Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue

Gastrointestinal Endoscopy
Ananya DasBaoxin Li

Abstract

Concomitant changes of chronic pancreatitis markedly degrade the performance of EUS in diagnosing pancreatic adenocarcinoma (PC). Digital image analysis (DIA) of the spatial distribution of pixels in a US image has been used as an effective approach to tissue characterization. We applied the techniques of DIA to EUS images of the pancreas to develop a classification model capable of differentiating pancreatic adenocarcinoma from non-neoplastic tissue. Representative regions of interest were digitally selected in EUS images of 3 groups of patients with normal pancreas (group I), chronic pancreatitis (group II), and pancreatic adenocarcinoma (group III). Texture analyses were then performed by using image analysis software. Principal component analysis (PCA) was used for data reduction, and, later, a neural-network-based predictive model was built, trained, and validated. Tertiary academic medical center. Patients undergoing EUS of the pancreas. A total of 110, 99, and 110 regions of interest in groups I, II, III, respectively, were available for analysis. For each region, a total of 256 statistical parameters were extracted. Eleven parameters were subsequently retained by PCA. A neural network model was built, trained by using t...Continue Reading

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Citations

Jun 18, 2010·World Journal of Gastroenterology : WJG·Tyler Stevens, Mansour A Parsi
Oct 4, 2011·Clinical Gastroenterology and Hepatology : the Official Clinical Practice Journal of the American Gastroenterological Association·Adrian SăftoiuUNKNOWN European EUS Elastography Multicentric Study Group
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