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Validation of a Microfluidic Device Prototype for Cancer Detection and Identification: Circulating Tumor Cells

Rifat Rejuan1, Eugenio Aulisa1, Wei Li2

  • 1Department of Mathematics and Statistics, Texas Tech University, Lubbock, Texas, USA.

International Journal for Numerical Methods in Biomedical Engineering
|April 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a cost-effective hyperuniform micropost microfluidic device (MD) for classifying circulating tumor cells (CTCs). Machine learning analyzes cell trajectories to distinguish CTC phenotypes, aiding early cancer detection.

Keywords:
cell‐based modelingcirculating tumor cell classificationfluid–structure interactionmachine learningmicrofluidic deviceplasma‐laden flow

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Cancer Research

Background:

  • Microfluidic devices (MDs) offer novel methods for detecting circulating tumor cells (CTCs).
  • Current CTC detection methods often result in heterogeneous populations, requiring complex, expensive, and time-consuming downstream processing for phenotype identification.
  • There is a need for efficient and cost-effective approaches for CTC classification.

Purpose of the Study:

  • To investigate a cost-effective hyperuniform micropost microfluidic device (MD) for classifying circulating tumor cells (CTCs).
  • To develop and validate a computational framework combining fluid dynamics and machine learning for CTC phenotype analysis.
  • To assess the potential of this approach for early cancer detection.

Main Methods:

  • Developed a cell-based modeling framework simulating fluid-structure interactions in a microfluidic channel with erythrocyte-laden plasma flow.
  • Generated a large dataset of circulating tumor cell (CTC) trajectories representing two distinct phenotypes.
  • Utilized convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for CTC phenotype classification based on trajectory data.

Main Results:

  • The hyperuniform micropost microfluidic device (MD) design demonstrated effectiveness in distinguishing between different circulating tumor cell (CTC) phenotypes.
  • Machine learning models accurately classified CTC phenotypes based on simulated cell trajectory data.
  • The approach successfully accounted for CTC dynamics within a complex plasma flow environment.

Conclusions:

  • The hyperuniform micropost microfluidic device (MD) coupled with machine learning offers a promising, cost-effective strategy for circulating tumor cell (CTC) classification.
  • This method has the potential to streamline CTC analysis and improve early cancer detection capabilities.
  • Further development could lead to more accessible and efficient cancer diagnostics.