## 4 Discussion  
In 2004, the CellSearch system was approved for the detection of CTCs in humans by the Food and Drug Administration (FDA). This system is based on magnetic cell sorting, the expression of EpCAM and cytokeratin (CK) [31]. The system is reliable and has radically improved the detection of CTCs. However, one drawback is the inability to detect CTCs with downregulated EpCAM expression. The level of EpCAM varies in different tumor types and is known to be downregulated to increase invasiveness and metastatic potential [11; 32]. The CellSearch system may miss EpCAM-negative tumor cells due to the loss of EpCAM expression. Several sorting methods exist based on other cell characteristics, such as physical properties (size, elasticity and surface charge). CTCs are usually larger  
Figure 3: Diagrams presenting the average cell area (**A**), average optical volume (**B**) and average cell thickness (**C**) for Jurkat, THP-1 and six breast cancer cell lines. The calculations were based on at least 10 images/sample. All results comparing THP-1 and Jurkat with the breast cancer cells were significant with p \(<\) 0.005.  
in size, in contrast to leukocytes, and therefore, many CTCs isolation methods are mainly based on size differences [14; 33; 34]. A microcavity array (MCA) system based on a polycarbonate filter for the isolation by size of epithelial tumor cells has been developed [35; 36]. The MCA system showed a significantly higher degree of CTCs isolation in advanced lung cancer patients than the CellSearch system [35]. [PERSON] et al. developed a microfluidic chip (CROSS) for the label-free isolation of CTCs, which showed higher sensitivity than CellSearch when isolating CTCs from metastatic colorectal cancer patients [36].  
As the morphological parameters including the area, thickness and volume of WBCs are distinctly different from epithelial cancer cell morphology, a CTC detection system based on cell morphology would detect even EpCAM-negative cancer cells. DHC is label-free and provides convenient quantitative imaging analyses of cell morphology for both single cells and cell populations. DHC applications have previously been used to discriminate between the maturity levels of red blood cells, perform cell death studies and analyze drug responses [37; 38; 39; 23]. Cell optical area, thickness and volume are useful parameters, particularly for discrimination between different cell types [39; 40; 41; 42; 43; 44].  
Recent developments in microfluidic methods have allowed a number of new investigations related to CTCs; however, the low frequency of CTCs in the circulation remains a challenge for the application of CTC-based technologies in the clinical setting [45]. DHC could, in combination with machine-learning algorithms, distinguish between normal skin cells and melanoma cells, and between primary and metastatic melanoma cell lines with high significance [46]. Machine-learning algorithms and computational tools are now provided methods for optimizing DHC for kinetic single adherent cell classification [47]. [PERSON] et al. optimized high-throughput holographic screening for detecting tumor cells in blood through a microchannel system and making it a promising tool for the label-free analysis of liquid biopsy samples [48]. [PERSON] et al. merged two technologies to perform the continuous high-resolution fluorescence imaging of cellular suspensions in a deep microfluidics chamber [49]. Here, a microfluidics chamber could be one option for continuing this project and extending the analysis to a large number of WBCs.  
In this study, we have shown that epithelial breast cancer cells can be discriminated from WBCs by measuring the cellular thickness, volume and area using DHC. The investigated parameters showed significant differences between epithelial cells and blood cells. Future applications will include peripheral blood cells and extended collections of epithelial cancer cells.