Staining-Free, In-Flow Enumeration of Tumor Cells in Blood Using Digital Holographic Microscopy and Deep Learning

Abstract

Currently, detection of circulating tumor cells (CTCs) in cancer patient blood samples relies on immunostaining, which does not provide access to live CTCs, limiting the breadth of CTC-based applications. As a first step to address this limitation, here, we demonstrate staining-free enumeration of tumor cells spiked into lysed blood samples using digital holographic microscopy (DHM), microfluidics and machine learning (ML). A 3D-printed module for laser assembly was developed to simplify the optical set up for holographic imaging of cells flowing through a sheath-based microfluidic device. Computational reconstruction of the holograms was performed to localize the cells in 3D and obtain the plane of best focus images to train deep learning models. First, we evaluated the classification performance of two convolutional neural networks (CNNs): ResNet-50 and a custom-designed shallow Network dubbed s-Net. The accuracy, sensitivity and specificity of these networks were found to range from 97.08% and 99.32%. Upon selecting the s-Net due to its simple architecture and low computational burden, we formulated a decision gating strategy to significantly lower the false positive rate (FPR). By applying an optimized decision threshold to mixed samples prepared in silico, the FPR was reduced from 1x10-2 to 2.77x10-4. Finally, the developed DHM-ML framework was successfully applied to enumerate spiked MCF-7 breast cancer cells from lysed blood samples containing a background of white blood cells (WBCs). We conclude by discussing the advances that need to be made to translate the DHM-ML approach to staining-free enumeration of CTCs in cancer patient blood samples.