PLS-ANN based classification model for oral submucous fibrosis and oral carcinogenesis.
OBJECTIVE
For effective management of oral neoplasia, autofluorescence spectroscopy was conducted on patients with different characteristics of oral lesions in vivo. This study tested the possibility of using a multivariate statistical algorithm to differentiate human oral premalignant and malignant lesions from benign lesions or normal oral mucosa.


METHODS
A fiber optics-based fluorospectrometer was used to measure the autofluorescence spectra from healthy volunteers (NOM) and patients with oral lesions of submucous fibrosis (OSF), epithelial hyperkeratosis (EH), epithelial dysplasia (ED), and squamous cell carcinoma (SCC). A partial least-squares and artificial neural network (PLS-ANN) classification algorithm was used to characterize these oral lesions to discriminate premalignant (ED) and malignant (SCC) tissues from "benign" (NOM, OSF, and EH) tissues.


RESULTS
The normalized and centerized spectra of the different kinds of samples showed similar but divergent patterns. Our PLS-ANN classification algorithm could differentiate "premalignant and malignant" tissues from "benign" tissues with a sensitivity of 81%, a specificity of 96%, and a positive predictive value of 88%.


CONCLUSIONS
We conclude that the PLS-ANN classification algorithm based on autofluorescence spectroscopy at 330-nm excitation is useful for in vivo diagnosis of OSF as well as oral premalignant and malignant lesions.
Wang
Chih-Yu
CY
Department of Biomedical Engineering, I-Shou University, Kaohsiung, Taiwan, Republic of China.
Tsai
Tsuimin
T
Chen
Hsin-Ming
HM
Chen
Chin-Tin
CT
Chiang
Chun-Pin
CP
D000465:Algorithms; D002294:Carcinoma, Squamous Cell / Q000145:classification*; D006801:Humans; D016018:Least-Squares Analysis; D009061:Mouth Mucosa / Q000473:pathology; D009062:Mouth Neoplasms / Q000145:classification*; D016571:Neural Networks, Computer; D011230:Precancerous Conditions / Q000145:classification*; D013050:Spectrometry, Fluorescence