BACKGROUND AND AIMS: Autofluorescence imaging (AFI) is a novel technology which can capture fluorescence emitted from intestinal tissues. While AFI is useful for detecting colorectal neoplasms, it is unclear whether AFI can facilitate the diagnosis by differentiating the extent of dysplasia of colorectal neoplasms. This study investigated the efficacy of AFI in discriminating high-grade from low-grade adenoma. MATERIALS AND METHODS: Sixty-seven patients who underwent colonoscopy with AFI were enrolled in this study. The AFI images obtained from 158 lesions in these patients were visually classified into four categories, namely, green (G), green with magenta spots (GM), magenta with green spots (MG), and magenta (M), according to their color intensities, immediately after the examination. The AFI images of the lesions were quantified using an image-analytical software program (F index). Either the F index or the visual assessment was prospectively compared with the dysplastic grade. RESULTS: The F index of the high-grade adenomas was significantly lower than that of the low-grade adenomas, hyperplasia, and normal mucosa (p < 0.05). The incidence of the lesions classified into the M classification for high-grade adenomas (55.6%) was significantly higher than that of either low-grade adenomas (20.8%) or hyperplasia (0%). No correlation was observed between the F index or the visual classification and the tumor shape. The F index was not influenced by the size of the lesion, while the size was significantly associated with the visual classification of AFI. CONCLUSIONS: AFI, particularly the F index, is considered to be a useful procedure for estimating the dysplastic grade of colonic adenomas.
BACKGROUND AND AIMS: Autofluorescence imaging (AFI) is a novel technology which can capture fluorescence emitted from intestinal tissues. While AFI is useful for detecting colorectal neoplasms, it is unclear whether AFI can facilitate the diagnosis by differentiating the extent of dysplasia of colorectal neoplasms. This study investigated the efficacy of AFI in discriminating high-grade from low-grade adenoma. MATERIALS AND METHODS: Sixty-seven patients who underwent colonoscopy with AFI were enrolled in this study. The AFI images obtained from 158 lesions in these patients were visually classified into four categories, namely, green (G), green with magenta spots (GM), magenta with green spots (MG), and magenta (M), according to their color intensities, immediately after the examination. The AFI images of the lesions were quantified using an image-analytical software program (F index). Either the F index or the visual assessment was prospectively compared with the dysplastic grade. RESULTS: The F index of the high-grade adenomas was significantly lower than that of the low-grade adenomas, hyperplasia, and normal mucosa (p < 0.05). The incidence of the lesions classified into the M classification for high-grade adenomas (55.6%) was significantly higher than that of either low-grade adenomas (20.8%) or hyperplasia (0%). No correlation was observed between the F index or the visual classification and the tumor shape. The F index was not influenced by the size of the lesion, while the size was significantly associated with the visual classification of AFI. CONCLUSIONS: AFI, particularly the F index, is considered to be a useful procedure for estimating the dysplastic grade of colonic adenomas.
Authors: R J Schlemper; R H Riddell; Y Kato; F Borchard; H S Cooper; S M Dawsey; M F Dixon; C M Fenoglio-Preiser; J F Fléjou; K Geboes; T Hattori; T Hirota; M Itabashi; M Iwafuchi; A Iwashita; Y I Kim; T Kirchner; M Klimpfinger; M Koike; G Y Lauwers; K J Lewin; G Oberhuber; F Offner; A B Price; C A Rubio; M Shimizu; T Shimoda; P Sipponen; E Solcia; M Stolte; H Watanabe; H Yamabe Journal: Gut Date: 2000-08 Impact factor: 23.059
Authors: F J C van den Broek; P Fockens; S van Eeden; J B Reitsma; J C H Hardwick; P C F Stokkers; E Dekker Journal: Gut Date: 2008-03-26 Impact factor: 23.059
Authors: Xiaoyun Xu; Jie Cheng; Michael J Thrall; Zhengfan Liu; Xi Wang; Stephen T C Wong Journal: Biomed Opt Express Date: 2013-11-15 Impact factor: 3.732
Authors: Felista L Tansi; Ronny Rüger; Ansgar M Kollmeier; Claudia Böhm; Roland E Kontermann; Ulf K Teichgraeber; Alfred Fahr; Ingrid Hilger Journal: BMC Biotechnol Date: 2017-01-18 Impact factor: 2.563