  Inter-observer variability and cancer over-diagnosis are emerging clinical
problems, and there is a strong necessity to support the standards histological
and cytological evaluations by biochemical composition information. Over the
past decades, there has been a very active research in the development of Raman
spectroscopy techniques for oncological applications and large scale clinical
diagnosis. A major issue that has received a lot of attention in the Raman
literature is the fact that variations in instrumental responses and intrinsic
spectral backgrounds over different days of measurements or devices creates
strong inconsistency of Raman intensity spectra over the various experimental
condition, thus making the use of Raman spectroscopy on a large scale and
reproductive basis difficult. We explore different methods to tackle this
inconsistency and show that regular preprocessing methods such as baseline
correction, normalization or wavelet transformation are inefficient on our
datasets. We find that subtracting the mean background spectrum estimated by
identifying non-cell regions in Raman images makes the data more consistent. As
a proof of concept, we employ our single-cell Raman Imaging protocol to
diagnosis challenging follicular lesions, that is known to be particularly
difficult due to the lack of obvious morphological and cytological criteria for
malignancy. We explore dimensionality reduction with both PCA and feature
selection methods, and classification is then performed at the single cell
level with standard classifiers such as k Nearest Neighbors or Random Forest.
We investigate Raman hyperspectral images from FTC133, RO82W-1 and NthyOri 3-1
cell lines and show that the chemical information for the diagnosis is mostly
contained in the cytoplasm. We also reveal some important wavenumber for
malignancy, that can be associated mainly to lipids, cytochrome and
phenylalanine.
