Interpreting limits to nanoparticle delivery in high-stroma low-perfusion tumors

4.4.7项目摘要/摘要 这项工作的中心主题是关键的空间模式存在于高度耐药的癌症间质和 血管密度会固有地抑制更大的纳米颗粒对癌症的渗透，而这些 表型可以在体内成像。我们将使用体内诊断成像，结合体外 在胰腺癌中测试这一点的分析也是众所周知的药物渗透限制。 具体地说，我们将量化纳米颗粒在胰腺癌中的渗透，胰腺癌具有高间质含量。 血管密度低。药效的分析和预测将通过以下方式进行量化 用傅立叶空间频率分析进行活体和体外图像的方法学关联。 我们将确定这些肿瘤微结构的特征空间模式，这些模式呈现为 纳米颗粒传输的障碍，通过体内/体外研究进行分析。我们已经看到，这些 在高场磁共振成像(HF-MRI)扫描和 正在进行的纳米颗粒计划内的肿瘤的微型计算机断层扫描(UCT)扫描，网址为 DHMC。此项目的范围是对正在进行的图像进行二级分析 在这些项目中产生的，有两个具体目标。1)我们将直接关联纳米颗粒 傅立叶空间对活体图像傅里叶空间频率的穿透和分布 我们在频率分析方面表现出了专业知识。活体图像将由 将其与治疗后纳米颗粒分布的组织切片相关联。肿瘤将会是 分为对特定纳米颗粒配方的高渗透性或低渗透性两个级别(至 量化所提供的药剂的量)，以及具有高或低各向同性(以量化 代理)。2)我们将把这一特征形态分析应用于治疗前、术前 HF-MRI、UCT图像，分析其作为潜在诊断分类的价值。我们将使用一种支持 向量机分析预测未知肿瘤的渗透性和各向同性，并验证我们的 结果与实验结果相比较。迭代策略将优化 方法，并用于区分好的和差的分类器的特征光谱。 这项研究将使用我们在期间设计的独特软件系统进行 初步研究，并将部署在一个可以与医院集成的分析平台上- 基于DICOM和虚拟病理环境，允许临床研究人员计划佐剂 提高纳米颗粒疗效的疗法。现在有数百种高质量扫描可供选择 分析，将在供资的第一年内进行处理和报告。到了第二年， 已建立的系统预计能够在扫描后几分钟内分析图像。这些 分析方法将为我们提供所需的关键背景，以促进我们对 纳米颗粒体内递送，以及在介入性未来工作中中断运输障碍的测试方法。

4.4.7 Project Summary/Abstract The central theme in this work is that critical spatial patterns exist in highly resistant cancer stroma and vascular density that inherently inhibit larger nanoparticle penetration into cancer, and that these phenotypes can be imaged in vivo. We will use in vivo diagnostic imaging, combined with ex vivo analysis to test this in pancreatic cancer, which has as well known drug penetration limitation. Specifically, we will quantify nanoparticle penetration in pancreas cancer, which has high stroma content and low vascular density. The analysis and prediction of efficacy will be quantitatively developed by methodological correlation of in-vivo and ex vivo images using Fourier spatial frequency analysis. We will determine the characteristic spatial patterns of these tumor microstructures that present as barriers to nanoparticle transport, as assayed through in vivo/ex vivo studies. We have seen that these characteristic spectral features appear in high-field magnetic resonance imaging (HF-MRI) scans and micro-Computed Tomography (uCT) scans of tumors imaged within the ongoing nanoparticle project at DHMC. The scope of this project is to conduct a secondary analysis on the images that are being produced within these projects, with two specific aims. 1) We will directly correlate nanoparticle penetration and distribution to the Fourier spatial frequencies found in in vivo images by Fourier spatial frequency analysis in which we have demonstrated expertise. The in vivo images will be analyzed by correlating them with histological sections of nanoparticle distribution post-treatment. Tumors will be classified on two levels as either a high or low permeability to a specific nanoparticle formulation (to quantify the amount of agent delivered), and as having high or low isotropy (to quantify the dispersion of the agent). 2) We will then apply this characteristic morphology analysis to pre-treatment, pre-operative HF-MRI, uCT images, and analyze their value as a potential diagnostic classifier. We will use a Support Vector Machine Analysis to predict the permeability and isotropy of unknown tumors, and validate our results against experimental outcomes. An iterative strategy will optimize the predictive power of the method, and be used to distinguish between characteristic spectra that are good and bad classifiers. The research will be produced using the unique software systems that we have designed during preliminary studies, and will be deployed on an analysis platform that can be integrated with the hospital- based DICOM and virtual pathology environment to allow clinical investigators to plan adjuvant therapies to promote nanoparticle efficacy. Several hundred high-quality scans are now available for analysis, which will be processed and reported on within the first year of funding. By year two, the established system is projected to be able to analyze images within a few minutes post-scan. These analysis methods will give us the key background needed to advance our fundamental understanding of nanoparticle in-vivo delivery, and test ways to interrupt transport barriers in interventional future work.