Informatics for Integrative Brain Tumor Whole Slide Analysis

综合脑肿瘤全玻片分析的信息学

基本信息

批准号：
8163751
负责人：
David J Foran
金额：
$ 49.64万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-07-01 至 2015-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8163751
关键词：
Accounting Algorithms Blood Vessels Brain Neoplasms Categories Cell Nucleus Cell Proliferation Cellular Morphology Characteristics Classification Clinical Computer Simulation Computer software Coupled Data Data Analyses Data Set Databases Dentistry Environment Erinaceidae Gene Expression Genetic Histology Human Hypoxia Image Image Analysis Immunohistochemistry Informatics Link Maps Measures Medicine Mesenchymal Metadata Methodology Methods Methylation Modeling Molecular Molecular Profiling Morphology Motivation Nanotechnology Necrosis New Jersey Nuclear Oligodendroglioma-Astrocytoma Outcome Pathology Pathway interactions Patients Pattern Peptides Performance Play Preparation Proto-Oncogene Proteins c-akt Quantum Dots Regulation Relative (related person)Research Research Infrastructure Research Personnel Resolution Resources Scientist Semantics Signal Transduction Pathway Site Slide Specimen Staining method Stains Stem cells Structure Students System Techniques Texture Transcriptional Activation Tumor-Derived Universities Validation Washington Work base biomedical informatics comparative data management design information model middleware neoplastic cell notch protein novel oncology open source outcome forecast protein expression repository response tool transcription factor tumor tumor progression validation studies virtual

项目摘要

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.

描述（由申请人提供）：数字化病理切片的高分辨率图像分析与分子数据相结合，具有巨大的潜力，可以为患者的预后和治疗分层提供额外信息。我们建议开发方法、分析流程和数据管理工具，使系统地对脑肿瘤组织学特征以及蛋白质和基因表达模式进行大规模比较分析成为可能。我们将开发信息模型来管理与脑肿瘤整个虚拟载玻片数据分析相关的信息。这些模型将捕获与患者数据、样本制备和特殊染色相关的上下文信息、涉及组织学分类和特征的人类观察、与分析管道相对应的算法组成、参数化和输入数据，以及算法和人类描述的分割、特征和数据。分类。我们将实现高性能数据库中间件和查询支持，以根据图像元数据和出处信息选择图像数据和结果子集；比较从多种算法以及人工标记获得的特征、空间结构和分类；并比较多个图像数据集的特征和分类的统计和摘要信息。利用信息模型和中间件，我们将进行必要的分析研究，以确定图像分析得出的肿瘤信息与临床结果、基因表达类别、遗传增益和丢失以及甲基化状态之间的关系。我们将采用一种新颖的自动化多重量子点免疫组织化学，具有肽控制和定量图像分析方法，使用组织学特征描述来绘制相对于肿瘤微环境的信号转导途径和转录网络的活性。我们将利用多变量数据融合技术，同时考虑测量的图像特征、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，其中填充了项目的图像、功能、分析管道、来源信息和分析结果。该存储库将为脑肿瘤研究提供公开可用的资源。该项目开发的所有软件和信息模型都将开源并免费供研究使用。