Integrating Raman spectroscopy and digital pathology as a novel tool for objective grading of oral epithelial dysplasia

Background: Oral cancer is a growing global health problem with variable incidence around the world.

While Scotland has the highest incidence of oral cancer in the UK, the highest incidence globally is reported in southeast Asia.

Despite advances in oral cancer treatment, improvements in survival remain static which is mainly attributed to late diagnosis.

One of the main problems that hinders early detection is the inability to predict disease progression in oral potentially malignant disorders (OPMDs); that carry a high risk of malignant transformation.

Clinical and histopathological systems currently used to identify the risk of malignant transformation are subjective, lack reproducibility and suffer poor predictive value.

Therefore, there is an urgent need for improved tools to aid in the early detection of oral cancer and predict disease progression in OPMDs.

Aims: This project aims to tackle diagnostic difficulties of OPMDs through developing an integrated Digital Pathology-Raman Spectroscopy approach that holds promise of revolutionising diagnostics in cancer and beyond.

Methods: Oral epithelial dysplasia; the histopathological changes associated with OPMDs will be measured using Raman Spectroscopy and Digital Pathology. Archival samples will be acquired from Scotland, Northern Ireland, Sri Lanka and Malaysia.

Digital histological slides will be used to assess morphological features using Digital Pathology and serial sections will be assessed by Raman spectroscopy to generate chemical fingerprints of the same tissues.

Raman peak ratios will be correlated with morphological parameters to identify biologically relevant features capable of predicting disease progression in oral epithelial dysplasia.

How the results will be used: The identified features will be incorporated into a modified oral epithelial dysplasia grading system, which will be validated on an independent cohort of samples to assess this approach’s diagnostic accuracy and predictive ability.

We believe that this will overcome the suboptimal accuracy of current systems as Raman Spectroscopy and Digital Pathology have individually demonstrated high specificity and sensitivity in a range of applications including distinguishing neoplastic from non-neoplastic head and neck tissues.

We believe that combining Raman Spectroscopy and Digital Pathology will help understand changes associated with oral epithelial dysplasia at the tissue, cellular and molecular levels, thus permitting identification of markers for disease progression.

This will aid in introducing robust and sensitive oral epithelial dysplasia grading systems, capable of detecting malignant transformation earlier than current gold standard methodologies.