1. Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review.
- Author
-
Osamura RY, Matsui N, Kawashima M, Saiga H, Ogura M, and Kiyuna T
- Subjects
- Adenocarcinoma of Lung genetics, Automation, Laboratory, DNA Mutational Analysis, ErbB Receptors genetics, Humans, Lung Neoplasms genetics, Mutation, Predictive Value of Tests, Reproducibility of Results, Adenocarcinoma of Lung secondary, Artificial Intelligence, Diagnosis, Computer-Assisted, Image Processing, Computer-Assisted, Lung Neoplasms pathology, Molecular Diagnostic Techniques, Pathology, Molecular
- 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., (© 2021 The Author(s) Published by S. Karger AG, Basel.)
- Published
- 2021
- Full Text
- View/download PDF