Characterization of the pancreas in vivo using EUS spectrum analysis with electronic array echoendoscopes

Spectral analysis of the radiofrequency (RF) signals that underlie grayscale EUS images has been used to provide quantitative, objective information about tissue histology.

Our purpose was to validate RF spectral analysis as a method to distinguish between chronic pancreatitis (CP) and pancreatic cancer (PC).

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

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.

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.

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.

Moderate sample size and spatial averaging inherent in the technique.

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.