Integrating Quantitative Imaging and Biophysical Models to Predict Tumor Growth

整合定量成像和生物物理模型来预测肿瘤生长

• 批准号: 8628808
• 负责人: Thomas E Yankeelov
• 金额: $ 16.43万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8628808
• 关键词: Animals; Blood Vessels; Breast Osteosarcoma; Cancer Patient; Carmustine; Cellularity; Characteristics; Clinical; Clinical Data; Clinical Management; Clinical Trials; Communities; Data; Data Set; Development; Diffusion Magnetic Resonance Imaging; Dimensions; Early Diagnosis; Early treatment; Exploratory/Developmental Grant; Future; Glioblastoma; Goals; Healthcare; Hypoxia; Image; Implant; Individual; Investigation; Lead; Logistic Models; Magnetic Resonance Imaging; Malignant Neoplasms; Measurement; Measures; Medicine; Metabolic; Metabolism; Methods; Modeling; Molecular; Monitor; Motivation; Neoadjuvant Therapy; Organism; Pancreas; Patients; Positioning Attribute; Positron-Emission Tomography; Process; Property; Prospective Studies; Rattus; Recording of previous events; Research; Resolution; Series; Simulate; System; Testing; Time; Translating; Treatment Efficacy; Vision; Work; angiogenesis; base; biophysical model; cancer care; cell growth; clinical practice; clinically relevant; data modeling; design; high risk; imaging modality; in vitro Assay; in vivo Model; in vivo imaging; innovation; interest; mathematical model; neoplastic cell; oncology; practical application; programs; public health relevance; rectal; research study; response; success; therapy outcome; tool; treatment response; tumor; tumor growth

DESCRIPTION (provided by applicant): The vision of this program is to develop tumor forecasting methods by integrating quantitative imaging data and biophysical models of tumor growth to predict the response of individual tumors to therapy. Current mathematical models of tumor growth are limited in their practical applicability as they require input data that are extraordinarily difficult to obtain in an intact organism with any reasonable spatial resolution at even a single time point, let alone at multiple time points. Consequently, there has been very little application of such models to clinical data and virtually no incorporation into clinical trils. The motivation for integrating imaging data into mathematical models of tumor growth is that imaging can provide quantitative information noninvasively, in 3D, and at multiple time points. Measurements can be made (without disturbing the system) at the time of diagnosis and early in the course of treatment, and then these data can be modeled to predict response at the end of therapy. In this way, imaging allows models to be initialized with patient specific data. We believe we are now in the position to apply for support to perform a complete set of prospective studies appropriately designed for testing and validating two imaging based mathematical models of tumor growth and treatment response. To achieve this goal, we have identified the following two specific aims: 1. Determine the ability of dynamic contrast enhanced MRI and diffusion weighted MRI measurements of tumor vascular and cellular characteristics, respectively, obtained early in the course of therapy, to initialize the logistic model of tumor growth in order to predict final treatment response in individual animals. 2. Determine the abilit of MRI and PET measurements of tumor cellular, vascular, hypoxic, and glycolytic characteristics, obtained early in the course of therapy, to initialize a biophysical model of angiogenesis and cell growth in order to predict final treatment response in individual animals. Success in this line of investigation would allow for accurate prediction of treatment efficacy, so that ineffective therapies can be switched to potentially more effective approaches thereby enabling a practical, clinically relevant realization of personalized medicine for cancer patients.

描述（由申请人提供）：该程序的视野是通过整合定量成像数据和肿瘤生长的生物物理模型来开发肿瘤预测方法，以预测单个肿瘤对治疗的反应。 当前的肿瘤生长数学模型的实际适用性受到限制 即使是一个时间点，更不用说在多个时间点了。因此，此类模型在临床数据中的应用很少，几乎没有纳入临床三元素。将成像数据整合到肿瘤生长的数学模型中的动机是，成像可以在3D中和多个时间点非侵入性地提供定量信息。在诊断时和治疗过程中的早期可以进行测量（不干扰系统），然后可以对这些数据进行建模以预测治疗结束时的反应。这样，成像允许使用特定于患者的数据初始化模型。我们认为，我们现在可以申请支持，以适当设计用于测试和验证两个基于成像的肿瘤生长和治疗反应的数学模型的完整前瞻性研究。为了实现这一目标，我们已经确定了以下两个特定目标：1。分别在治疗过程中获得了动态对比度增强的MRI和扩散加权MRI测量，分别在治疗过程中获得了初始化肿瘤生长的逻辑模型，以预测单个动物的最终治疗反应。 2。确定在治疗过程中早期获得的肿瘤细胞，血管，低氧和糖酵解特征的MRI和PET测量值，以初始化血管生成和细胞生长的生物物理模型，以预测单个动物的最终治疗反应。在这一调查方面的成功将允许准确预测治疗疗效，因此 这种无效的疗法可以转变为潜在的更有效的方法，从而为癌症患者实现实用，临床上相关的实现。