Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review.
Journal:
Acta cytologica
PMID:
33934096
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.
Authors
Keywords
Adenocarcinoma of Lung
Artificial Intelligence
Automation, Laboratory
Diagnosis, Computer-Assisted
DNA Mutational Analysis
ErbB Receptors
Humans
Image Processing, Computer-Assisted
Lung Neoplasms
Molecular Diagnostic Techniques
Mutation
Pathology, Molecular
Predictive Value of Tests
Reproducibility of Results