A comprehensive FTIR micro-spectroscopic analysis and classification of precancerous human oral tissue aided by machine learning

Abstract

We present an analysis of the molecular vibrational assessments of different grades of oral precancerous tissue sections, aiming to an early, alternative method other than histopathology to definitively distinguish their grades and remove the interobserver variability related to histopathological grading. Assessment of the prognosis of oral potentially malignant disorders (OPMDs) is dependent only on clinical features, and no defined criteria are still proposed to analyze the treatment outcome. Chair-side analysis of the lymph node metastasis and staging of oral squamous cell carcinoma (OSCC) is also dependent on palpatory findings followed by magnetic resonance imaging (MRI). Among these, Fourier-transform infrared (FTIR) micro-spectroscopy emerges as a highly promising and versatile approach for analyzing oral cancer and precancer specimens, enabling the identification of chemical and molecular changes in tissue samples. In this work, an adequate number of tissue sections affected by different grades of precancer (mild dysplasia, moderate dysplasia, and severe dysplasia) were investigated for biochemical changes in the epithelium and sub-epithelium layers as characterized by their corresponding molecular vibration spectrum. The current study demonstrated distinct alterations based on the spectrum shift of proteins (particularly amide I and amide III) over the progression of precancer. Additionally, using the amide I and amide III regions, a peak fitting method was employed to estimate the secondary structures of proteins. Further, chemometric techniques of principal components analysis–linear discriminant analysis (PCA–LDA) were used to create discrimination models for the precancerous and control groups. Our investigation revealed that the predictive performance of the amide III region was better than that of the amide I region, achieving a 95% accuracy rate. To the best of our knowledge, this is one of the first studies on the application of FTIR micro-spectroscopy for the classification of oral precancers in humans, aided by machine learning.