A Probability‐based Multivariate Statistical Algorithm for Autofluorescence Spectroscopic Identification of Oral Carcinogenesis

Abstract

A probability-based multivariate statistical algorithm combining partial least-squares (PLS) and logistic regression was developed to identify the development stages of oral cancer through analysis of autofluorescence spectra of oral tissues. Tissues were taken from a 7,12-dimethylbenz[a]anthracene-induced hamster buccal pouch carcinogenesis model. Analyses were conducted at various excitation wavelengths, ranging from 280 nm to 400 nm in 20 nm increments, to assess classification performance at different excitations. For each excitation the PLS analysis and logistic regression were combined, on the basis of cross validation, to calculate the posterior probabilities of samples belonging to four stages of cancer development: normal tissues, hyperplasia, dysplasia and early cancers and frankly invasive cancers. Results showed that the 320 nm excitation wavelength optimally classified the cancer development stages: the accuracy rates for identifying samples at that excitation were 91.7%, 83.3%, 66.7% and 83.3% for the four respective stages. The average accuracy rate was 81.3%. These results suggest that the algorithm described in this study might be useful for the detection of human oral cancers.