Combined contrast-enhanced power Doppler and real-time sonoelastography performed during EUS, used in the differential diagnosis of focal pancreatic masses (with videos).

BACKGROUND: Contrast-enhanced power Doppler (CEPD) and real-time sonoelastography (RTSE) performed during EUS were previously described to be useful for the differential diagnosis between chronic pseudotumoral pancreatitis and pancreatic cancer. OBJECTIVE: To prospectively assess the accuracy of the combination of CEPD and RTSE to differentiate pancreatic focal masses. DESIGN: Cross-sectional feasibility study. SETTING: A tertiary-care academic referral center. PATIENTS: The study group included 54 patients with chronic pancreatitis (n = 21) and pancreatic adenocarcinoma (n = 33). INTERVENTIONS: Both imaging methods (CEPD and RTSE) were performed sequentially during the same EUS examination. Power Doppler mode examination was performed after intravenous injection of a second-generation contrast agent (2.4 mL of SonoVue), and the data were digitally recorded, comprising both the early arterial phase and venous/late phase. Three 10-second sonoelastographic videos were also digitally recorded that included the focal mass and the surrounding pancreatic parenchyma. Postprocessing analyses based on specially designed software were used to analyze the CEPD and RTSE videos. A power Doppler vascularity index was used to characterize CEPD videos, the values being averaged during a 10-second video in the venous phase. Hue histogram analysis was used to characterize RTSE videos, with the mean hue histogram values being also averaged during a 10-second video. MAIN OUTCOME MEASUREMENTS: To differentiate chronic pancreatitis and pancreatic cancer. RESULTS: The sensitivity, specificity, and accuracy of combined information provided by CEPD and RTSE to differentiate hypovascular hard masses suggestive of pancreatic carcinoma were 75.8%, 95.2%, and 83.3%, respectively, with a positive predictive value and negative predictive value of 96.2% and 71.4%, respectively. LIMITATION: A single-center, average size of study population. CONCLUSIONS: A combination of CEPD and RTSE performed during EUS seems to be a promising method that allows characterization and differentiation of focal pancreatic masses.

Real-time sono-elastography in the diagnosis of diffuse liver diseases.

AIM: To analyze whether computer-enhanced dynamic analysis of elastography movies is able to better characterize and differentiate between different degrees of liver fibrosis. METHODS: The study design was prospective. A total of 132 consecutive patients with chronic liver diseases and healthy volunteers were examined by transabdominal ultrasound elastography. All examinations were done by two doctors. RESULTS: Due to the limitations of the method, we obtained high-quality elastography information in only 73.48% of the patients. The kappa-means clustering method was applied to assess the inter-observer diagnosis variability, which showed good variability values in accordance with the experience of ultrasound examination of every observer. Cohen's kappa test indicated a moderate agreement between the study observers (kappa = 0.4728). Furthermore, we compared the way the two observers clustered the patients, using the test for comparing two proportions (t value, two-sided test). There was no statistically significant difference between the two physicians, regardless of the patients' real status. CONCLUSION: Transabdominal real-time elastography is certainly a very useful method in depicting liver hardness, although it is incompletely tested in large multicenter studies.

Neural network analysis of dynamic sequences of EUS elastography used for the differential diagnosis of chronic pancreatitis and pancreatic cancer.

BACKGROUND: EUS elastography is a newly developed imaging procedure that characterizes the differences of hardness and strain between diseased and normal tissue. OBJECTIVE: To assess the accuracy of real-time EUS elastography in pancreatic lesions. DESIGN: Cross-sectional feasibility study. PATIENTS: The study group included, in total, 68 patients with normal pancreas (N = 22), chronic pancreatitis (N = 11), pancreatic adenocarcinoma (N = 32), and pancreatic neuroendocrine tumors (N = 3). A subgroup analysis of 43 cases with focal pancreatic masses was also performed. INTERVENTIONS: A postprocessing software analysis was used to examine the EUS elastography movies by calculating hue histograms of each individual image, data that were further subjected to an extended neural network analysis to differentiate benign from malignant patterns. MAIN OUTCOME MEASUREMENTS: To differentiate normal pancreas, chronic pancreatitis, pancreatic cancer, and neuroendocrine tumors. RESULTS: Based on a cutoff of 175 for the mean hue histogram values recorded on the region of interest, the sensitivity, specificity, and accuracy of differentiation of benign and malignant masses were 91.4%, 87.9%, and 89.7%, respectively. The positive and negative predictive values were 88.9% and 90.6%, respectively. Multilayer perceptron neural networks with both one and two hidden layers of neurons (3-layer perceptron and 4-layer perceptron) were trained to learn how to classify cases as benign or malignant, and yielded an excellent testing performance of 95% on average, together with a high training performance that equaled 97% on average. LIMITATION: A lack of the surgical standard in all cases. CONCLUSIONS: EUS elastography is a promising method that allows characterization and differentiation of normal pancreas, chronic pancreatitis, and pancreatic cancer. The currently developed methodology, based on artificial neural network processing of EUS elastography digitalized movies, enabled an optimal prediction of the types of pancreatic lesions. Future multicentric, randomized studies with adequate power will have to establish the clinical impact of this procedure for the differential diagnosis of focal pancreatic masses.

Dynamic analysis of EUS used for the differentiation of benign and malignant lymph nodes.

BACKGROUND: EUS elastography was reported to offer supplemental information that allows a better characterization of tissue, and that might enhance conventional EUS imaging. OBJECTIVE: Our purpose was to apply real-time elastography during EUS examinations and to assess the accuracy of the differentiation of benign versus malignant lymph nodes. DESIGN: Prospective cross-sectional feasibility study. SETTING: Department of Surgical Gastroenterology, Gentofte University Hospital, Hellerup, Denmark. PATIENTS: Patients diagnosed by EUS with cervical, mediastinal, or abdominal lymph nodes were included, with a total number of 78 lymph nodes examined. The final diagnosis of the type of lymph node was obtained by EUS-FNA cytologic analysis or by surgical pathologic examination and by a minimum 6 months of follow-up. INTERVENTIONS: Hue histogram analysis of the average images computed from EUS elastography movies was used to assess the color information inside the region of interest and to consequently differentiate benign and malignant lymph nodes. MAIN OUTCOME MEASUREMENTS: Differentiate between malignant and benign lymph nodes. RESULTS: By using mean hue histogram values, the sensitivity, specificity, and accuracy for the differential diagnosis were 85.4%, 91.9%, and 88.5%, respectively, on the basis of a cutoff level of 166 (middle of green-blue rainbow scale). The proposed method might be useful to avoid color perception errors, moving artifacts, or possible selection bias induced by analysis of still images. LIMITATIONS: Lack of the surgical standard in all cases. CONCLUSIONS: Computer-enhanced dynamic analysis based on hue histograms of the EUS elastography movies represents a promising method that allows the differential diagnosis of benign and malignant lymph nodes, offering complementary information added to conventional EUS imaging.