Abstract
The surface-enhanced Raman scattering (SERS) technique has garnered significant interest due to its ultrahigh sensitivity, making it suitable for addressing the growing demand for disease diagnosis. In addition to its sensitivity and uniformity, an ideal SERS platform should possess characteristics such as simplicity in manufacturing and low analyte consumption, enabling practical applications in complex diagnoses including cancer. Furthermore, the integration of machine learning algorithms with SERS can enhance the practical usability of sensing devices by effectively classifying the subtle vibrational fingerprints produced by molecules such as those found in human blood. In this study, we demonstrate an approach for early detection of breast cancer using a bottom-up strategy to construct a flexible and simple three-dimensional (3D) plasmonic cluster SERS platform integrated with a deep learning algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate that the 3D plasmonic cluster (3D-PC) exhibits a significantly enhanced Raman intensity through detection limit down to 10(-6) M (femtomole-(10(-17) mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects and healthy subjects was used to fabricate the bioink to build 3D-PC structures. The collected SERS successfully classified into two clusters of cancer subjects and healthy subjects with high accuracy of up to 93%. These results highlight the potential of the 3D plasmonic cluster SERS platform for early breast cancer detection and open promising avenues for future research in this field.