Discrimination between benign, primary and secondary malignancies in lymph nodes from the head and neck utilising Raman spectroscopy and multivariate analysis

Background: The potential use of Raman spectroscopy (RS) for the detection of malignancy within lymph nodes of the head and neck was evaluated. RS measures the presence of biomolecules by the inelastic scattering of light within cells and tissues. This can be performed in vivo in real-time. Methods: 103 lymph nodes were collected from 23 patients undergoing surgery for suspicious lymph nodes. Five pathologies, defined by consensus histopathology, were collected including reactive nodes (benign), Hodgkin's and non-Hodgkin's lymphomas, metastases from both squamous cell carcinomas and adenocarcinomas. Raman spectra were measured with 830 nm excitation from numerous positions on each biopsy. Spectral diagnostic models were constructed using principal component analysis followed by linear discriminant analysis (PCA-LDA), and by partial least squares discriminant analysis (PLS-DA) for comparison. Two-group models were constructed to distinguish between reactive and malignant nodes, and three-group models to distinguish between the benign, primary and secondary conditions. Results: Results were validated using a repeated subsampling procedure. Sensitivities and specificities of 90% and 86% were obtained using PCA-LDA, and 89% and 88% using PLS-DA, for the two-group models. Both PCA-LDA and PLS-DA models were also found to be very successful at discriminating between pathologies in the three-group models achieving sensitivities and specificities of over 78% and 89% for PCA-LDA, and over 81% and 89% for PLS-DA for all three pathology groups. Conclusion: Raman spectroscopy and chemometric techniques can be successfully utilised in combination for discriminating between different cancerous conditions of lymph nodes from the head and neck.