Issue 7, 2023

Deep learning assisted holography microscopy for in-flow enumeration of tumor cells in blood

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. Here, we take the first steps to address this limitation, by demonstrating 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. We developed a custom-designed light-weight shallow Network dubbed s-Net and compared its performance against off-the-shelf CNN models including ResNet-50. The accuracy, sensitivity and specificity of the s-Net model was found to be higher than the off-the-shelf ML models. By applying an optimized decision threshold to mixed samples prepared in silico, the false positive rate was reduced from 1 × 10−2 to 2.77 × 10−4. Finally, the developed DHM-ML framework was successfully applied to enumerate spiked MCF-7 breast cancer cells and SkOV3 ovarian cancer cells from lysed blood samples containing white blood cells (WBCs) at concentrations typical of label-free enrichment techniques. We conclude by discussing the advances that need to be made to translate the DHM-ML approach to staining-free enumeration of actual CTCs in cancer patient blood samples.

Graphical abstract: Deep learning assisted holography microscopy for in-flow enumeration of tumor cells in blood

Supplementary files

Article information

Article type
Paper
Submitted
14 12 2022
Accepted
25 1 2023
First published
02 2 2023
This article is Open Access
Creative Commons BY license

RSC Adv., 2023,13, 4222-4235

Deep learning assisted holography microscopy for in-flow enumeration of tumor cells in blood

A. Gangadhar, H. Sari-Sarraf and S. A. Vanapalli, RSC Adv., 2023, 13, 4222 DOI: 10.1039/D2RA07972K

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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