The Emergence of Pathomics
The Emergence of Pathomics
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DOI：
10.1007/s40139-019-00200-x
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发表时间：
2019-09-01
影响因子：
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通讯作者：
Saltz, Joel
中科院分区：
文献类型：
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作者：
Gupta, Rajarsi;Kurc, Tahsin;Saltz, Joel
我们的目标是概述数字病理学图像分析中的机器学习方法和人工智能。我们还强调了新的可视化工具来解释定量图像为基础的病理组学数据，是从整个幻灯片图像中提取，以描述不同的表型特征的癌症在一个频谱的tissues.Recent发现图像分析的组织是基于组织，建筑元素，细胞，细胞核，和其他组织学特征的识别和分类。我们报告新兴的数字病理学图像分析应用程序，研究几种类型和亚型的癌症，以补充传统的组织病理学evaluation.Summary WSI通常包含数十万到数百万的对象在一个异构的组织学景观。因此，病理组学代表了一种非常强大的新兴方法，通过识别相关的空间关系来对细胞相互作用和信号进行分类。癌症中不同表型和行为的内在变异性的定量可用于分析和预测临床结果和治疗反应。
Purpose of Review Our goal is to provide an overview of machine learning methods and artificial intelligence in digital pathology image analysis. We also highlight novel visualization tools to interpret quantitative image-based pathomics data that is extracted from whole slide images to describe diverse phenotypic characteristics of cancer in a spectrum of tissues.Recent Findings Image analysis of tissues is based on the identification and classification of tissue, architectural elements, cells, nuclei, and other histologic features. We report emerging digital pathology image analysis applications to study several types and subtypes of cancer to complement traditional histopathologic evaluation.Summary WSIs typically contain hundreds of thousands to millions of objects within a heterogeneous histologic landscape. Therefore, Pathomics represents an incredibly powerful emerging approach to classify cellular interactions and signaling by identifying relevant spatial relationships. The quantification of the intrinsic variability of different phenotypes and behavior in cancer is useful in analyzing and predicting clinical outcomes and treatment response.