Computer-aided diagnosis of dysplasia in Barrett"s esophagus using endoscopic optical coherence tomography

Abstract

Barrett's esophagus (BE) has become a major health care burden because of its association with adenocarcinoma of the esophagus. We have shown that endoscopic optical coherence tomography (EOCT) has a 70% accuracy in the diagnosis of dysplasia (Gastrointest Endosc 2003; 57:AB77). To demonstrate the feasiblity of computer aided diagnosis (CAD) of dysplasia in BE using EOCT digital images, to quantitate/standardize the diagnosis of dysplasia, and to develop algorithms suitable for EOCT surveillance of large areas of Barrett’s mucosa, 106 EOCT images were selected (13 patients from 28 cases) from the clinical study including 68 of non-dysplastic and 38 of dysplastic mucosa. From the digital image stream, the 3 frames immediately preceding impact of the forceps on the tissue were selected to insure close correlation between histology/EOCT image pairs. Computer aided diagnosis by center symmetric autocorrelation (CENS) and principal component analysis (PCA) were used for feature parameter extraction and analysis based on the segmented ROI. Leave-one-out cross-validation was used for classification and finally receiver operating characteristic (ROC) curve was used to evaluate the performance of CAD and the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated. The result shows that CAD is able to achieve a higher accuracy than humans for identification of dysplasia in EOCT images. CAD may be of assistance in the EOCT surveillance of large surface areas of Barrett’s mucosa for dysplasia.