In a recent paper we presented an innovative method of liquid biopsy, for the detection of circulating tumor cells (CTC) in the peripheral blood. Using microfluidics, CTC are individually encapsulated in water-in-oil droplets and selected by their increased rate of extracellular acidification (ECAR). During the analysis, empty or debris-containing droplets are discarded manually by screening images of positive droplets, increasing the operator-dependency and time-consumption of the assay. In this work, we addressed the limitations of the current method integrating computer vision techniques in the analysis. We implemented an automatic classification of droplets using convolutional neural networks, correctly classifying more than 96% of droplets. A second limitation of the technique is that ECAR is computed using an average droplet volume, without considering small variations in extracellular volume which can occur due to the normal variability in the size of the droplets or cells. Here, with the use of neural networks for object detection, we segmented the images of droplets and cells to measure their relative volumes, correcting over- or under-estimation of ECAR, which was present up to 20%. Finally, we evaluated whether droplet images contained additional information. We preliminarily gave a proof-of-concept demonstration showing that white blood cells expression of CD45 can be predicted with 82.9% accuracy, based on bright-field cell images alone. Then, we applied the method to classify acid droplets as coming from metastatic breast cancer patients or healthy donors, obtaining an accuracy of 90.2%.
Keywords: Cancer; circulating tumor cells; liquid biopsy; machine learning; neural networks.