Insight into metastatic oral cancer tissue from novel analyses using FTIR spectroscopy and aperture IR-SNOM

A novel machine learning algorithm is shown to accurately discriminate between oral squamous cell carcinoma (OSCC) nodal metastases and surrounding lymphoid tissue on the basis of a single metric, the ratio of Fourier transform infrared (FTIR) absorption intensities at 1252 cm(-1) and 1285 cm(-1). The metric yields discriminating sensitivities, specificities and precision of 98.8 +/- 0.1%, 99.89 +/- 0.01% and 99.78 +/- 0.02% respectively, and an area under receiver operator characteristic (AUC) of 0.9935 +/- 0.0006. The delineation of the OSCC and lymphoid tissue revealed by the image formed from the metric is in better agreement with an immunohistochemistry (IHC) stained image than are either of the FTIR images obtained at the individual wavenumbers. Scanning near-field optical microscopy (SNOM) images of the tissue obtained at a number of key wavenumbers, with high spatial resolution, show variations in the chemical structure of the tissue with a feature size down to similar to 4 mu m. The image formed from the ratio of the SNOM images obtained at 1252 cm(-1) and 1285 cm(-1) shows more contrast than the SNOM images obtained at these or a number of other individual wavenumbers. The discrimination between the two tissue types is dominated by the contribution from the 1252 cm(-1) signal, which is representative of nucleic acids, and this shows the OSCC tissue to be accompanied by two wide arcs of tissue which are particularly low in nucleic acids. Haematoxylin and eosin (H&E) staining shows the tumour core in this specimen to be similar to 40 mu m wide and the SNOM topography shows that the core centre is raised by similar to 1 mu m compared to the surrounding tissue. Line profiles of the SNOM signal intensity taken through the highly keratinised core show that the increase in height correlates with an increase in the protein signal. SNOM line profiles show that the nucleic acids signal decreases at the centre of the tumour core between two peaks of higher intensity. All these nucleic acid features are similar to 25 mu m wide, roughly the width of two cancer cells.

Automated summary

A novel machine learning algorithm accurately distinguishes between oral squamous cell carcinoma nodal metastases and surrounding lymphoid tissue based on a specific Fourier transform infrared (FTIR) absorption ratio. The resulting image shows better agreement with immunohistochemistry staining images and better discrimination than FTIR images obtained at individual wavenumbers. Notably, scanning near-field optical microscopy (SNOM) images reveal variations in tissue chemical structure with high spatial resolution.