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

该计划的愿景是开发肿瘤预测方法，以预测和优化对 多形性胶质母细胞瘤可以采用标准的治疗方法--而且是在肿瘤特异性的基础上进行的。一项基本原则 脑肿瘤患者护理中的挑战是标准放射学方法的局限性 准确地评估患者的反应，更不用说预测了。我们建议通过开发以下方法来解决此缺陷 基于生物学的预测性数学模型，结合了脑瘤的特征 生长(例如，肿瘤导致的血管生成、缺氧、坏死、增殖、侵袭和对治疗的抵抗) 可以使用先进的、特定于对象的成像数据进行初始化。该项目将解决两个关键差距 在与脑癌作斗争的病人的护理中。首先，我们以成像为基础的数学框架解释了 基于模型预测的特定对象的特征和治疗方案。其次，在大多数研究中， 用于验证预测模型的基本事实是该模型是否能够预测未来区域 对比度增强，尽管这一定性MRI特征有众所周知的局限性。因此，虽然之前 人体研究已经证明了预测性建模的潜力，它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，在这些验证中，关键的肿瘤特征 (例如，肿瘤的异质性和全脑肿瘤细胞的分布)可以精确和严格地知道 控制住了。为了克服这些限制，我们的目标是：1)建立肿瘤特异性建模的准确性，以 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，这些模型使用 定量的MRI数据，以捕捉胶质母细胞瘤的基本生物学特征。这些数据是 在接受单纯治疗或接受放射治疗和/或治疗的患者来源的异种移植物中纵向获得 化疗。然后用这些数据校准模型族，并且一种新的模型选择策略是 用来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 将通过配准透明的体外组织的3D荧光图像进行验证，这是一项能够 全脑肿瘤负荷的可视化。我们将为临床和科学界提供一个 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 通过一系列相关的神经胶质瘤信号通路进行反应，可以很容易地应用于临床环境。

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.