title: Neural network analysis of dynamic sequences of EUS elastography used for the differential diagnosis of chronic pancreatitis and pancreatic cancer.
abstract: BACKGROUND
  EUS elastography is a newly developed imaging procedure that characterizes the differences of hardness and strain between diseased and normal tissue.
  
  
  OBJECTIVE
  To assess the accuracy of real-time EUS elastography in pancreatic lesions.
  
  
  METHODS
  Cross-sectional feasibility study.
  
  
  METHODS
  The study group included, in total, 68 patients with normal pancreas (N = 22), chronic pancreatitis (N = 11), pancreatic adenocarcinoma (N = 32), and pancreatic neuroendocrine tumors (N = 3). A subgroup analysis of 43 cases with focal pancreatic masses was also performed.
  
  
  METHODS
  A postprocessing software analysis was used to examine the EUS elastography movies by calculating hue histograms of each individual image, data that were further subjected to an extended neural network analysis to differentiate benign from malignant patterns.
  
  
  METHODS
  To differentiate normal pancreas, chronic pancreatitis, pancreatic cancer, and neuroendocrine tumors.
  
  
  RESULTS
  Based on a cutoff of 175 for the mean hue histogram values recorded on the region of interest, the sensitivity, specificity, and accuracy of differentiation of benign and malignant masses were 91.4%, 87.9%, and 89.7%, respectively. The positive and negative predictive values were 88.9% and 90.6%, respectively. Multilayer perceptron neural networks with both one and two hidden layers of neurons (3-layer perceptron and 4-layer perceptron) were trained to learn how to classify cases as benign or malignant, and yielded an excellent testing performance of 95% on average, together with a high training performance that equaled 97% on average.
  
  
  CONCLUSIONS
  A lack of the surgical standard in all cases.
  
  
  CONCLUSIONS
  EUS elastography is a promising method that allows characterization and differentiation of normal pancreas, chronic pancreatitis, and pancreatic cancer. The currently developed methodology, based on artificial neural network processing of EUS elastography digitalized movies, enabled an optimal prediction of the types of pancreatic lesions. Future multicentric, randomized studies with adequate power will have to establish the clinical impact of this procedure for the differential diagnosis of focal pancreatic masses.
authors.affiliation: Department of Gastroenterology, University of Medicine and Pharmacy Craiova, Craiova, Dolj, Romania.
authors.forename: Adrian
authors.initials: A
authors.lastname: Săftoiu
authors.forename: Peter
authors.initials: P
authors.lastname: Vilmann
authors.forename: Florin
authors.initials: F
authors.lastname: Gorunescu
authors.forename: Dan Ionuţ
authors.initials: DI
authors.lastname: Gheonea
authors.forename: Marina
authors.initials: M
authors.lastname: Gorunescu
authors.forename: Tudorel
authors.initials: T
authors.lastname: Ciurea
authors.forename: Gabriel Lucian
authors.initials: GL
authors.lastname: Popescu
authors.forename: Alexandru
authors.initials: A
authors.lastname: Iordache
authors.forename: Hazem
authors.initials: H
authors.lastname: Hassan
authors.forename: Sevastiţa
authors.initials: S
authors.lastname: Iordache
mesh_terms: D003430:Cross-Sectional Studies; D003937:Diagnosis, Differential; D054459:Elasticity Imaging Techniques / Q000379:methods*; D019160:Endosonography; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010190:Pancreatic Neoplasms / Q000000981:diagnostic imaging*; D050500:Pancreatitis, Chronic / Q000000981:diagnostic imaging*; D011446:Prospective Studies