Molecular Probe-Guided Surface-Enhanced Raman Scattering Sensing Interfaces and Multiview Feature Fusion for...

Accurate discrimination between prostate cancer (PCa) and benign prostatic hyperplasia (BPH) remains clinically challenging. Here, we developed a multiprobe serum surface-enhanced Raman scattering (SERS) sensing platform comprising Ag NPs (Ag), 4-mercaptobenzoic acid (MBA)@Ag, 4-mercaptophenylboronic acid (MPBA)@Ag, and 4-aminothiophenol (ATP)@Ag, together with a molecular probe-guided spectral feature selection and multiview fusion (MPGSF) strategy for classifying PCa, BPH, and healthy group samples. The probe-modified substrates provided distinct surface-chemical interfaces and reporter-peak modulation patterns. MPGSF selected Raman-shift windows from probe-induced differential responses by integrating the peak intensity, local signal-to-noise ratio, repeated-measurement stability, and extracted sample-level multiview features from measured spectra. In sample-level cross-validation, the MPGSF-random forest (RF) model achieved 92.44% accuracy and 92.13% macro-F1, outperforming single-view and full-spectrum concatenation baselines. In the independent batch hold-out validation set, it maintained 90.00% accuracy and 89.95% macro-F1. Among PCa samples with prostate-specific membrane antigen positron emission tomography molecular-imaging reference information, the PCa recognition consistency was 39/43 (90.7%). Feature-contribution analysis showed that all four SERS views contributed to classification, with high-contribution windows located in serum-response regions with potential biochemical relevance. MPGSF-guided multiprobe SERS provides an interpretable analytical framework for minimally invasive auxiliary classification of prostate diseases.