necrosis; Patient Care; Patients; Positioning Attribute; Prediction of Response to Therapy; Proliferating; Protocols documentation; Radiation; Radiation therapy; Radiology Specialty; Resolution; Signal Pathway; Techniques; Testing; Time; Tissues; Translations; Treatment Protocols; Tumor Burden; Validation; Vision; Visualization; angiogenesis; chemotherapy; clinical application; contrast enhanced; fluorescence imaging; in vivo; individual patient; individual response; mathematical model; neoplastic cell; novel; optical imaging; patient derived xenograft model; patient response; pre-clinical; predicting response; predictive modeling; programs; radiological imaging; response; spatiotemporal; standard of care; success; temozolomide; therapy resistant; tool; treatment optimization; treatment response; tumor; tumor growth; tumor heterogeneity; tumor progression

The vision for this program is to develop tumor forecasting methods to predict and optimize the response of glioblastoma multiforme to standard-of-care therapies—and do so on a tumor-specific basis. A fundamental challenge in the care of patients with brain tumors is the limitation of standard radiographic methods to accurately evaluate, let alone predict, patient response. We propose to address this shortcoming by developing predictive, biologically-based mathematical models that incorporate the hallmark characteristics of brain tumor growth (e.g., tumor induced angiogenesis, hypoxia, necrosis, proliferation, invasion, and resistance to therapy) that can be initialized using advanced, subject-specific imaging data. This project will address two critical gaps in the care of patients battling brain cancer. First, our imaging-based, mathematical framework accounts for subject-specific characteristics and treatment regimens on model predictions. Second, in most studies, the ground truth used for validation of the predictive model is whether the model can predict future regional contrast enhancement, despite the well-known limitations of this qualitative MRI feature. Thus, while prior human studies have demonstrated the potential of predictive modeling, its translation into a realistic radiologic tool is fundamentally hindered by lack of systematic, pre-clinical validation where critical tumor characteristics (e.g., tumor heterogeneity and whole brain tumor cell distribution) can be precisely known and rigorously controlled. To overcome these limitations, we aim to: 1) establish the accuracy of tumor-specific modeling to predict spatiotemporal progression and 2) establish the accuracy of tumor-specific modeling to predict therapeutic response. Experimentally, we will construct a family of mathematical models that employ quantitative MRI data to capture the fundamental biological features of glioblastoma. These data are longitudinally acquired in patient derived xenografts that are treatment naïve or undergoing radiotherapy and/or chemotherapy. The model family is then calibrated with these data and a novel model selection strategy is employed to choose the most parsimonious model for predicting the spatio-temporal evolution of each tumor which is then compared to MRI data collected at future time points. Model predictions of tumor progression will be validated via registration to 3D fluorescent images of cleared ex vivo tissue, a technique that enables visualization of whole brain tumor burden. We will provide the clinical and scientific community with a validated mathematical description of glioma progression that can reliably predict progression and therapy response across a range of relevant glioma signaling pathways and can be readily applied to the clinical setting.

该计划的愿景是开发肿瘤预测方法，以预测和优化肿瘤的反应。 多形性胶质母细胞瘤的标准治疗，并在肿瘤特异性的基础上这样做。一项基本 脑肿瘤患者护理中的挑战是标准放射学方法的局限性， 更不用说预测病人的反应了我们建议通过开发 预测性的、基于生物学的数学模型， 生长（例如，肿瘤诱导的血管生成、缺氧、坏死、增殖、侵袭和对治疗的抗性） 可以使用先进的特定对象成像数据进行初始化。该项目将解决两个关键差距 在治疗脑癌患者方面。首先，我们基于成像的数学框架解释了 受试者特异性特征和治疗方案对模型预测的影响。其次，在大多数研究中， 用于验证预测模型的基础事实是该模型是否可以预测未来的区域性 对比度增强，尽管这种定性MRI特征具有众所周知的局限性。因此，虽然先前 人类研究已经证明了预测建模的潜力，其转化为现实的放射学 该工具从根本上受到缺乏系统的临床前验证的阻碍， (e.g.,肿瘤异质性和全脑肿瘤细胞分布）可以精确地知道并且严格地 控制。为了克服这些局限性，我们的目标是：1）建立肿瘤特异性建模的准确性， 预测时空进展和2）建立肿瘤特异性建模的准确性，以预测 治疗反应。在实验上，我们将构建一系列数学模型， 定量MRI数据来捕获胶质母细胞瘤的基本生物学特征。这些数据 在未经治疗或正在接受放疗的患者来源的异种移植物中纵向获得，和/或 化疗然后用这些数据校准模型族，并提出一种新的模型选择策略。 用于选择最简约的模型来预测每个肿瘤的时空演变 然后将其与在未来时间点收集的MRI数据进行比较。肿瘤进展的模型预测 将通过与已清除的离体组织的3D荧光图像配准进行验证，该技术 全脑肿瘤负荷的可视化。我们将为临床和科学界提供一个 胶质瘤进展的有效数学描述，可以可靠地预测进展和治疗 该方法可以在一系列相关的胶质瘤信号传导途径中产生应答，并且可以容易地应用于临床环境。