Development and validation of Raman spectroscopic classification models to discriminate tongue squamous cell carcinoma from non-tumorous tissue

Background: Currently, up to 85% of the oral resection specimens have inadequate resection margins, of which the majority is located in the deeper soft tissue layers. The prognosis of patients with oral cavity squamous cell carcinoma (OCSCC) of the tongue is negatively affected by these inadequate surgical resections. Raman spectroscopy, an optical technique, can potentially be used for intra-operative evaluation of resection margins.

Objective: To develop in vitro Raman spectroscopy-based tissue classification models that discriminate OCSCC of the tongue from (subepithelial) non-tumorous tissue.

Materials and methods: Tissue classification models were developed using Principal Components Analysis (PCA) followed by (hierarchical) Linear Discriminant Analysis ((h)LDA). The models were based on a training set of 720 histopathologically annotated Raman spectra, obtained from 25 tongue samples (11 OCSCC and 14 normal) of 10 patients, and were validated by means of an independent validation set of 367 spectra, obtained from 19 tongue samples (6 OCSCC and 13 normal) of 11 patients.

Results: A PCA-LDA tissue classification model 'tumor' versus 'non-tumorous tissue' (i.e. surface squamous epithelium, connective tissue, muscle, adipose tissue, gland and nerve) showed an accuracy of 86% (sensitivity: 100%, specificity: 66%). A two-step PCA-hLDA tissue classification model 'tumor' versus 'non-tumorous tissue' showed an accuracy of 91% (sensitivity: 100%, specificity: 78%).

Conclusion: An accurate PCA-hLDA Raman spectroscopy-based tissue classification model for discrimination between OCSCC and (especially the subepithelial) non-tumorous tongue tissue was developed and validated. This model with high sensitivity and specificity may prove to be very helpful to detect tumor in the resection margins.