Collaborative Systems for Analyzing Tissue Microarrays

描述（由申请人提供）：组织微阵列技术在减少与癌症生物学、肿瘤学和药物发现进行调查研究相关的时间和成本方面具有巨大潜力。TMA使构建一个精心规划的阵列成为可能，这样，只需使用几微升抗体，就可以对600名或更多患者进行20年的生存分析。然而，捕获、组织、更新、交换和分析由该技术生成的数据会带来许多重大挑战。即使是涉及组织微阵列的有限研究所产生的大量数据、文本和图像，也可以随着时间的推移迅速接近一个小型临床部门的数据、文本和图像。此次修订的更新申请的中心目标是：(1)通过扩大TMA成像标本和相关临床数据的参考档案，包括更广泛的恶性肿瘤、组织和生物标志物，建立在第一阶段研究进展的基础上；(2)开发先进的成像、计算和数据管理工具，以支持协作框架中组织微阵列的自动分析；(3)增加向临床和研究界传播具有查询功能的图像存档、成像和数据管理工具，用于研究、教育和临床决策支持。拟议项目的目标将通过开发和实施先进的计算、成像和模式识别工具和新技术来实现。

DESCRIPTION (provided by applicant): Tissue microarray technology holds great potential for reducing the time and cost associated with conducting investigative research in cancer biology, oncology, and drug discovery. TMA's make it possible to construct a carefully planned array such that a 20-year survival analysis can be performed on a cohort of 600 or more patients using only a few micro-liters of antibody. However, capturing, organizing, updating, exchanging, and analyzing the data generated by this technology creates a number of significant challenges. The sheer volume of data, text, and images arising from even limited studies involving tissue microarrays can over time quickly approach those of a small clinical department. The central objective of this revised renewal application is to (1) build upon the progress made in the first phase of research by expanding the reference archive of imaged TMA specimens and correlated clinical data to include a wider scope of malignancies, tissues and biomarkers; (2) develop advanced imaging, computational and data management tools to support automated analysis of tissue microarrays in collaborative frameworks; and (3) increase dissemination of the query-enabled image archive and imaging and data management tools to the clinical and research communities for research, education and clinical decision support. The aims of the proposed project will be achieved through the development and implementation of advanced computational, imaging, and pattern recognition tools and new technologies. PUBLIC HEALTH RELEVANCE: Tissue microarray technology holds great promise for advancing investigative research in cancer biology, oncology and drug discovery. The overarching objective of the proposed project is to develop a suite of algorithms and software tools which facilitate automated imaging, analysis, and archiving of tissue microarrays. The key computational and imaging tools that were developed in the first phase of the project including a color decomposition algorithm for analyzing the staining characteristics of the histology; image analysis tools for automatically computing the integrated staining intensity, effective staining area and effective staining intensity of expression patterns; an intelligent image archiving system; caBIG compliant data management tools; a reference library of expression signatures for more than 120, 000 imaged tissue discs originating from a mixed set of cancer tissue microarrays; and a new texture descriptor based on region covariance which was shown to provide quick, reliable performance for identifying and delineating tumor regions and performing antigen localization at the tissue level. The central objective of this revised renewal application is to build upon the progress made in the first phase of our research by (1) expanding the reference archive of imaged specimens and correlated clinical data to include a wider range of tissues, cancer types and biomarkers; (2) building upon our prior work by integrating a vendor-independent interface to the TMA analysis and data management toolset to support a full range of commercially available virtual slide formats; (3) investigating the use of a new repulsion force term to be used in conjunction with the existing internal and external energy equations for improved accuracy in delineating boundaries in regions exhibiting dense, concentrations of cells; (4) integrating a variable channel module into the segmentation algorithm and evaluate its capacity to support multi-dimensional image data; (5) building upon our successful efforts to design, develop, and evaluate a quick, reliable approach for performing unsupervised, deformable co-registration of consecutive histological sections to facilitate analysis across multiple experiments and correlate image features across adjacent sections; (6) deploying the updated software suite, data management tools and query-enabled reference archive of imaged TMA specimens to the consortium of adopter sites and assess performance using quantitative imaging experiments and a newly developed man-machine comparative analysis software toolkit. Upon completion of the project the archive of imaged specimens, computational and data management tools will be made available to the clinical and research communities as shareable resources for collaborative research, education and clinical decision support.