In vivo characterization of pancreatic and lymph node tissue by using EUS spectrum analysis: a validation study

Background

Quantitative spectral analysis of the radiofrequency (RF) signals that underlie grayscale EUS images can be used to provide additional, objective information about tissue state.

Objective

Our purpose was to validate RF spectral analysis as a method to distinguish between (1) benign and malignant lymph nodes and (2) normal pancreas, chronic pancreatitis, and pancreatic cancer.

Design and Setting

A prospective validation study of eligible patients was conducted to compare with pilot study RF data.

Patients

Forty-three patients underwent EUS of the esophagus, stomach, pancreas, and surrounding intra-abdominal and mediastinal lymph nodes (19 from a previous pilot study and 24 additional patients).

Main Outcome Measurements

Midband fit, slope, intercept, and correlation coefficient from a linear regression of the calibrated RF power spectra were determined.

Results

Discriminant analysis of mean pilot-study parameters was then performed to classify validation-study parameters. For benign versus malignant lymph nodes, midband fit and intercept (both with t test P < .058) provided classification with 67% accuracy and area under the receiver operating curve (AUC) of 0.86. For diseased versus normal pancreas, midband fit and correlation coefficient (both with analysis of variance P < .001) provided 93% accuracy and an AUC of 0.98. For pancreatic cancer versus chronic pancreatitis, the same parameters provided 77% accuracy and an AUC of 0.89. Results improved further when classification was performed with all data.

Limitations

Moderate sample size and spatial averaging inherent to the technique.

Conclusions

This study confirms that mean spectral parameters provide a noninvasive method to quantitatively discriminate benign and malignant lymph nodes as well as normal and diseased pancreas.

Abbreviations: ANOVA, analysis of variance, AUC, area under curve, CP, chronic pancreatitis, LDA, linear discriminant analysis, NP, normal pancreas, PC, pancreatic cancer, RF, radiofrequency, ROC, receiver operating characteristic, ROI, region of interest