Ananya Das1, Cuong C Nguyen, Feng Li, Baoxin Li. 1. Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic Arizona, Scottsdale, Arizona 85259, USA.
Abstract
BACKGROUND: 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. OBJECTIVE: 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. DESIGN: 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. SETTING: Tertiary academic medical center. PATIENTS: Patients undergoing EUS of the pancreas. RESULTS: 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 these parameters as input variables for prediction of PC, and then validated in the remainder of the data set. This model was very accurate in classifying PC with an area under the receiver operating characteristic curve of 0.93. LIMITATION: Exploratory study with a small number of patients. CONCLUSIONS: DIA of EUS images is accurate in differentiating PC from chronic inflammation and normal tissue. With the potential availability of real-time application, DIA can develop into a useful clinical diagnostic tool in pancreatic diseases and in certain situations may obviate EUS-guided FNA.
BACKGROUND: 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. OBJECTIVE: 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. DESIGN: 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. SETTING: Tertiary academic medical center. PATIENTS: Patients undergoing EUS of the pancreas. RESULTS: 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 these parameters as input variables for prediction of PC, and then validated in the remainder of the data set. This model was very accurate in classifying PC with an area under the receiver operating characteristic curve of 0.93. LIMITATION: Exploratory study with a small number of patients. CONCLUSIONS: DIA of EUS images is accurate in differentiating PC from chronic inflammation and normal tissue. With the potential availability of real-time application, DIA can develop into a useful clinical diagnostic tool in pancreatic diseases and in certain situations may obviate EUS-guided FNA.
Authors: Ronald E Kumon; Aparna Repaka; Matthew Atkinson; Ashley L Faulx; Richard C K Wong; Gerard A Isenberg; Yi-Sing Hsiao; Madhu S R Gudur; Cheri X Deng; Amitabh Chak Journal: Gastrointest Endosc Date: 2012-04-11 Impact factor: 9.427
Authors: Ronald E Kumon; Michael J Pollack; Ashley L Faulx; Kayode Olowe; Farees T Farooq; Victor K Chen; Yun Zhou; Richard C K Wong; Gerard A Isenberg; Michael V Sivak; Amitabh Chak; Cheri X Deng Journal: Gastrointest Endosc Date: 2009-11-17 Impact factor: 9.427