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Detection of Cell-Free DNA in Blood Plasma Samples of Cancer Patients
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Automated Analysis of Liquid Biopsy Using Deep Learning: Detecting Circulating Tumor Cells and Cancer-Associated

Cheng Shen1, Haowen Zhou1, Siyu Lin1

  • 1Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|July 9, 2026
PubMed
Summary

Deep learning and advanced optical imaging enable accurate detection of circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs). This automated approach enhances cancer prognosis and diagnosis by overcoming limitations of manual cell analysis.

Keywords:
Cell imagingCirculating tumor cellsComputational microscopyComputer-aided diagnosisCytologyDeep learningDigital pathologyLiquid biopsyMachine learningOptical microscopy

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Oncology

Background:

  • Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) are key biomarkers for cancer prognosis and diagnosis.
  • Manual cell detection methods are time-consuming and prone to variability.
  • There is a need for automated and accurate cell detection techniques.

Purpose of the Study:

  • To provide an overview of deep learning for cell detection, focusing on CTCs and CAFs.
  • To discuss advancements in high-resolution optical imaging systems.
  • To demonstrate an integrated approach for high-accuracy CTC and CAF detection.

Main Methods:

  • Review of deep learning techniques for general and specific cell detection (CTCs, CAFs).
  • Discussion of high-resolution, all-in-focus optical imaging system developments.
  • Integration of advanced optical hardware with deep learning algorithms for cell analysis in microfiltered samples.

Main Results:

  • Demonstrated high-accuracy and high-fidelity detection of CTCs and CAFs using the integrated approach.
  • Showcased the potential of combining advanced optics with deep learning for cell analysis.
  • Highlighted the limitations of traditional manual cell detection methods.

Conclusions:

  • The integrated approach offers a new automated paradigm for CTC and CAF analysis.
  • Refined technologies and models are crucial for enhancing clinical utility of CTC characterization.
  • This work advances the understanding of metastasis through improved cell analysis.