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用于分子细胞学检测的数字/计算技术:一篇带文献综述的简短技术说明

Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review.

作者信息

Osamura Robert Y, Matsui Naruaki, Kawashima Masato, Saiga Hiroyasu, Ogura Maki, Kiyuna Tomoharu

机构信息

Department of Diagnostic Pathology Nippon Koukan Hospital, Kawasaki, Japan.

Keio University School of Medicine, Tokyo, Japan.

出版信息

Acta Cytol. 2021;65(4):342-347. doi: 10.1159/000515379. Epub 2021 Apr 30.

Abstract

This short article describes the method of digital cytopathology using Z-stack scanning with or without extended focusing. This technology is suitable to observe such thick clusters as adenocarcinoma on cytologic specimens. Artificial intelligence (AI) has been applied to histological images, but its application on cytologic images is still limited. This article describes our attempt to apply AI technology to cytologic digital images. For molecular analysis, cytologic materials, such as smear, LBC, and cell blocks, have been successfully used for targeted single gene detection and multiplex gene analysis with next-generation sequencing. As a future perspective, the system can be connected to full automation by combining digital cytopathology with AI application to detect target cancer cells and to perform molecular analysis. The literature review is updated according to the subjects.

摘要

这篇短文描述了使用Z轴堆叠扫描(有无扩展聚焦)的数字细胞病理学方法。这项技术适用于观察细胞学标本上如腺癌这样的厚细胞团。人工智能(AI)已应用于组织学图像,但在细胞学图像上的应用仍然有限。本文描述了我们将AI技术应用于细胞学数字图像的尝试。对于分子分析,细胞学材料,如涂片、液基薄层制片(LBC)和细胞块,已成功用于靶向单基因检测和下一代测序的多重基因分析。从未来前景看,通过将数字细胞病理学与AI应用相结合,该系统可以连接到全自动化,以检测目标癌细胞并进行分子分析。文献综述根据主题进行了更新。

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本文引用的文献

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Translational AI and Deep Learning in Diagnostic Pathology.诊断病理学中的转化人工智能与深度学习
Front Med (Lausanne). 2019 Oct 1;6:185. doi: 10.3389/fmed.2019.00185. eCollection 2019.
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Artificial Intelligence in Pathology.病理学中的人工智能
J Pathol Transl Med. 2019 Jan;53(1):1-12. doi: 10.4132/jptm.2018.12.16. Epub 2018 Dec 28.

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