Light-Induced Autofluorescence Spectroscopy for Detection of Nasopharyngeal Carcinoma in Vivo

Abstract

Autofluorescence spectral signals were measured in vivo from 85 nasopharyngeal carcinoma lesions and 131 normal tissue sites of 59 subjects during routine nasal endoscopy. Diagnostic algorithms based on principal component analysis and the ratio of the spectral signals between multiple-wavelength bands were developed for classifying the autofluorescence spectra. Performances of the algorithms were evaluated using the cross-validation method. The principal component analysis based algorithms using information from the entire fluorescence spectrum can differentiate nasopharyngeal carcinoma lesions from normal tissue with 95% sensitivity and 93% specificity. With 94% sensitivity, the specificities of multiple-wavelength ratio algorithms are about 83%. The results demonstrate that light-induced autofluorescence endoscopy with principal component analysis algorithms can provide accurate diagnostic information for the detection of nasopharyngeal carcinoma in vivo and may be potentially used in clinical practice combined with the routine white-light endoscopy procedure.