Recent Advances in Light‐Induced Fluorescence Endoscopy (LIFE) of the Gastrointestinal Tract

High-sensitivity and specificity of laser-induced autofluorescence spectra for detection of colorectal cancer with an artificial neural network

An artificial neural network (ANN) has been used in various clinical research for the prediction and classification of data in cancer disease. Previous research in this direction focused on the correlation between various input parameters such as age, antigen, and size of tumor growth. Recently, laser-induced autofluorescence (LIAF) techniques have been shown to be a useful noninvasive early diagnostic tool for various cancer diseases. We report on a successful application of ANN to in vitro LIAF spectra. We show that classification of tumor samples with ANN can be done with high sensitivity, specificity, and accuracy. Thus a combination of LIAF techniques and ANN can provide a robust method for clinical diagnosis.

Detection of early gastric cancer by a real-time autofluorescence imaging system

A light-induced fluorescence endoscopy in the gastrointestinal tract system was used in 52 patients with 54 lesions (33 early gastric cancers, 21 benign lesions) to assess its ability to detect early gastric cancer. Comparing the images with the histological findings, 21 of the 33 carcinomas appeared dark red, ten had a mixed pattern of dark red and white, and two could not be detected. Of the 21 benign lesions, 18 appeared light blue, as do normal mucosa, with this system. In 85% of the cancer lesions (28/33), cancer extension was correctly detected. The sensitivity and specificity were 94 and 86%, respectively. Real-time autofluorescence endoscopy is a useful adjunct to conventional white-light endoscopy for detecting early gastric cancer.