Detection of circulating tumor cells with image cytometry is limited by the sensitivity and specificity of the biomarker panel. We collected confocal images of ~100,000 cells labeled for DNA, lipids, CD45, and Cytokeratin on a model system of MCF7 and WBCs representing disease positive, D+ and disease negative, D- populations. We computed spatial image metrics and performed multivariable regression and feature selection, increasing the separation of the D+ and D- populations to 7 standard deviations with detection limit of ~1 in 480. In conclusion, simple regression analysis holds promise to improve the separation of rare cells in cytometry applications.

Date Published: 4 March 2019
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Proc. SPIE 10889, High-Speed Biomedical Imaging and Spectroscopy IV, 108890A (4 March 2019); doi: 10.1117/12.2514487

Published in SPIE Proceedings Vol. 10889:
High-Speed Biomedical Imaging and Spectroscopy IV
Kevin K. Tsia; Keisuke Goda, Editor(s)