PROJECT SUMMARY/ABSTRACT
Successful treatment and management of oral mucosal lesions depend on a definitive, accurate, and timely
diagnosis. Despite easy accessibility to the oral cavity, oral squamous cell carcinoma (OSCC), the most common
oral cancer, is often not diagnosed until late stages, leading to a poor prognosis. Oral epithelial dysplasia (OED)
is a microscopically diagnosed precancerous lesion associated with an increased risk of OSCC transformation.
An OED can be histologically graded as mild, moderate, or severe based on the World Health Organization’s
three-tier classification system. Unfortunately, the gold standard histopathological diagnosis relies on subjective
morphological evaluation of the biopsy tissue and is unable to identify high-risk OEDs that are most likely to
undergo malignant transformation. The lack of an objective and quantitative OED risk stratification approach has
prevented effective management of precancerous oral lesions and delayed the diagnosis of OSCC. We propose
a novel approach using Fourier transform infrared spectroscopic (FTIR) imaging and machine learning to
address the medical gap of objective OED risk assessment. FTIR spectroscopy provides quantitative
biochemical information of a sample in the form of characteristic absorption spectrum. With a microscope
coupled to an FTIR spectrometer, FTIR imaging allows detailed and spatially resolved biochemical analysis of a
sample, with each pixel containing a full FTIR spectrum. Machine learning is a powerful tool for hyperspectral
FTIR image analysis and diagnostic model development. Using FTIR imaging aided by machine learning, we
successfully trained three machine learning classifiers with 95–100% accuracy in discriminating OSCC from
benign oral tissues in our preliminary study. More excitingly, our results demonstrated an innovative stratification
of severe OEDs into Benign-like and OSCC-like subgroups based on their epithelial FTIR fingerprints. Inspired
by the early finding, the central hypothesis of this proposal is that FTIR imaging aided by machine learning
provides objective and quantitative OED risk stratification. To test the hypothesis, we propose the following two
specific aims: 1) to develop OSCC-Benign classifiers based on epithelial and stromal FTIR fingerprints, and 2)
to evaluate the feasibility of the FTIR image-based approach in OED risk stratification. The long-term goal of the
research is to develop an artificial intelligence aided precision imaging system using FTIR imaging or in
combination with other morphological and functional imaging modalities such as digital pathology and
immunohistochemistry for early oral cancer diagnosis, treatment, and prevention.
