Adrian Săftoiu1, Peter Vilmann2, Christoph F Dietrich3, Julio Iglesias-Garcia4, Michael Hocke5, Andrada Seicean6, Andre Ignee7, Hazem Hassan2, Costin Teodor Streba8, Ana Maria Ioncică8, Dan Ionuţ Gheonea8, Tudorel Ciurea8. 1. Research Center of Gastroenterology and Hepatology of Craiova, University of Medicine and Pharmacy, Craiova, Romania; Endoscopy Department, Copenhagen University Hospital, Herlev, Denmark. 2. Endoscopy Department, Copenhagen University Hospital, Herlev, Denmark. 3. Sino-German Research Center of Ultrasound in Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Medical D 2, Caritas-Krankenhaus Bad, Mergentheim, Germany. 4. Gastroenterology Department, University Hospital Santiago de Compostela, Coruña, Spain. 5. Internal Medicine II, Hospital Meiningen, Meiningen, Germany. 6. Regional Institute of Gastroenterology and Hepatology, University of Medicine and Pharmacy "Iuliu Haţieganu" Cluj-Napoca, Romania. 7. Medical D 2, Caritas-Krankenhaus Bad, Mergentheim, Germany. 8. Research Center of Gastroenterology and Hepatology of Craiova, University of Medicine and Pharmacy, Craiova, Romania.
Abstract
BACKGROUND: The role of EUS with contrast agents can be expanded through the use of time-intensity curve (TIC) analysis and computer-aided interpretation. OBJECTIVE: To validate the use of parameters derived from TIC analysis in an artificial neural network (ANN) classification model designed to diagnose pancreatic carcinoma (PC) and chronic pancreatitis (CP). SETTING: Prospective, multicenter, observational trial-endoscopy units from Romania, Denmark, Germany, and Spain. PATIENTS: A total of 167 consecutive patients with PC or CP. INTERVENTIONS: Contrast-enhanced harmonic EUS (CEH-EUS) and EUS-guided FNA (EUS-FNA), TIC analysis, and ANN processing. MAIN OUTCOME MEASUREMENTS: Sensitivity, specificity, positive and negative predictive values (PPV, NPV) for EUS-FNA, CEH-EUS, and the ANN. RESULTS: After excluding all of the recordings that did not meet the technical and procedural criteria, 112 cases of PC and 55 cases of CP were included. EUS-FNA was performed in 129 patients, and the diagnosis was confirmed by surgery (n = 15) or follow-up (n = 23) in the remaining cases. Its sensitivity and specificity were 84.82% and 100%, respectively, whereas the PPV and NPV were 100% and 76.63%, respectively. The sensitivity of real-time quantitative assessment of CEH-EUS was 87.5%, specificity 92.72%, PPV 96.07%, and NPV 78.46%. Peak enhancement, wash-in area under the curve, wash-in rate, and the wash-in perfusion index were significantly different between the groups. No significant differences were found between rise time, mean transit time, and time to peak. For the ANN, sensitivity was 94.64%, specificity 94.44%, PPV 97.24%, and NPV 89.47%. LIMITATIONS: Only PC and CP lesions were included. CONCLUSION: Parameters obtained through TIC analysis can differentiate between PC and CP cases and can be used in an automated computer-aided diagnostic system with good diagnostic results. ( CLINICAL TRIAL REGISTRATION NUMBER: NCT01315548.).
BACKGROUND: The role of EUS with contrast agents can be expanded through the use of time-intensity curve (TIC) analysis and computer-aided interpretation. OBJECTIVE: To validate the use of parameters derived from TIC analysis in an artificial neural network (ANN) classification model designed to diagnose pancreatic carcinoma (PC) and chronic pancreatitis (CP). SETTING: Prospective, multicenter, observational trial-endoscopy units from Romania, Denmark, Germany, and Spain. PATIENTS: A total of 167 consecutive patients with PC or CP. INTERVENTIONS: Contrast-enhanced harmonic EUS (CEH-EUS) and EUS-guided FNA (EUS-FNA), TIC analysis, and ANN processing. MAIN OUTCOME MEASUREMENTS: Sensitivity, specificity, positive and negative predictive values (PPV, NPV) for EUS-FNA, CEH-EUS, and the ANN. RESULTS: After excluding all of the recordings that did not meet the technical and procedural criteria, 112 cases of PC and 55 cases of CP were included. EUS-FNA was performed in 129 patients, and the diagnosis was confirmed by surgery (n = 15) or follow-up (n = 23) in the remaining cases. Its sensitivity and specificity were 84.82% and 100%, respectively, whereas the PPV and NPV were 100% and 76.63%, respectively. The sensitivity of real-time quantitative assessment of CEH-EUS was 87.5%, specificity 92.72%, PPV 96.07%, and NPV 78.46%. Peak enhancement, wash-in area under the curve, wash-in rate, and the wash-in perfusion index were significantly different between the groups. No significant differences were found between rise time, mean transit time, and time to peak. For the ANN, sensitivity was 94.64%, specificity 94.44%, PPV 97.24%, and NPV 89.47%. LIMITATIONS: Only PC and CP lesions were included. CONCLUSION: Parameters obtained through TIC analysis can differentiate between PC and CP cases and can be used in an automated computer-aided diagnostic system with good diagnostic results. ( CLINICAL TRIAL REGISTRATION NUMBER: NCT01315548.).
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