BACKGROUND: EUS is limited by variability in the examiner's subjective interpretation of B-scan images to differentiate among normal, inflammatory, and malignant tissue. By using information otherwise discarded by conventional EUS systems, quantitative spectral analysis of the raw radiofrequency (RF) signals underlying EUS images enables tissue to be characterized more objectively. OBJECTIVE: Our purpose was to determine the feasibility of using spectral analysis of EUS data for characterization of pancreatic tissue and lymph nodes. DESIGN AND SETTING: A pilot study of eligible patients was conducted to analyze the RF data obtained during EUS by using spectral parameters. PATIENTS: Twenty-one subjects who underwent EUS of the esophagus, stomach, pancreas, and surrounding intra-abdominal and mediastinal lymph nodes. MAIN OUTCOME MEASUREMENTS: Linear regression parameters of calibrated power spectra of the RF signals were tested to differentiate normal pancreas from chronic pancreatitis and from pancreatic cancer as well as benign from malignant-appearing lymph nodes. RESULTS: The mean intercept, slope, and midband fit of the spectra differed significantly among normal pancreas, adenocarcinoma, and chronic pancreatitis when all were compared with each other (P < .01). On direct comparison, mean midband fit for adenocarcinoma differed significantly from that for chronic pancreatitis (P < .05). For lymph nodes, mean midband fit and intercept differed significantly between benign- and malignant-appearing lymph nodes (P < .01 and P < .05, respectively). LIMITATIONS: Small sample population and spatial averaging inherent to this technique. CONCLUSIONS: Mean spectral parameters in EUS imaging can provide a noninvasive method to discriminate normal from diseased pancreas and lymph nodes.
BACKGROUND: EUS is limited by variability in the examiner's subjective interpretation of B-scan images to differentiate among normal, inflammatory, and malignant tissue. By using information otherwise discarded by conventional EUS systems, quantitative spectral analysis of the raw radiofrequency (RF) signals underlying EUS images enables tissue to be characterized more objectively. OBJECTIVE: Our purpose was to determine the feasibility of using spectral analysis of EUS data for characterization of pancreatic tissue and lymph nodes. DESIGN AND SETTING: A pilot study of eligible patients was conducted to analyze the RF data obtained during EUS by using spectral parameters. PATIENTS: Twenty-one subjects who underwent EUS of the esophagus, stomach, pancreas, and surrounding intra-abdominal and mediastinal lymph nodes. MAIN OUTCOME MEASUREMENTS: Linear regression parameters of calibrated power spectra of the RF signals were tested to differentiate normal pancreas from chronic pancreatitis and from pancreatic cancer as well as benign from malignant-appearing lymph nodes. RESULTS: The mean intercept, slope, and midband fit of the spectra differed significantly among normal pancreas, adenocarcinoma, and chronic pancreatitis when all were compared with each other (P < .01). On direct comparison, mean midband fit for adenocarcinoma differed significantly from that for chronic pancreatitis (P < .05). For lymph nodes, mean midband fit and intercept differed significantly between benign- and malignant-appearing lymph nodes (P < .01 and P < .05, respectively). LIMITATIONS: Small sample population and spatial averaging inherent to this technique. CONCLUSIONS: Mean spectral parameters in EUS imaging can provide a noninvasive method to discriminate normal from diseased pancreas and lymph nodes.
Authors: R E Kumon; A Repaka; M Atkinson; A L Faulx; R C K Wong; G A Isenberg; Y-S Hsiao; M S R Gudur; C X Deng; A Chak Journal: Endoscopy Date: 2012-05-25 Impact factor: 10.093
Authors: Ronald E Kumon; Aparna Repaka; Matthew Atkinson; Ashley L Faulx; Richard C K Wong; Gerard A Isenberg; Yi-Sing Hsiao; Madhu S R Gudur; Cheri X Deng; Amitabh Chak Journal: Gastrointest Endosc Date: 2012-04-11 Impact factor: 9.427
Authors: Ronald E Kumon; Michael J Pollack; Ashley L Faulx; Kayode Olowe; Farees T Farooq; Victor K Chen; Yun Zhou; Richard C K Wong; Gerard A Isenberg; Michael V Sivak; Amitabh Chak; Cheri X Deng Journal: Gastrointest Endosc Date: 2009-11-17 Impact factor: 9.427