Informatics for Integrative Brain Tumor Whole Slide Analysis

描述（由申请人提供）： 结合分子数据的数字化病理切片的高分辨率图像分析具有巨大的潜力，可以为预后和治疗方面的患者分层提供额外的信息。我们建议开发方法，分析管道和数据管理工具，使其能够系统地进行大规模的脑肿瘤组织学特征和蛋白质和基因表达模式的比较分析。 我们将开发信息模型来管理与脑肿瘤整体虚拟切片数据分析相关的信息。这些模型将捕获与患者数据、标本制备和特殊染色剂相关的背景信息，涉及组织学分类和特征的人类观察结果，算法组成、参数化和对应于分析管道的输入数据，以及算法和人类描述的分割、特征和分类。 我们将实现高性能数据库和查询支持的中间件查询，选择图像数据的子集和结果的基础上的图像和出处信息的元数据;比较功能，空间结构，从多个算法以及人类标记获得的分类;并比较统计和汇总信息的功能和分类跨多个图像数据集。 使用信息模型和中间件，我们将进行所需的分析研究，以确定图像分析导出的肿瘤信息与临床结果、基因表达类别、遗传获得和丢失以及甲基化状态之间的关系。我们将采用一种新的自动化多重量子点免疫组织化学与肽控制和定量图像分析方法来映射信号转导途径和转录网络的活动相对于肿瘤微环境使用组织学特征描述。我们将利用多变量数据融合技术，同时考虑到测量的图像特征，分子特征之间的潜在相关性和关系，以预测患者的结果。 我们将部署一个数据存储库，其中包含来自我们项目的图像、功能、分析管道、来源信息和分析结果。这个资料库将为脑肿瘤研究提供一个公开可用的资源。在这个项目中开发的所有软件和信息模型将是开源的，免费供研究使用。

DESCRIPTION (provided by applicant): High-resolution image analysis of digitized pathology slides coupled with molecular data has enormous potential to provide additional information for stratifying patients in terms of prognosis and therapy. We propose to develop methods, analytic pipelines, and data management tools that will make it feasible to systematically carry out large-scale comparative analyses of brain tumor histological features and of patterns of protein and gene expression. We will develop information models to manage information associated with analysis of brain tumor whole virtual slide data. These models will capture information about context relating to patient data, specimen preparation, and special stains, human observations involving histological classification and characteristics, algorithmic composition, parameterization and input data corresponding to analysis pipelines, and algorithm and human-described segmentations, features, and classifications. We will implement middleware for high-performance database and query support for queries that selects subsets of image data and results based on metadata on images and provenance information; that compare features, spatial structures, and classifications obtained from multiple algorithms as well as human markups; and that compare statistical and summary information on features and classifications across multiple image datasets. Using the information models and middleware, we will carry out analysis studies needed to determine the relationship between image analysis derived tumor information and clinical outcome, gene expression category, genetic gains and losses, and methylation status. We will employ a novel automated multiplex quantum dot immunohistochemistry with peptide controls and quantitative image analysis methodology to map the activity of signal transduction pathways and transcriptional networks relative to the tumor microenvironment using histology feature descriptions. We will leverage multivariate data fusion techniques to simultaneously take into account potential correlations and relationships among the measured image features, molecular signatures to predict patient outcomes. We will deploy a data repository populated with images, features, analysis pipelines, provenance information, and analytic results from our project. This repository will provide a publicly available resource for brain tumor research. All software and information models developed in this project will be open source and free for research use.