Salivary gland tumor detection from saliva to theranostic application of surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) can be considered a rapid, label-free, nondestructive analytical measurement for tumor detection and theranostic applications, beginning from diagnosis as well as tumor treatment and recovery. SERS of saliva samples collected from patients with salivary gland tumors and healthy controls were used to establish a new tool for fast diagnosis before surgery and in follow-up surgery results. The Partial Least Squares Regression (PLSR) method divided the two analyzed data sets, namely the saliva of control patients and those with salivary gland tumors, with 96 % of explained variables in the first three consecutive factors. The outcome indicates the prediction ability of the analyzed model as the low value of root mean square error (cross-validation; RMSE(CV) = 0.11) and high values of R-squared (cross-validation; R2(CV) = 0.95) were obtained. The calibration models were created and optimized using other supervised methods, e.g., partial least squares-discriminant analysis, support vector machine classification, and linear discriminant analysis-principal component analysis. Then, their classification abilities were tested with external samples, achieving impressive accuracy. The study showed that the SERS spectra of the two analyzed classes related to the patient's disease state showed significant differences, allowing the discrimination between them and identifying the external sample.