Rapid Detection and Purification of Extracellular Vesicles for Hepatocellular Carcinoma Screening Using a Plasmonic Metasurface Integrated with the Kolmogorov–Arnold Network

Hepatocellular carcinoma (HCC) is a leading cause of global cancer-related mortality, with delayed diagnosis adversely affecting patient outcomes. Liquid biopsy techniques using small extracellular vesicles (EVs) offer potential for cancer detection, though current methods are often time-consuming and require complex equipment, limiting clinical utility. Here, we report a metasurface-enhanced EV detection chip (metaEVchip) platform for the dynamic monitoring of HCC-specific EVs, enabling rapid detection and purification. This system provides results within 5 min. The platform integrates a plasmonic metasurface with a Kolmogorov-Arnold network (KAN) to facilitate real-time EV capture, enhancing detection speed while achieving an area under the curve (AUC) of 0.914 for HCC screening. By optimizing the purification process and incorporating complementary detection of alpha-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist II (PIVKAII), the AUC for HCC screening reaches 0.961 in an external validation set. These results effectively differentiate HCC from benign liver diseases (BLD) and early-stage HCC from cirrhosis, addressing limitations of conventional EV detection and demonstrating the potential for rapid cancer screening.

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Journal
ACS Nano
Published
2025-12-04
DOI
https://doi.org/10.1021/acsnano.5c11740
Citations
1
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
0.81
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article

Rapid Detection and Purification of Extracellular Vesicles for Hepatocellular Carcinoma Screening Using a Plasmonic Metasurface Integrated with the Kolmogorov–Arnold Network

Kaibin Chen, Yanling Song, Yinong Xie, Jiaqing Shen et al.
1 citations
ACS Nano
Extracellular vesicles in disease
0.81
article

Rapid Detection and Purification of Extracellular Vesicles for Hepatocellular Carcinoma Screening Using a Plasmonic Metasurface Integrated with the Kolmogorov–Arnold Network

Kaibin Chen, Yanling Song, Yinong Xie, Jiaqing Shen, Jinfeng Zhu, Longhui Zeng, Hanyang Chen, Xiaohui Liu, Xiangyujie Lin, Tianhao Huang, Xinyue Huang, C.C. Wang, Baichang Deng, Yuan Gao, Qiang Niu, Lijun Zeng, Wei Chen, Junjie Chen, Boan Li, Yiming Yan, Jiaheng Zhu, Bo Li, Mengqi Jiang
article en
1 citations

Abstract

Hepatocellular carcinoma (HCC) is a leading cause of global cancer-related mortality, with delayed diagnosis adversely affecting patient outcomes. Liquid biopsy techniques using small extracellular vesicles (EVs) offer potential for cancer detection, though current methods are often time-consuming and require complex equipment, limiting clinical utility. Here, we report a metasurface-enhanced EV detection chip (metaEVchip) platform for the dynamic monitoring of HCC-specific EVs, enabling rapid detection and purification. This system provides results within 5 min. The platform integrates a plasmonic metasurface with a Kolmogorov-Arnold network (KAN) to facilitate real-time EV capture, enhancing detection speed while achieving an area under the curve (AUC) of 0.914 for HCC screening. By optimizing the purification process and incorporating complementary detection of alpha-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist II (PIVKAII), the AUC for HCC screening reaches 0.961 in an external validation set. These results effectively differentiate HCC from benign liver diseases (BLD) and early-stage HCC from cirrhosis, addressing limitations of conventional EV detection and demonstrating the potential for rapid cancer screening.

ACS NanoVol. 19(49)
Weifang Medical University (CN), Xiamen University (CN), Chinese PLA General Hospital (CN), Research Institute for Electromagnetic Materials (JP), Analysis and Measurement Services (United States) (US), National Engineering Research Center for Information Technology in Agriculture (CN), Collaborative Innovation Center of Chemistry for Energy Materials (CN), Beijing Information Science & Technology University (CN), Tsinghua University (CN)
Openalex Percentile: Top 21%
Extracellular vesicles in disease
0.81
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