Related Experiment Video
Updated: Jan 17, 2026
A Standardized Liquid Biopsy Preanalytical Protocol for Downstream Circulating-Free DNA Applications
Published on: September 16, 2022
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.
Abstract:
Liquid biopsy enables noninvasive cancer diagnosis via the detection of circulating tumor cells and small extracellular vesicles (sEVs), yet accurate tumor subtype discrimination remains limited by low biomarker abundance. Here, we propose a low-cost, automated cancer classification platform based on freeze-thaw-induced floating patterns of gold nanoparticles (FTFPA), integrating smartphone-based image capture and AI-driven analysis. The system classifies nine cell types and their sEVs with F1 scores of 0.891 and 0.898 (n = 864) and achieves 0.814 (n = 576) on clinical samples including healthy controls, breast nodules, and breast cancer subtypes. Capable of processing 96 samples in 1.5 min at 1% of conventional microscopy cost, the method exploits AuNP aggregation driven by freeze-induced concentration and weak interactions. This portable and rapid approach enables robust sEV classification and tumor subtype diagnosis, providing a practical solution for point-of-care cancer diagnostics.
