Autofluorescence imaging of Barrett's esophageal lesions with additional transformation into spatial images of green autofluorescence intensity

Abstract

Background: Early diagnosis of patients with Barrett's esophagus is required to implement appropriate treatment to prevent neoplastic disease development. In this work, we examined the usefulness of autofluorescence imaging as a method to increase the sensitivity of targeted biopsy under numerical color value control with the additional conversion of autofluorescence images into spatial green autofluorescence intensity images. Methods: 148 patients were included in the study. Autofluorescence imaging was used in each endoscopic examination. The obtained images of lesions were transformed with Image Pro PLUS 5.0.2 software to show the points of lesions with the highest values of numerical color value and the lowest green intensity. The obtained results were analyzed statistically using Statistica 8.0 software. Mann-Whitney U test was used to compare red to green ratio, red fluorescence intensity and green color intensity between the examined groups of lesions. Results: Thanks to targeted biopsy under the control of red to green ratio factor and green autofluorescence intensity, this imaging method's sensitivity was also increased in all studied stages of histopathological dysplasia in Barrett's esophagus. In total analysis, the sensitivity of tri-modal imaging with the analysis of green autofluorescence intensity was almost 97%. The spatial maps of autofluorescence intensity significantly improved the effectiveness of biopsies performed to take tissue samples for a histopathological examination compared to white light endoscopy. The extension of autofluorescence to spatial autofluorescence intensity maps significantly reduced the percentage of false-negative results. Conclusions: The study results indicate that autofluorescence imaging allows for assessing the extent of dysplasia lesions and determining the margin of healthy and pathologically effected tissues. Our team's method to convert autofluorescence images into spatial images of green autofluorescence intensity further increased the sensitivity of the study.