title: Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network: a pilot study.
abstract: The performance of an artificial neural network was evaluated as an alternative classification technique of autofluorescence spectra of oral leukoplakia, which may reflect the grade of tissue dysplasia. Twenty-two visible lesions of 21 patients suffering from oral leukoplakia and six locations on normal oral mucosa of volunteers were investigated with autofluorescence spectroscopy (420 nm excitation, 465-650 nm emission). Pre-scaled spectra were combined with the corresponding visual and histopathological classifications in order to train artificial neural networks. A trained network is mapping input spectra to tissue characteristics, which was evaluated using a blind set of spectra. Abnormal tissue could be distinguished from normal tissue by a neural network with a sensitivity of 86% and a specificity of 100%. Also, classifying either homogeneous or non-homogeneous tissue performed reasonably well. Weak or no correlation existed between spectral patterns and verrucous or erosive tissue or the grade of dysplasia, hyperplasia and hyperkeratosis.
authors.affiliation: Department of Clinical Physics, PDT Laboratory, Daniel den Hoed Cancer Center, University Hospital Rotterdam, PO Box 5201, NL-3008 AE, Rotterdam, Netherlands.
authors.forename: H J
authors.initials: HJ
authors.lastname: van Staveren
authors.forename: R L
authors.initials: RL
authors.lastname: van Veen
authors.forename: O C
authors.initials: OC
authors.lastname: Speelman
authors.forename: M J
authors.initials: MJ
authors.lastname: Witjes
authors.forename: W M
authors.initials: WM
authors.lastname: Star
authors.forename: J L
authors.initials: JL
authors.lastname: Roodenburg
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D007972:Leukoplakia, Oral / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D009061:Mouth Mucosa / Q000473:pathology*; D016571:Neural Networks, Computer; D010865:Pilot Projects; D013050:Spectrometry, Fluorescence