Breast cancer continues to be one of the most prevalent and life‐threatening malignancies amongwomen, emphasizing the demand for diagnostic methods that are rapid, sensitive, and noninvasive. This work explored the diagnostic utility of surface‐enhanced Raman spectroscopy (SERS) in conjunction with 100 kDa centrifugal filtration of serum samples, employing gold nanoparticles (AuNPs) as SERS substrate, for stage‐specific detection of breast cancer. Serum specimens collected from patients across four clinical stages, along with healthy controls, were analyzed, and their spectral profiles were subjected to chemometric evaluation using principal component analysis (PCA) and support vector machine (SVM) learning. The analysis revealed distinct biochemical signatures in proteins, lipids, nucleic acids, and carbohydrates that reflected disease progression. PCA enabled clear separation between spectra from healthy and cancer groups, while SVM classification delivered strong predictive performance, with area under the curve (AUC) values of 1.00, 0.93, and 0.89 for healthy, stage 3, and stage 4, respectively. Although early‐stage detection yielded moderate results, with AUC values of 0.55 and 0.71 for stages 1 and 2, the study demonstrates that AuNP‐based SERS integrated with advanced machine learning provides a promising, label‐free, and noninvasive platform for rapid breast cancer diagnosis and staging, with potential for clinical translation.
Fatima et al. (Wed,) studied this question.