MRI Diffusion in Tumors using Oscillating Gradients

使用振荡梯度进行肿瘤 MRI 扩散

基本信息

批准号：
10312766
负责人：
Junzhong Xu
金额：
$ 29.42万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-03-15 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10312766
关键词：
Affect Aftercare Animal Cancer Model Animal Model Animals Apoptosis Autophagocytosis BT 474 Biopsy Breast Cancer Model Breast Cancer Patient Breast Cancer therapy Cancer Patient Cell Culture Techniques Cell Cycle Cell Density Cell Membrane Permeability Cell Size Cell division Cell model Cells Cellular Morphology Clinical Clinical Trials Computer Simulation Data Data Analyses Development Diffusion Diffusion Magnetic Resonance Imaging Dimensions Doxorubicin Drug Targeting Evaluation Funding G2 Phase Arrest Gefitinib Guidelines Histology Human Image Image Analysis Imaging Techniques MCF7 cell MDA MB 231 Magnetic Resonance Imaging Malignant Neoplasms Mammary Neoplasms Maps Measurement Measures Methods Mitosis Mitotic Mus Neoadjuvant Therapy Operative Surgical Procedures Paclitaxel Pathologic Patients Performance Phase Physical condensation Play Property Regimen Role S phase Solid Neoplasm Spectrum Analysis Subcellular structure Techniques Testing Therapeutic Time Tissues Toxic effect Transgenic Mice Translating Treatment Efficacy Treatment Protocols Tumor Volume Water Work base breast imaging cancer care cell dimension chemotherapy clinical application clinically translatable imaging biomarker improved in vivo in vivo imaging innovation irradiation malignant breast neoplasm mouse model neoplastic cell new therapeutic target novel optimal treatments oscillating gradient spin echo quantitative imaging response side effect translation to humans treatment response tumor water diffusion

项目摘要

Abstract / Summary This revised competitive renewal seeks to extend the technical developments of the previous funding period to validate and apply a novel diffusion-based MR imaging technique, quantitative temporal diffusion spectroscopy (qTDS), that provides unique information on tissue microstructure and in particular can reveal early changes in tumors after treatment. In the previous cycle we developed this innovative method and showed it is a sensitive indicator of changes in cell dimensions and tissue microstructure such as those that occur with cell division and during apoptosis, before frank changes occur in cell density or tumor volume. As such, qTDS has considerable potential for assessing whether specific treatment regimens are working, and so may inform the selection of optimal therapies for patients and the reduction of avoidable side-effects. QTDS is based on measurements of water diffusion rates over different time scales corresponding to different spatial dimensions. We have previously shown it can detect changes in intracellular structure and cell sizes and density, in cell cultures and in animal models, early in the course of a treatment and without some of the confounding factors that affect other diffusion techniques, such as changes in cell membrane permeability. We have performed theoretical analyses, computer simulations, and cell and in vivo animal studies, to understand the factors that affect qTDS measurements, and have implemented the first practical qTDS acquisitions on a human 3T scanner. In the current proposal we aim to extend our previous work and use qTDS as an in vivo imaging technique for non-invasive characterization of specific cellular changes which are currently assessable only via invasive biopsy. We propose to validate qTDS in cell and animal models of cancer, and determine whether qTDS is capable of detecting treatment-induced cell size changes early in specific therapeutic regimens. We also propose to translate qTDS clinically by demonstrating its performance in predicting neoadjuvant treatment response in breast cancer. We hypothesize that qTDS is capable of characterizing the distinct cellular changes associated with treatment-induced apoptosis, thereby providing an innovative and unique means of assessing tumor response at an early stage of therapy. Our specific aims are: [i] in a transgenic mouse model of breast cancer, we will quantitatively map tumor cell size and density in vivo, and validate the qTDS derived parameters on a voxel by voxel basis using using quantitative, co-registered histology: [ii] in mouse models of breast cancer treated by different targeted drugs, we will evaluate qTDS as an imaging biomarker capable of detecting treatment-induced apoptosis and predicting treatment efficacy early during therapy: [iii] In human breast cancer patients, we will evaluate qTDS as an imaging biomarker for assessing breast tumor early response to neoadjuvant chemotherapy and predicting treatment efficacy after the first and subsequent cycles of treatment by correlating imaging data with clinical and pathological responses. The proposed qTDS method has the potential to measure cell size changes in vivo and improve the assessment of treatment response and thereby contribute to personalized clinical cancer care.

摘要/总结此次修订后的竞争性续展旨在扩展先前资助的技术发展验证和应用新型基于扩散的 MR 成像技术（定量时间扩散）的时期光谱（qTDS），提供有关组织微观结构的独特信息，特别是可以揭示治疗后肿瘤的早期变化。在上一个周期中，我们开发了这种创新方法，表明它是细胞尺寸和组织微观结构变化的敏感指标，例如发生在细胞分裂和细胞凋亡期间，在细胞密度或肿瘤体积发生明显变化之前。作为因此，qTDS 在评估特定治疗方案是否有效方面具有相当大的潜力，因此可以为患者选择最佳疗法和减少可避免的副作用提供信息。 QTDS 是基于不同时间尺度对应于不同空间的水扩散速率的测量方面。我们之前已经证明它可以检测细胞内结构和细胞大小的变化，密度，在细胞培养物和动物模型中，在治疗过程的早期并且没有一些影响其他扩散技术的混杂因素，例如细胞膜渗透性的变化。我们进行了理论分析、计算机模拟以及细胞和体内动物研究，以了解影响 qTDS 测量的因素，并已在人体3T扫描仪。在当前的提案中，我们的目标是扩展我们之前的工作并使用 qTDS 作为体内用于非侵入性表征特定细胞变化的成像技术，目前只能通过侵入性活检进行评估。我们建议在癌症细胞和动物模型中验证 qTDS，以及确定 qTDS 是否能够在特定的早期检测治疗引起的细胞大小变化治疗方案。我们还建议通过展示 qTDS 在以下方面的表现来将其转化为临床：预测乳腺癌新辅助治疗反应。我们假设 qTDS 能够表征与治疗诱导的细胞凋亡相关的独特细胞变化，从而提供在治疗的早期阶段评估肿瘤反应的创新和独特的方法。我们的具体目标是： [i] 在乳腺癌转基因小鼠模型中，我们将定量绘制体内肿瘤细胞大小和密度图，并使用定量、共同配准逐个体素地验证 qTDS 导出参数组织学：[ii]在不同靶向药物治疗的乳腺癌小鼠模型中，我们将评估 qTDS 为能够检测治疗诱导的细胞凋亡并预测治疗效果的成像生物标志物治疗早期：[iii] 在人类乳腺癌患者中，我们将评估 qTDS 作为成像生物标志物评估乳腺肿瘤对新辅助化疗的早期反应并预测治疗后的疗效通过将影像数据与临床和病理相关联来确定第一个和后续治疗周期回应。所提出的 qTDS 方法有可能测量体内细胞大小的变化并改善评估治疗反应，从而有助于个性化的临床癌症护理。