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：
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
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/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

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.

该程序的愿景是开发肿瘤预测方法，以预测和优化胶质母细胞瘤多形到护理标准疗法，并以肿瘤特异性进行。基本在护理脑肿瘤患者的挑战是标准放射学方法的局限性准确评估患者反应，更不用说预测。我们建议通过发展来解决这一缺点预测性，基于生物学的数学模型，结合了脑肿瘤的标志性特征生长（例如，肿瘤诱导血管生成，缺氧，坏死，增殖，入侵和对治疗的抗药性）可以使用高级，特定于主题的成像数据初始化。该项目将解决两个关键差距在护理患者中，脑癌战斗。首先，我们基于成像的数学框架对对模型预测的特定特定特征和治疗方案。第二，在大多数研究中，用于验证预测模型的地面真相是该模型是否可以预测未来区域对比度增强，对此定性MRI功能的众所周知局限性。那，虽然之前人类的研究表明了预测建模的潜力，其翻译成现实的放射学缺乏系统的临床前验证，从根本上阻碍了工具（例如，肿瘤异质性和整个脑肿瘤细胞分布）可以精确且严格受控。为了克服这些局限性，我们的目的是：1）确定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，我们将建立一个使用数学模型的家庭定量MRI数据以捕获胶质母细胞瘤的基本生物学特征。这些数据是在接受放射疗法和/或接受放射疗法的患者衍生的异种移植物中纵向获得化学疗法。然后，使用这些数据对模型家族进行校准，而新颖的模型选择策略是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将来时间点收集的MRI数据进行比较。肿瘤进展的模型预测将通过注册到清除的离体组织的3D荧光图像进行验证，该技术可以整个脑肿瘤伯恩的可视化。我们将为临床和科学界提供经过验证的胶质瘤进展的数学描述，可以可靠地预测进展和治疗跨一系列相关的神经胶质瘤信号通路的响应，可以容易地应用于临床环境。