title: Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue.
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.
  
  
  METHODS
  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.
  
  
  METHODS
  Tertiary academic medical center.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.affiliation: Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic Arizona, Scottsdale, Arizona 85259, USA.
authors.forename: Ananya
authors.initials: A
authors.lastname: Das
authors.forename: Cuong C
authors.initials: CC
authors.lastname: Nguyen
authors.forename: Feng
authors.initials: F
authors.lastname: Li
authors.forename: Baoxin
authors.initials: B
authors.lastname: Li
mesh_terms: D000230:Adenocarcinoma / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D019160:Endosonography / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010179:Pancreas / Q000000981:diagnostic imaging*; D010190:Pancreatic Neoplasms / Q000000981:diagnostic imaging*; D050500:Pancreatitis, Chronic / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity