Digital pathology and image analysis in tissue biomarker research
Digital pathology and image analysis in tissue biomarker research
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DOI：
10.1016/j.ymeth.2014.06.015
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发表时间：
2014-11-01
期刊：
影响因子：
4.8
通讯作者：
Salto-Tellez, Manuel
中科院分区：
文献类型：
--
作者：
Hamilton, Peter W.;Bankhead, Peter;Salto-Tellez, Manuel
数字病理学以及图像分析的应用在过去几年中迅速发展。这在很大程度上归因于全切片扫描的实施、软件和计算机处理能力的进步，以及基于组织的研究对于生物标志物发现和分层医学日益增长的重要性。本综述阐述了数字病理学和图像分析的关键应用领域，尤其侧重于研究和生物标志物发现。对多种图像分析应用进行了综述，包括细胞核形态测量和组织结构分析，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在药物/伴随诊断开发流程中发挥着重要作用，包括生物样本库、分子病理学、组织微阵列分析、组织的分子图谱分析，对这些重要进展进行了综述。支撑所有这些重要进展的是对高质量组织样本的需求，并且讨论了分析前变量对组织研究的影响。这一要求与建立和运营数字病理学实验室的实用建议相结合。最后，我们讨论了将数字图像分析数据与流行病学、临床和基因组数据相结合的必要性，以便充分理解基因型和表型之间的关系，并推动发现和个性化医疗的实施。（C）2014爱思唯尔公司。保留所有权利。
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.