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）：该计划的愿景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，以预测个体肿瘤对治疗的反应。 当前肿瘤生长的数学模型在其实际适用性方面受到限制，因为它们需要输入数据，这些数据非常难以在完整生物体中以任何合理的空间分辨率获得， 即使是一个时间点，更不用说在多个时间点。因此，这种模型很少应用于临床数据，实际上也没有纳入临床试验。将成像数据整合到肿瘤生长的数学模型中的动机是，成像可以在多个时间点以3D方式无创地提供定量信息。可以在诊断时和治疗过程的早期进行测量（而不干扰系统），然后可以对这些数据进行建模，以预测治疗结束时的反应。以这种方式，成像允许模型用患者特定数据初始化。我们相信，我们现在可以申请支持，以进行一整套前瞻性研究，这些研究旨在测试和验证两种基于成像的肿瘤生长和治疗反应数学模型。为了达到这个目标，我们确定了以下两个具体目标：1.分别确定在治疗过程早期获得的肿瘤血管和细胞特征的动态对比增强MRI和弥散加权MRI测量值初始化肿瘤生长的逻辑模型的能力，以预测个体动物的最终治疗反应。 2.确定在治疗过程早期获得的肿瘤细胞、血管、缺氧和糖酵解特征的MRI和PET测量值的能力，以初始化血管生成和细胞生长的生物物理模型，从而预测个体动物的最终治疗反应。这一研究路线的成功将允许准确预测治疗效果， 可以将无效的治疗转换为潜在的更有效的方法，从而能够为癌症患者实现实用的、临床相关的个性化医疗。