Image Mining for Comparative Analysis of Expression Patterns in Tissue Microarray

描述（由申请人提供）：区分疾病亚类的能力影响患者的治疗方式，哪些药物是合适的，以及什么风险水平是合理的。组织微阵列（TMA）技术使研究和确认基于整个组织学切片评估的临床病理相关性成为可能。不幸的是，由于主观印象和观察者之间和内部的差异，在评估过程中经常出现不一致的情况。先进的成像和计算工具使检测和跟踪可测量参数的细微变化成为可能，从而深入了解疾病进展的潜在机制，并发现新的诊断和预后线索，这些线索仅通过人类检查是不明显的。该更新申请的总体目标是建立在项目第一阶段的进展基础上，通过满足以下具体目标来设计、开发和评估新功能：(1)开发和评估一系列新的多阶段搜索算法，以促进大规模临床和研究的快速、可靠的询问，显微镜应用包括全切片成像和组织微阵列；(2)开发和评估一套高通量服务，能够自动检测、归档和索引大量图像中用户指定的对象（如组织、细胞），并实现对数据模型的扩展和对优化管道选择的支持。这些功能将实现大规模的相关结果研究，并支持“黄金标准”图像档案和相关临床知识库的扩展。这些服务将利用最先进的并行CPU-GPU机器和目标1中描述的搜索算法；(3)利用广泛的不同组织、癌症类型和生物标志物，优化成像、计算和基于内容的图像检索算法和工具，以支持涉及患者分层、质量控制和结果评估的临床和研究实验；(4)将成像标本的分析工具、数据模型、以用户为中心的界面和参考库部署到参与采用者站点，进行开放集可用性和性能研究，并将这些资源作为开源软件和资源提供给临床和研究社区，以支持未来新假设、算法和方法的开发和测试。

DESCRIPTION (provided by applicant): The capacity to distinguish among subclasses of disease affects how patients are treated, which medications are appropriate, and what levels of risk are justified. Tissue microarray (TMA) technology makes it possible to investigate and confirm clinico-pathologic correlations which have been postulated based upon the evaluation of whole histology sections. Unfortunately, inconsistencies often arise during the evaluation process as a result of subjective impressions and inter- and intra-observer variability. Advanced imaging and computational tools make it possible to detect and track subtle changes in measurable parameters leading to insight regarding the underlying mechanisms of disease progression and the discovery of novel diagnostic and prognostic clues which are not apparent by human inspection alone. The overarching goals of this renewal application are to build upon progress made in the first phase of the project and design, develop and evaluate new capabilities by meeting the objectives of the following specific aims: (1) Develop and evaluate a new family of multi-stage, searching algorithms to facilitate quick, reliable interrogation of larg-scale, clinical and research, microscopy applications including whole-slide imaging and tissue microarray; (2) Develop and evaluate a suite of high-throughput services capable of automatically detecting, archiving and indexing user-specified objects (e.g. tissues, cells) in large collections of images and implement extensions to the data models and support for optimized pipeline selection. These capabilities will enable large-scale correlative outcomes studies and support expansion of the "gold standard" image archives and correlated clinical repositories. The services will take advantage of state-of-the-art parallel CPU-GPU machines and the searching algorithms described in Aim 1; (3) Optimize the imaging, computational and content-based image retrieval algorithms and tools using a wide range of different tissues, cancer types and biomarkers to support clinical and research experiments and studies involving patient stratification, quality-control, and outcomes assessment; and (4) Deploy the analytical tools, data models, user-centered interfaces and reference libraries of imaged specimens to participating adopter sites to conduct open-set usability and performance studies and make these resources available to the clinical and research communities as open source software and resources to support future development and testing of new hypotheses, algorithms and methods.