PLS-ANN based classification model for oral submucous fibrosis and oral carcinogenesis

Chih Yu Wang, Tsuimin Tsai, Hsin Ming Chen, Chin Tin Chen, Chun Pin Chiang

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

Background and Objectives: 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. Study Design/Materials and 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.

Original languageEnglish
Pages (from-to)318-326
Number of pages9
JournalLasers in Surgery and Medicine
Volume32
Issue number4
DOIs
Publication statusPublished - 2003

Keywords

  • Artificial neural networks
  • Autofluorescence spectroscopy
  • Oral cancer diagnosis
  • Partial-least squares

ASJC Scopus subject areas

  • Surgery

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