title: Characterization of the pancreas in vivo using EUS spectrum analysis with electronic array echoendoscopes.
abstract: BACKGROUND
  Spectral analysis of the radiofrequency (RF) signals that underlie grayscale EUS images has been used to provide quantitative, objective information about tissue histology.
  
  
  OBJECTIVE
  Our purpose was to validate RF spectral analysis as a method to distinguish between chronic pancreatitis (CP) and pancreatic cancer (PC).
  
  
  METHODS
  A prospective study of eligible patients was conducted to analyze the RF data obtained by using electronic array echoendoscopes.
  
  
  METHODS
  Pancreatic images were obtained by using electronic array echoendoscopes from 41 patients in a prospective study, including 15 patients with PC, 15 with CP, and 11 with a normal pancreas.
  
  
  METHODS
  Midband fit, slope, intercept, correlation coefficient, and root mean square deviation from a linear regression of the calibrated power spectra were determined and compared among the groups.
  
  
  RESULTS
  Statistical analysis showed that significant differences were observable between groups for mean midband fit, intercept, and root mean square deviation (t test, P < .05). Discriminant analysis of these parameters was then performed to classify the data. For CP (n = 15) versus PC (n = 15), the same parameters provided 83% accuracy and an area under the curve of 0.83.
  
  
  CONCLUSIONS
  Moderate sample size and spatial averaging inherent in the technique.
  
  
  CONCLUSIONS
  This study shows that mean spectral parameters of the backscattered signals obtained by using electronic array echoendoscopes can provide a noninvasive method to quantitatively discriminate between CP and PC.
authors.affiliation: Department of Physics, Kettering University, Flint, MI 48504-6214, USA. research@kumonweb.com
authors.forename: Ronald E
authors.initials: RE
authors.lastname: Kumon
authors.forename: Aparna
authors.initials: A
authors.lastname: Repaka
authors.forename: Matthew
authors.initials: M
authors.lastname: Atkinson
authors.forename: Ashley L
authors.initials: AL
authors.lastname: Faulx
authors.forename: Richard C K
authors.initials: RC
authors.lastname: Wong
authors.forename: Gerard A
authors.initials: GA
authors.lastname: Isenberg
authors.forename: Yi-Sing
authors.initials: YS
authors.lastname: Hsiao
authors.forename: Madhu S R
authors.initials: MS
authors.lastname: Gudur
authors.forename: Cheri X
authors.initials: CX
authors.lastname: Deng
authors.forename: Amitabh
authors.initials: A
authors.lastname: Chak
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D016002:Discriminant Analysis; D019160:Endosonography / Q000295:instrumentation / Q000379:methods*; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D010179:Pancreas / Q000000981:diagnostic imaging* / Q000473:pathology; D010190:Pancreatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D050500:Pancreatitis, Chronic / Q000000981:diagnostic imaging* / Q000473:pathology; D012372:ROC Curve