Microcapillary-Derived Plasmonic-Enhanced Cluster through the Self-Assembly Process for Breast Cancer Diagnosis

Abstract

Artificial intelligence (AI)-based surface-enhanced Raman scattering (SERS) is a powerful system for cancer diagnosis, leveraging its unique advantages by combining the high sensitivity of the SERS technique with the advanced classification capabilities provided by computing power. While previous studies have yielded significant results through using exosomes, miRNA, and phenotypic biomarkers for detecting breast cancer, these methods frequently entail time-consuming and complex pretreatment steps, demanding highly skilled handling. Here, we present a free-label SERS platform with faster sampling without any pretreat using blood plasma for breast cancer diagnosis. In this study, a cluster structure of gold nanoparticles within a confines space of microcapillary was fabricated to generate close-packing nanoparticles for enhancing electromagnetic field and large number of “hot spot.” We demonstrate that our SERS platform can significantly amplify the Raman signal through standard chemical detection of R6G molecules. Consequently, a solution mixed appropriately between blood plasma collected from participants with gold nanoparticles to build the hybrid cluster in the microcapillary for SERS measurement. With the support of a machine learning model, the breast cancer diagnosis has successfully classified between patients and normal participants with a high accuracy of 87.5%.