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Related Experiment Video

Updated: Jun 8, 2025

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids
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Machine learning assisted dual-modal SERS detection for circulating tumor cells.

Chenguang Zhang1, Lei Xu2, Xinyu Miao2

  • 1Ningbo Key Laboratory of Biomedical Imaging Probe Materials and Technology, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Ningbo Cixi Institute of Biomedical Engineering, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, PR China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, PR China.

Biosensors & Bioelectronics
|November 2, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for detecting circulating tumor cells (CTCs) using encoded Surface-Enhanced Raman Spectroscopy (SERS) bioprobes and machine learning. This approach achieves high accuracy in identifying CTCs in blood samples, improving cancer diagnosis.

Keywords:
CTCsCancer diagnosisHigh sensitivityMachine learningSERS

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Oncology

Background:

  • Circulating tumor cells (CTCs) are crucial biomarkers for cancer diagnosis and monitoring.
  • Surface-Enhanced Raman Spectroscopy (SERS) offers high sensitivity and selectivity for biomolecule detection.
  • Machine learning (ML) enhances analytical capabilities in biomedical applications.

Purpose of the Study:

  • To develop an integrated strategy for sensitive and specific detection of CTCs.
  • To combine encoded SERS bioprobes with ML for robust CTC identification.
  • To establish a new method for early cancer diagnosis and prognosis.

Main Methods:

  • Design and synthesis of dual-modal SERS bioprobes for magnetic separation and Raman signal encoding.
  • Co-incubation of SERS bioprobes with tumor cells using a "cocktail" method.
  • Development of a CTC identification model using Principal Component Analysis (PCA) and Random Forest (RF) algorithms.

Main Results:

  • Achieved highly sensitive detection of CTCs down to 2 cells/mL.
  • Demonstrated a high CTC detection rate of 98%.
  • Successfully differentiated CTCs from white blood cells (WBCs), minimizing interference.

Conclusions:

  • The developed strategy provides an efficient and accurate method for CTC detection.
  • This approach holds significant potential for improving non-invasive cancer diagnosis.
  • The combination of SERS bioprobes and ML offers a promising platform for future clinical applications.