Ultrasensitive Whispering-Gallery Extracellular Vesicle Biosensor for Early Gastric Cancer Detection

Abstract Extracellular vesicles (EVs) have emerged as superior tools compared with plasma tumor biomarkers for gastric cancer detection. However, clinical practice of EV detection for early stage gastric cancer (EGC) discrimination remains limited, primarily hindered by the low sensitivity of current detection technologies and the intrinsic complexity of EVs. Here, we propose a label-free microfluidic microsensor for the ultrasensitive and rapid detection of EVs in mixed environments. An estimated detection limit of 23.87 EVs/mL for EV detection is achieved by using the ultrahigh-Q whispering-gallery-mode microbubble resonator. Moreover, by incorporating an interpretable machine learning algorithm, the complex spectral features of EVs can be effectively captured for discrimination analysis. In clinical validation, the system achieves a discrimination performance of 90.91% in distinguishing EGC from healthy individuals, significantly outperforming that of commonly used tumor biomarkers. These findings provide robust evidence supporting the feasibility of machine learning-assisted, microcavity-enhanced EV analysis for distinguishing EGC from other clinical groups.