Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry

• Published in: Frontiers in physics Vol. 9
• Main Authors: Chen, Cindy X., Park, Han Sang, Price, Hillel, Wax, Adam
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 30.11.2021
• Subjects: cancer diagnostics and screening; cell biophysical properties; high throughput microfluidics; machine learning; quantitative phase imaging (QPI)
• ISSN: 2296-424X; 2296-424X
• DOI: 10.3389/fphy.2021.759142

Holographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to distinguishing carcinogen-exposed cells from normal cells and cancer cells. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data, we are able to build single-cell-analysis-based biophysical phenotype profiles on the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, we demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. The high classification accuracy levels show the platform’s potential in being developed into a diagnostic tool for abnormal cell screening.