Digital imaging processing of EUS image in differentiating autoimmune pancreatitis from chronic pancreatitis

Abstract

Objective To explore the feasibility of using digital imaging processing (DIP) to extract EUS image parameters for the differential diagnosis of autoimmune pancreatitis (AIP) and chronic pancreatitis (CP). Methods A total of 81 patients with AIP and 100 patients with CP diagnosed from May 2005 to January 2013 were recruited to this study. A total of 105 parameters of 9 categories were extracted from the region of interest by using computer-based techniques. Then the distance between class algorithm and sequential forward selection (SFS) algorithm were used for a better combination of features. A support vector machine (SVM) predictive model was built, trained, and validated. Results Overall, 25 parameters of 5 categories were selected as a better combination of features when the incidence of accurate category was max (90.08%). A total of 181 sample sets were randomly divided into a training set and a testing set by using two different algorithms and 200 random tests were performed. The average accuracy, sensitivity, specificity, the positive and negative predictive values of AIP based on the half-and-half method were (86.04±3.15)%, (83.66±6.57)%, (88.54±4.37)%, (85.96±4.44)% and (87.12±4.39)%, respectively. Conclusion Computer-aided diagnosis of EUS images is objective and non-invasive, which can improve the accuracy in differentiating AIP from CP. This technology provides a new valuable diagnostic tool for the clinical determination of AIP. Key words: Autoimmune pancreatitis; Chronic pancreatitis; Endoscopic ultrasonograghy; Digital imaging processing; Support vector machine