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).

Design and setting: A prospective study of eligible patients was conducted to analyze the RF data obtained by using electronic array echoendoscopes.

Patients: 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.

Main outcome measurements: 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.

Limitations: 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.