Detection of rare circulating tumor cell clusters in whole blood using deep-learning-enabled, label-free, flow cytometry

Irene Georgakoudi

doi:10.1117/12.3022673

18 June 2024 Detection of rare circulating tumor cell clusters in whole blood using deep-learning-enabled, label-free, flow cytometry

Irene Georgakoudi

Author Affiliations +

Proceedings Volume PC12996, Unconventional Optical Imaging IV; PC129960W (2024) https://doi.org/10.1117/12.3022673
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Circulating tumor cell clusters (CTCCs) are associated with high metastatic potential and poor patient prognosis. However, they are difficult to detect and isolate because of their extremely low numbers. Here, we report on the use of machine learning and deep learning based analysis to achieve accurate detection of CTCCs in flowing whole blood samples relying on the confocal detection of endogenous light scattering signals. Our custom flow cytometer utilizes laser excitation at 405, 488, and 633 nm and confocal detection of the corresponding light scattering signals as well as fluorescence in the 510-530nm range. Samples consist of whole blood isolated from rats spiked with varying concentrations of CTCCs, flowed through the channels of a microfluidic device. The CTCCs express GFP, so that we can detect the strong GFP signal with the 520 nm detector and use it as the ground truth for assessing the performance of algorithms relying on the endogenous signals of the same peaks detected by the other detectors. We achieve a low false alarm rate of 0.78 events/min, a detection purity of 72%, and a sensitivity of 35.3%.

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Irene Georgakoudi "Detection of rare circulating tumor cell clusters in whole blood using deep-learning-enabled, label-free, flow cytometry", Proc. SPIE PC12996, Unconventional Optical Imaging IV, PC129960W (18 June 2024); https://doi.org/10.1117/12.3022673

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KEYWORDS

Cancer detection

Tumors

Whole blood

Signal detection

Flow cytometry

Light scattering

Microfluidics

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