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、肿瘤学和药物发现相关的调查研究的时间和成本方面具有巨大的潜力。TMA使得有可能构建精心规划的阵列，使得仅使用几微升抗体就可以对600名或更多患者的队列进行20年生存分析。然而，捕获、组织、更新、交换和分析该技术生成的数据带来了许多重大挑战。即使是涉及组织微阵列的有限研究所产生的数据、文本和图像的绝对量，随着时间的推移，也可以快速接近小型临床部门的数据、文本和图像。此次修订后的更新申请的中心目标是：（1）通过扩大成像TMA标本和相关临床数据的参考档案，以包括更广泛的恶性肿瘤、组织和生物标志物，在第一阶段研究取得进展的基础上再接再厉;（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化分析;以及（3）增加向临床和研究团体传播支持查询的图像存档以及成像和数据管理工具，以用于研究、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。 公共卫生相关性：组织微阵列技术为推进癌症生物学、肿瘤学和药物发现的调查研究带来了巨大的希望。该项目的总体目标是开发一套算法和软件工具，以促进组织微阵列的自动成像，分析和存档。在项目第一阶段开发的关键计算和成像工具包括用于分析组织学染色特征的颜色分解算法;用于自动计算表达模式的综合染色强度、有效染色面积和有效染色强度的图像分析工具;智能图像存档系统;符合caBIG的数据管理工具;来自一组混合癌症组织微阵列的超过120，000个成像组织圆盘的表达特征参考库;以及基于区域协方差的新纹理描述符，该描述符已被证明为识别和描绘肿瘤区域以及在组织水平进行抗原定位提供快速、可靠的性能。此次修订的更新申请的中心目标是在我们研究第一阶段取得的进展的基础上，通过（1）扩大成像标本和相关临床数据的参考档案，以包括更广泛的组织，癌症类型和生物标志物;（2）在我们先前工作的基础上，通过整合供应商-TMA分析和数据管理工具集的独立接口，支持全系列商用虚拟载玻片格式;（3）研究与现有的内部和外部能量方程结合使用的新的排斥力项的使用，以提高在呈现密集细胞集中的区域中描绘边界的准确性;（4）将可变通道模块集成到分割算法中，并评估其支持多维图像数据的能力;（5）在我们成功设计、开发和评估用于执行连续组织学切片的无监督、可变形共配准的快速、可靠方法的基础上，以促进跨多个实验的分析并关联跨相邻切片的图像特征;（6）将更新的软件套件、数据管理工具和可查询的TMA成像标本参考档案部署到采用者网站联盟，并使用定量成像实验和新开发的人机比较分析软件工具包评估性能。项目完成后，将向临床和研究界提供成像标本、计算和数据管理工具的档案，作为合作研究、教育和临床决策支持的可共享资源。

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.