Malignant melanoma (MM) is the most severe
tumor affecting the skin and accounts for three quarters of all
skin cancer deaths. Raman spectroscopy is a promising nondestructive tool that has been increasingly used for characterization of the molecular features of cancerous tissues. Different multivariate statistical analysis techniques are used in order to extract relevant information that can be considered as functional spectroscopic descriptors of a particular pathology.
Paraffin embedding (waxing) is a highly efficient process used to conserve biopsies in
tumor banks for several years. However, the use of non-dewaxed
formalin-fixed
paraffin-embedded tissues for Raman spectroscopic investigations remains very restricted, limiting the development of the technique as a routine analytical tool for biomedical purposes. This is due to the highly intense signal of
paraffin, which masks important vibrations of the
biological tissues. In addition to being time consuming and chemical intensive, chemical dewaxing methods are not efficient and they leave traces of the
paraffin in tissues, which affects the Raman signal. In the present study, we use independent component analysis (ICA) on Raman spectral images collected on
melanoma and
nevus samples. The sources obtained from these images are then used to eliminate, using non-negativity constrained least squares (NCLS), the
paraffin contribution from each individual spectrum of the spectral images of
nevi and melanomas. Corrected spectra of both types of lesion are then compared and classified into dendrograms using hierarchical cluster analysis (HCA).