Deep Label-Free Cell Imaging of Liquid Biopsies for Cancer Monitoring

We will develop and commercialize an innovative device for diagnosis and monitoring of cancer in liquid biopsies based on a label-free interferometric phase microscopy (IPM) unit, coupled with dedicated real-time artificial intelligence (AI) for cell classification. This device will materialize an innovative approach for the much-anticipated imaging flow cytometry, dramatically decreasing its costs, and improving patient care by accurate monitoring of cancer in the clinical lab from a simple lab test (liquid biopsy). The success of the project is dependent on four high-risk/high-gain aspects: (a) Building the first clinical IPM device. (b) Designing and manufacturing a disposable microfluidic device for imaging flow cytometry. (c) Obtaining high-enough acquisition and processing throughput in imaging flow cytometry of urine samples. (d) Training a deep natural network to detect cancer cells based on the information-deep label-free IPM images of cancer cells during flow. The proposed PoC project stems from my on-going ERC StG project that focuses on the application of IPM for grading the metastatic potential of cancer cells, as a basic-science research tool.