Diagnosis and staging of cervical cancer using label-free surface-enhanced Raman spectroscopy and BWRPCA-TLNN model

This paper presents a label-free and highly accurate classification serum analytical platform, which will be used to identify cervical cancer at different stages. In detail, the microarray chip fabricated based on the ordered AgAuNRs substrate was prepared to measure the surface-enhanced Raman scattering (SERS) spectra of serum of healthy subjects and cervical cancer patients (I, II, III, and IV), and then a binary weight robust principal component analysis (BWRPCA)-two-layer nearest neighbor (TLNN) model was designed as the diagnosis and recognition model of SERS spectra. The results revealed that the microarray chip can realize rapid, sensitive, high-throughput detection of SERS spectra of serum. The BWRPCA-TLNN successfully differentiated the SERS spectra and accurately captured the key characteristics for classification. The established BWRPCA-TLNN model achieved an excellent classification accuracy of 91.2 %, a diagnostic sensitivity of over 84.0 % and a specificity of over 97.0 %. This exploratory work demonstrated that SERS combined with BWRPCA-TLNN as a diagnostic screening method has potential for improving cervical cancer detection and screening.