Application of a TNTs/Ag nanoparticle-based SERS platform for rapid recognition of breast cancer cells

This study addresses the limitations of traditional imaging methods—such as radiation exposure, insufficient sensitivity, and operational constraints—in early breast cancer diagnosis by developing a three-dimensional Surface-Enhanced Raman Spectroscopy (SERS) detection system based on TiO₂ nanotube arrays (TNTs) modified with Ag nanoparticles (Ag NPs). By optimizing anodization parameters (voltage, time, and electrolyte composition), TNTs with optimal tube diameter, spacing, and length were fabricated, facilitating a uniform deposition of Ag NPs and significantly enhancing the SERS signal. The TNTs/Ag substrate was then applied to detect breast cancer cells (MDA-MB-231, MCF-7) and normal breast cells (MCF-10A). With the aid of machine learning models—including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Decision Trees (DT), Random Forests (RF), K-Nearest Neighbors (KNN), and Logistic Regression (LR)—the system achieved a 100 % accurate classification of cell types. This method, characterized by easy synthesis, low cost, and direct cellular-level recognition, offers a promising approach for the early diagnosis of breast cancer.