Zhu Xubin, Xie Han, Chen Kaiyu, Zhang Zhilin, Zhao Xudong, Miao Zeyu, Xu Jinyi, Li Yiwei, Chen Peng, Liu Bi-Feng
Key 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 Lett. 2025 Oct 1;25(39):14293-14303. doi: 10.1021/acs.nanolett.5c03217. Epub 2025 Sep 17.
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 ( = 864) and achieves 0.814 ( = 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.
液体活检能够通过检测循环肿瘤细胞和小细胞外囊泡（sEVs）实现非侵入性癌症诊断，但由于生物标志物丰度低，准确的肿瘤亚型鉴别仍然受限。在此，我们提出了一种基于冻融诱导金纳米颗粒漂浮模式（FTFPA）的低成本自动化癌症分类平台，集成了基于智能手机的图像捕获和人工智能驱动的分析。该系统对九种细胞类型及其sEVs进行分类，F1分数分别为0.891和0.898（ = 864），在包括健康对照、乳腺结节和乳腺癌亚型的临床样本上达到0.814（ = 576）。该方法能够以传统显微镜成本的1%在1.5分钟内处理96个样本，利用了冻融诱导的浓度和弱相互作用驱动的金纳米颗粒聚集。这种便携且快速的方法能够实现可靠的sEV分类和肿瘤亚型诊断，为即时癌症诊断提供了一种实用的解决方案。
