下一代数字病理学:分子病理学的新兴趋势和测量挑战

A. Dexter, D. Tsikritsis, Natalie A. Belsey, S. Thomas, Jenny Venton, J. Bunch, M. Romanchikova
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引用次数: 0

摘要

数字病理学正在彻底改变组织学特征的分析,并在临床和研究中变得越来越广泛。分子病理学通过提供空间分辨的分子信息来补充组织病理学提供的结构信息,从而扩展了传统组织病理学提供的组织形态信息。分子数据的多维性对数据处理、挖掘和分析提出了重大挑战。新的和现有的病理从业者面临的主要挑战之一是如何选择最合适的分子病理技术为给定的诊断。通过提供不同方法的比较,这篇叙述性综述旨在介绍分子病理学领域,提供许多不同方法的高层次概述。由于图像的每个像素都包含丰富的分子信息,因此分子病理学中的数据处理更为复杂。讨论了关键的数据处理步骤和变量及其对数据的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology
Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.
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