Deep learning-enabled detection of rare circulating tumor cell clusters in whole blood using label-free, flow cytometry-Reference-Cited by-同舟云学术

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

Published:2024 Issue:8 Volume:24 Page:2237-2252
ISSN:1473-0197
Container-title:Lab on a Chip
language:en
Short-container-title:Lab Chip

Author:

Vora Nilay¹^ORCID,Shekar Prashant²,Hanulia Taras¹³^ORCID,Esmail Michael⁴,Patra Abani⁵,Georgakoudi Irene¹

Affiliation:

1. Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA

2. Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL, 32114, USA

3. Institute of Physics, National Academy of Sciences of Ukraine, Kyiv, Ukraine

4. Tufts Comparative Medicine Services, Tufts University, Medford, MA, 02155, USA

5. Data Intensive Studies Center, Tufts University, Medford, MA, 02155, USA

Abstract

We present a deep-learning enabled, label-free flow cytometry platform for identifying circulating tumor cell clusters in whole blood based on the endogenous scattering detected at three wavelengths. The method has potential for in vivo translation.

Funder

National Institute of Biomedical Imaging and Bioengineering

Center for Strategic Scientific Initiatives, National Cancer Institute

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/LC/D3LC00694H

Reference69 articles.

1. Circulating Tumor Cell Clusters Are Oligoclonal Precursors of Breast Cancer Metastasis

2. Clinical Applications and Future Directions of Minimal Residual Disease Testing in Multiple Myeloma

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4. A mouse model for SARS-CoV-2-induced acute respiratory distress syndrome

5. Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles

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