Dawn of the digital diagnosis assisting system, can it open a new age for pathology?
Dawn of the digital diagnosis assisting system, can it open a new age for pathology?
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数字诊断辅助系统的曙光，能否开启病理学新时代？
DOI：
10.1117/12.2008967
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发表时间：
2013
影响因子：
--
通讯作者：
M. Sakamoto
中科院分区：
文献类型：
--
作者：
A. Saito;E. Cosatto;T. Kiyuna;M. Sakamoto
数字病理学是在WSI（全切片成像）扫描仪的改进和普及的基础上发展起来的。人们普遍预计WSI扫描仪将被用作下一代诊断显微镜;然而，它们的使用目前主要限于教育和归档。事实上，在使用WSI扫描仪进行诊断（而不是研究目的）方面仍然存在许多障碍，其中两个主要原因是通过从显微镜切换到WSI系统获得的生产率的高成本和小增益以及缺乏WSI标准化。我们认为，推进数字病理学的一个关键因素是计算机辅助诊断系统（CAD）的创建。这种系统需要高分辨率的幻灯片数字化，并为通常昂贵的WSI转换提供明确的附加值。我们（NEC公司）正在创建一个CAD系统，名为e-Pathologist ®。该系统目前用于独立病理学实验室的质量控制（QC/QA），双重检查病理学家的诊断和防止遗漏的癌症。到2012年底，e-Pathologist ®将分析大约80，000张切片、200，000份胃和结肠直肠样本组织。通过e-Pathologist ®的开发，人们已经清楚地认识到，计算机程序应该受到病理学家诊断过程的启发，但它不应该仅仅是病理学家诊断过程的复制或模拟。事实上，病理学家经常以“整体”的方式来诊断载玻片，在各种放大倍率下检查载玻片，以看似随意的方式进行平移和缩放，他们通常很难精确描述。因此，在与病理学家的多次访谈中，关于如何精确地计算机编码诊断专家系统，一直没有明确的处方。相反，我们专注于提取一小部分病理学家一致认为重要的组织病理学特征，然后让计算机找出如何以定量的方式解释整个载玻片上这些特征的存在或不存在。使用病理学家的整体诊断（分为一类疾病），我们使用先进的机器学习技术训练计算机系统，以根据提取的特征预测疾病。通过在训练阶段考虑几位专家病理学家的诊断，我们确保机器正在学习一个“黄金标准”，该标准将一致且客观地应用于所有后续诊断，使其更具可预测性和可靠性。考虑到数字病理学的未来，CAD系统必须产生有效和准确的临床数据。为此，仍然存在许多障碍，包括标准化以及更多的研究，以寻求临床证据，从“计算机友好”的客观测量的组织学图像。目前最常用的染色方法是H&E（苏木精和伊红），但H&E染色过程的标准化非常困难。目前的病理学标准、分类、定义和阈值都是基于病理学家的主观观察。数字病理学是一个新兴的领域，研究人员不仅要负责开发新的算法，还要理解测量的定量数据的意义。
Digital pathology is developing based on the improvement and popularization of WSI (whole slide imaging) scanners. WSI scanners are widely expected to be used as the next generation microscope for diagnosis; however, their usage is currently mostly limited to education and archiving. Indeed, there are still many hindrances in using WSI scanners for diagnosis (not research purpose), two of the main reasons being the perceived high cost and small gain in productivity obtained by switching from the microscope to a WSI system and the lack of WSI standardization. We believe that a key factor for advancing digital pathology is the creation of computer assisted diagnosis systems (CAD). Such systems require high-resolution digitization of slides and provide a clear added value to the often costly conversion to WSI. We (NEC Corporation) are creating a CAD system, named e-Pathologist ®. This system is currently used at independent pathology labs for quality control (QC/QA), double-checking pathologists diagnosis and preventing missed cancers. At the end of 2012, about 80,000 slides, 200,000 tissues of gastric and colorectal samples will have been analyzed by e-Pathologist ®. Through the development of e-Pathologist ®, it has become clear that a computer program should be inspired by the pathologist diagnosis process, yet it should not be a mere copy or simulation of it. Indeed pathologists often approach the diagnosis of slides in a "holistic" manner, examining them at various magnifications, panning and zooming in a seemingly haphazard way that they often have a hard time to precisely describe. Hence there has been no clear recipe emerging from numerous interviews with pathologists on how to exactly computer code a diagnosis expert system. Instead, we focused on extracting a small set of histopathological features that were consistently indicated as important by the pathologists and then let the computer figure out how to interpret in a quantitative way the presence or absence of these features over the entire slide. Using the overall pathologists diagnosis (into a class of disease), we train the computer system using advanced machine learning techniques to predict the disease based on the extracted features. By considering the diagnosis of several expert pathologists during the training phase, we insure that the machine is learning a "gold standard" that will be applied consistently and objectively for all subsequent diagnosis, making them more predictable and reliable. Considering the future of digital pathology, it is essential for a CAD system to produce effective and accurate clinical data. To this effect, there remain many hurdles, including standardization as well as more research into seeking clinical evidences from "computer-friendly" objective measurements of histological images. Currently the most commonly used staining method is H&E (Hematoxylin and Eosin), but it is extremely difficult to standardize the H&E staining process. Current pathology criteria, category, definitions, and thresholds are all on based pathologists subjective observations. Digital pathology is an emerging field and researchers should bear responsibility not only for developing new algorithms, but also for understanding the meaning of measured quantitative data.