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

基本信息

批准号：
10367617
负责人：
Christopher Chad Quarles
金额：
$ 68.44万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
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/10367617
关键词：
3-Dimensional Address Area Biological Biological Process Blood Vessels Brain Brain Neoplasms Cellularity Characteristics Clinical Communities Data Disease Evolution Failure Family Future Genetic Glioblastoma Glioma Goals Human Hypoxia Image Individual Infrastructure Magnetic Resonance Imaging Malignant neoplasm of brain Mathematics Methods Modeling Necrosis Patient Care Patients Positioning Attribute Prediction of Response to Therapy Protocols documentation Radiation Radiation therapy Radiology Specialty Resolution Signal Pathway Techniques Testing Time Tissues Translations Treatment Protocols Tumor Burden Validation Vision Visualization angiogenesis base 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

项目摘要

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来预测和优化肿瘤的反应多形性胶质母细胞瘤转为标准护理疗法，并且是在肿瘤特异性的基础上进行的。脑肿瘤患者护理中的挑战是标准放射线照相方法的局限性我们建议通过开发来解决这一缺点，更不用说预测患者的反应了。基于生物学的预测数学模型，结合了脑肿瘤的标志特征生长（例如肿瘤诱导的血管生成、缺氧、坏死、增殖、侵袭和对治疗的抵抗）可以使用先进的、特定于主题的成像数据进行初始化，该项目将解决两个关键差距。首先，我们基于成像的数学框架考虑了脑癌患者的护理。其次，在大多数研究中，模型预测的受试者特定特征和治疗方案。用于验证预测模型的基本事实是该模型是否可以预测未来的区域对比度增强，尽管这种定性 MRI 功能存在众所周知的局限性。人类研究已经证明了预测模型的潜力，将其转化为现实的放射学模型由于缺乏系统的临床前验证，该工具从根本上受到阻碍，其中关键的肿瘤特征（例如，肿瘤异质性和全脑肿瘤细胞分布）可以被精确地了解和严格地为了克服这些限制，我们的目标是：1）建立肿瘤特异性模型的准确性预测时空进展，2) 建立肿瘤特异性模型的准确性来预测在实验上，我们将构建一系列采用的数学模型。 MRI 数据捕捉胶质母细胞瘤的基本生物学特征。在未经治疗或正在接受放射治疗的患者来源的异种移植物中纵向采集和/或然后使用这些数据校准模型家族，并制定新的模型选择策略。用于选择最简约的模型来预测每个肿瘤的时空演化然后将其与未来时间点收集的 MRI 数据进行比较，模型预测肿瘤进展。将通过配准已清除的离体组织的 3D 荧光图像进行验证，该技术使我们将为临床和科学界提供全脑肿瘤负荷的可视化。神经胶质瘤进展的经过验证的数学描述可以可靠地预测进展和治疗跨一系列相关神经胶质瘤信号通路的反应，并且可以很容易地应用于临床环境。