  We present cytometric classification of live healthy and cancer cells by
using the spatial morphological and textural information found in the
label-free quantitative phase images of the cells. We compare both healthy
cells to primary tumor cell and primary tumor cells to metastatic cancer cells,
where tumor biopsies and normal tissues were isolated from the same
individuals. To mimic analysis of liquid biopsies by flow cytometry, the cells
were imaged while unattached to the substrate. We used low-coherence off-axis
interferometric phase microscopy setup, which allows a single-exposure
acquisition mode, and thus is suitable for quantitative imaging of dynamic
cells during flow. After acquisition, the optical path delay maps of the cells
were extracted, and used to calculate 15 parameters derived from cellular 3-D
morphology and texture. Upon analyzing tens of cells in each group, we found
high statistical significance in the difference between the groups in most of
the parameters calculated, with the same trends for all statistically
significant parameters. Furthermore, a specially designed machine learning
algorithm, implemented on the phase map extracted features, classified the
correct cell type (healthy/cancer/metastatic) with 81%-93% sensitivity and
81%-99% specificity. The quantitative phase imaging approach for liquid
biopsies presented in this paper could be the basis for advanced techniques of
staging freshly isolated live cancer cells in imaging flow cytometers.
