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Related Experiment Video

Updated: Jan 17, 2026

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Decoding Liquid Biopsy with AI: Freeze-Thaw-Induced Fingerprints in Small Extracellular Vesicles.

Xubin Zhu1, Han Xie1, Kaiyu Chen1

  • 1Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

Nano Letters
|September 17, 2025
PubMed
Summary

A new platform uses gold nanoparticles and AI for noninvasive cancer diagnosis from liquid biopsies. This rapid, low-cost method accurately classifies cancer subtypes and small extracellular vesicles (sEVs) for point-of-care testing.

Keywords:
AuNPsDeep learningcirculating tumor cellsfreeze−thaw imagingsmall extracellular vesicles

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Area of Science:

  • Biomedical Engineering
  • Nanotechnology
  • Artificial Intelligence

Background:

  • Liquid biopsy offers noninvasive cancer detection through circulating tumor cells and small extracellular vesicles (sEVs).
  • Accurate tumor subtype classification is challenging due to low biomarker abundance in liquid biopsies.

Purpose of the Study:

  • To develop a low-cost, automated platform for cancer classification using freeze-thaw-induced floating patterns of gold nanoparticles (FTFPA).
  • To integrate smartphone imaging and AI for analyzing FTFPA for sEV classification and tumor subtype diagnosis.

Main Methods:

  • Developed an automated platform utilizing freeze-thaw-induced floating patterns of gold nanoparticles (FTFPA).
  • Integrated smartphone-based image capture and AI-driven analysis for classifying cell types and sEVs.
  • Validated the system on cell lines and clinical samples, including healthy controls, breast nodules, and breast cancer subtypes.

Main Results:

  • Achieved high F1 scores for classifying nine cell types (0.891) and their sEVs (0.898).
  • Demonstrated diagnostic capability on clinical samples with an F1 score of 0.814.
  • The platform processes 96 samples in 1.5 minutes at a fraction of conventional microscopy costs.

Conclusions:

  • The FTFPA method provides a portable, rapid, and cost-effective approach for robust sEV classification.
  • This technology enables practical point-of-care cancer diagnostics, facilitating accurate tumor subtype identification.