Image Mining for Comparative Analysis of Expression Patterns in Tissue Microarray

用于组织微阵列表达模式比较分析的图像挖掘

• 批准号: 8705583
• 负责人: David J Foran
• 金额: $ 48.46万
• 依托单位: RBHS -CANCER INSTITUTE OF NEW JERSEY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-30 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8705583
• 关键词: Affect; Algorithms; Archives; Biological Markers; Cells; Classification; Clinical; Clinical Research; Collection; Communities; Computer Assisted; Computer software; Consensus; Development; Diagnostic; Disease; Disease Progression; Evaluation; Exhibits; Family; Future; Goals; Gold; Histology; Histopathology; Human; Image; Imaging Device; Intraobserver Variability; Libraries; Measurable; Methods; Microarray Analysis; Microscopy; Mining; Molecular Profiling; Outcome Assessment; Outcome Study; Pathologic; Pathology; Patients; Pattern; Performance; Pharmaceutical Preparations; Phase; Process; Quality Control; Reproducibility; Research; Research Personnel; Research Project Grants; Resources; Retrieval; Risk; Sampling; Services; Site; Slide; Specific qualifier value; Specimen; Staging; Staining method; Stains; Stratification; Testing; Tissue Microarray; Tissue Sample; Tissues; Work; analytical tool; base; cancer type; cluster computing; comparative; computerized tools; data modeling; design; image archival system; impression; improved; indexing; insight; meetings; novel diagnostics; oncology; open source; prognostic; repository; research study; software systems; tool; usability

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.

描述（由申请人提供）：区分疾病亚类的能力会影响患者的治疗方式、合适的药物以及合理的风险水平。组织微阵列（TMA）技术使得研究和确认基于整个组织学切片的评估所假设的临床病理相关性成为可能。不幸的是，由于主观印象以及观察者之间和观察者内部的差异，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得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，并发现仅靠人类检查无法明显看出的新诊断和预后线索。该更新应用程序的总体目标是在项目第一阶段取得的进展的基础上，通过满足以下具体目标来设计、开发和评估新功能：（1）开发和评估新的多用途系列阶段，搜索算法以促进大规模、临床和研究、显微镜应用（包括全玻片成像和组织微阵列）的快速、可靠询问； (2) 开发和评估一套高吞吐量服务，能够自动检测、归档和索引大量图像中用户指定的对象（例如组织、细胞），并实现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相关结果研究，并支持“黄金标准”图像档案和相关临床存储库的扩展。这些服务将利用最先进的并行 CPU-GPU 机器以及目标 1 中描述的搜索算法； (3) 使用广泛的不同组织、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和研究； (4) 将分析工具、数据模型、以用户为中心的界面和成像样本参考库部署到参与采用站点，以进行开放式可用性和性能研究，并将这些资源作为开源​​软件提供给临床和研究社区以及支持未来开发和测试新假设、算法和方法的资源。