Computer-aided diagnosis of dysplasia in Barrett's esophagus using multiple endoscopic OCT images

Abstract

We are developing computer-aided diagnosis (CAD) algorithms to aid in the classification of dysplasia in Barrett's esophagus (BE) using endoscopic OCT (EOCT). Our previous CAD algorithm yielded single spatial scale texture features, used a single parameter as a classifier, and used only one EOCT image per biopsy site. In this work, we aim to overcome these limitations. We present progress in development of Fourier domain fractal analysis with classification trees using multiple images to classify a single site for more accurate dysplasia classification in BE EOCT images. A total of 812 EOCT images (13 patients, 70 non-dysplastic biopsy sites including 499 images and 38 dysplastic biopsy sites including 313 images) were analyzed. Using only one frame for classification, 95% sensitivity (95% confidence interval (CI) is 82%-100%) and 94% specificity (95% CI is 86%-98%) were achieved. Using three frames per biopsy site and requiring two frames to be positive to classify the site as positive, 100% sensitivity (95% CI is 89%-100%) and 100% specificity (95% CI is 94%-100%) were achieved, and 97% of the sites had at least three frames available. In conclusion, Fourier domain fractal analysis with classification tree achieved more successful classification of dysplasia in BE than our previous CAD algorithm. And making use of multiple images from a single biopsy site can improve the accuracy of classification. CAD has the potential to enable EOCT surveillance of large surface areas of Barrett's mucosa for dysplasia.