工作流程
背景(考古学)
计算机科学
生物标志物
数字化病理学
免疫组织化学
过程(计算)
空间分析
蛋白质表达
病理
数据挖掘
计算生物学
人工智能
生物
医学
地理
数据库
古生物学
操作系统
生物化学
遥感
基因
作者
Arvydas Laurinavičius,Benoît Plancoulaine,Paulette Herlin,Aida Laurinavičienė
出处
期刊:Pathobiology
[Karger Publishers]
日期:2016-01-01
卷期号:83 (2-3): 156-163
被引量:18
摘要
Immunohistochemistry (IHC) is widely used in contemporary pathology as a diagnostic and, increasingly, as a prognostic and predictive tool. The main value of the method today comes from a sensitive and specific detection of a protein of interest in the context of tissue architecture and cell populations. One of the major limitations of conventional IHC is related to the fact that the results are usually obtained by visual qualitative or semiquantitative evaluation. While this is sufficient for diagnostic purposes, measurement of prognostic and predictive biomarkers requires better accuracy and reproducibility. Also, objective evaluation of the spatial heterogeneity of biomarker expression as well as the development of combined/integrated biomarkers are in great demand. On the other end of the scale, the rapid development of tissue proteomics accounting for 2D spatial aspects has led to a disruptive concept of next-generation IHC, promising high multiplexing and broad dynamic range quantitative/spatial data on tissue protein expression. This ‘evolutionary gap' between conventional and next-generation IHC can be filled by comprehensive IHC based on digital technologies (empowered by quantification and spatial and multiparametric analytics) and integrated into the pathology workflow and information systems. In this paper, we share our perspectives on a comprehensive IHC road map as a multistep development process.
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