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首页> 外文期刊>Methods: A Companion to Methods in Enzymology >Digital pathology and image analysis in tissue biomarker research
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Digital pathology and image analysis in tissue biomarker research

机译:组织生物标志物研究中的数字病理与图像分析

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Digital pathology and the adoption of image analysis have grown rapidly in the last few years. This is largely due to the implementation of whole slide scanning, advances in software and computer processing capacity and the increasing importance of tissue-based research for biomarker discovery and stratified medicine. This review sets out the key application areas for digital pathology and image analysis, with a particular focus on research and biomarker discovery. A variety of image analysis applications are reviewed including nuclear morphometry and tissue architecture analysis, but with emphasis on immunohistochemistry and fluorescence analysis of tissue biomarkers. Digital pathology and image analysis have important roles across the drug/companion diagnostic development pipeline including biobanking, molecular pathology, tissue microarray analysis, molecular profiling of tissue and these important developments are reviewed. Underpinning all of these important developments is the need for high quality tissue samples and the impact of pre-analytical variables on tissue research is discussed. This requirement is combined with practical advice on setting up and running a digital pathology laboratory. Finally, we discuss the need to integrate digital image analysis data with epidemiological, clinical and genomic data in order to fully understand the relationship between genotype and phenotype and to drive discovery and the delivery of personalized medicine. (C) 2014 Elsevier Inc. All rights reserved.
机译:数字病理学和图像分析的采用在过去几年中迅速增长。这主要是由于实施整个幻灯片扫描,软件和计算机处理能力的进步以及基于组织的生物标志物发现和分层药物的研究的重要性。本综述列出了数字病理和图像分析的关键应用领域,特别关注研究和生物标志物发现。综述了各种图像分析应用,包括核形态学和组织架构分析,但重点是组织生物标志物的免疫组织化学和荧光分析。数字病理学和图像分析对药物/伴随诊断发育管道具有重要作用,包括生物库,分子病理学,组织微阵列分析,组织的分子剖析以及这些重要的发展。支持所有这些重要的发展是需要高质量的组织样本,并讨论了分析前变量对组织研究的影响。这一要求与关于建立和运行数字病理实验室的实际建议相结合。最后,我们讨论了将数字图像分析数据与流行病学,临床和基因组数据集成,以充分了解基因型和表型之间的关系,并驱动发现和个性化药物的交付。 (c)2014年elsevier Inc.保留所有权利。

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