Barbara Schellhaas1, Thomas Bernatik2, Wolfram Bohle3, Fanny Borowitzka4, Johannes Chang5, Christoph F Dietrich6, Klaus Dirks7, Robert Donoval8, Kristine Drube9, Mireen Friedrich-Rust10, Christine Gall11, Fleur Gittinger12, Martin Gutermann13, Mark Martin Haenle14, Alexandra von Herbay15, Chau Hong Ho13, Rico Hochdoerffer16, Tatjana Hoffmann17, Matthias Hüttig18, Christopher Janson19, Ernst-Michael Jung20, Norbert Jung21, Thomas Karlas22, Christoph Klinger23, Adam Kornmehl24, Wolfgang Kratzer14, Sebastian Krug12, Georg Kunze25, Jens Leitlein26, Alexander Link27, Christian Lottspeich28, Aldo Marano29, Martin Mauch30, Lukas Moleda31, Albrecht Neesse32, Golo Petzold32, Andrej Potthoff33, Michael Praktiknjo5, Klaus-Dieter Rösner34, Stefan Schanz35, Michael Schultheiß36, Visvakanth Sivanathan37, Joachim Stock38, Thomas Thomsen39, Johanna Vogelpohl40, Christoph Vogt41, Siegfried Wagner42, Christiane Wiegard43, Isabel Wiesinger31, Uwe Will44, Matthias Ziesch45, Patrick Zimmermann44, Deike Strobel1. 1. Department of Internal Medicine 1, University Hospital Erlangen, Germany. 2. Department of Internal Medicine 1, Kreisklinik Ebersberg gGmbH, Ebersberg, Germany. 3. Klinik für Allgemeine Innere Medizin, Gastroenterologie, Hepatologie und Infektiologie, Katharinenhospital, Klinikum Stuttgart, Germany. 4. Department of Internal Medicine 2, Universitätsmedizin Rostock, Germany. 5. Department of Internal Medicine I, University Hospital Bonn, Germany. 6. Medizinische Klinik 2, Caritas-Krankenhaus, Bad Mergentheim, Germany. 7. Gastroenterologie und Innere Medizin, Rems-Murr-Klinikum Winnenden, Germany. 8. Klinik für Gastroenterologie, Diabetologie und Infektiologie, Lausitzer Seenland Klinikum GmbH, Hoyerswerda, Germany. 9. Department of Internal Medicine, Allgemeines Krankenhaus Celle, Germany. 10. Department of Internal Medicine 1, J.W. Goethe University Hospital, Frankfurt, Germany. 11. Institut für Medizininformatik, Biometrie und Epidemiologie, FAU IMBE, Erlangen, Germany. 12. Department of Internal Medicine, University Hospital Halle, Halle, Germany. 13. Department of Internal Medicine, Hufeland-Hospital, Mühlhausen, Germany. 14. Department of Internal Medicine, University Hospital Ulm, Germany. 15. Department of Internal Medicine, Evangelisches Krankenhaus Hamm gGmbH, Hamm, Germany. 16. Department of Internal Medicine, Städtisches Klinikum Karlsruhe gGmbH, Karlsruhe, Germany. 17. Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany. 18. Department of Internal Medicine, DRK-Kliniken Berlin-Köpenick, Berlin, Germany. 19. Department of Internal Medicine, Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany. 20. Department of Radiology, Universitätsklinikum Regensburg, Germany. 21. Department of Internal Medicine, Klinikum Heidenheim, Germany. 22. Department of Internal Medicine, Division of Gastroenterology, Universitätsklinikum Leipzig, Germany. 23. Medizinische Klinik I, Klinikum Ludwigsburg, Ludwigsburg, Germany. 24. Department of Internal Medicine, Klinikum Weiden, Germany. 25. Internal Medicine, KH Villingen-Schwenningen, Villingen-Schwenningen, Germany. 26. Department of Internal Medicine, Klinikum am Steinenberg Reutlingen, Germany. 27. Department of Internal Medicine, University Hospital Magdeburg, Germany. 28. Medical Clinic and Policlinic IV, Division of Vascular Medicine, Hospital of the Ludwig Maximilians University Hospital, Munich, Germany. 29. Department of Internal Medicine, ViDia Christliche Kliniken Karlsruhe, Germany. 30. Department of Internal Medicine, Innere, Kreisklinik Sigmaringen, Germany. 31. Department of Internal Medicine, Universitätsklinikum Regensburg, Germany. 32. Klinik für Gastroenterologie, gastrointestinale Onkologie und Endokrinologie, Universitätsmedizin Göttingen, Göttingen, Germany. 33. Gastroenterology and Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany. 34. Department of Internal Medicine, Barmherzige Brüder Klinikum Sankt Elisabeth Straubing GmbH, Straubing, Germany. 35. Department of Internal Medicine, Kreisklinikum Siegen gGmbH, Siegen, Germany. 36. Department of Internal Medicine, University of Freiburg Hospital, Freiburg, Germany. 37. Department of Internal Medicine, University Hospital Mainz, Department of Internal Medicine 3, Mainz, Germany. 38. Department of Internal Medicine, Klinikum Barnim, Eberswalde, Germany. 39. Department of Internal Medicine, Westküstenklinik Brunsbüttel, Brunsbüttel, Germany. 40. Department of Internal Medicine I, Krankenhaus GmbH Alb-Donau-Kreis Blaubeuren, Germany. 41. Department of Internal Medicine, St.-Josef-Krankenhaus Moers, Germany. 42. Department of Internal Medicine, Donau-Isar-Kliniken Deggendorf, Germany. 43. Department of Internal Medicine, University Hospital Hamburg Eppendorf Center of Internal Medicine, Hamburg, Germany. 44. Internal Medicine, Klinikum Gera, Gera, Germany. 45. Department of Internal Medicine, Diakonissenkrankenhaus Dresden, Germany.
Abstract
BACKGROUND: This prospective multicenter study funded by the DEGUM assesses the diagnostic accuracy of standardized contrast-enhanced ultrasound (CEUS) for the noninvasive diagnosis of hepatocellular carcinoma (HCC) in high-risk patients. METHODS: Patients at high risk for HCC with a histologically proven focal liver lesion on B-mode ultrasound were recruited prospectively in a multicenter approach. Clinical and imaging data were entered via online entry forms. The diagnostic accuracies for the noninvasive diagnosis of HCC were compared for the conventional interpretation of standardized CEUS at the time of the examination (= CEUS on-site) and the two CEUS algorithms ESCULAP (Erlanger Synopsis for Contrast-enhanced Ultrasound for Liver lesion Assessment in Patients at risk) and CEUS LI-RADS (Contrast-Enhanced UltraSound Liver Imaging Reporting and Data System). RESULTS: 321 patients were recruited in 43 centers; 299 (93.1 %) had liver cirrhosis. The diagnosis according to histology was HCC in 256 cases, and intrahepatic cholangiocarcinoma (iCCA) in 23 cases. In the subgroup of cirrhotic patients (n = 299), the highest sensitivity for the diagnosis of HCC was achieved with the CEUS algorithm ESCULAP (94.2 %) and CEUS on-site (90.9 %). The lowest sensitivity was reached with the CEUS LI-RADS algorithm (64 %; p < 0.001). However, the specificity of CEUS LI-RADS (78.9 %) was superior to that of ESCULAP (50.9 %) and CEUS on-site (64.9 %; p < 0.001). At the same time, the negative predictive value (NPV) of CEUS LI-RADS was significantly inferior to that of ESCULAP (34.1 % vs. 67.4 %; p < 0.001) and CEUS on-site (62.7 %; p < 0.001). The positive predictive values of all modalities were high (around 90 %), with the best results seen for CEUS LI-RADS and CEUS on-site. CONCLUSION: This is the first multicenter, prospective comparison of standardized CEUS and the recently developed CEUS-based algorithms in histologically proven liver lesions in cirrhotic patients. Our results reaffirm the excellent diagnostic accuracy of CEUS for the noninvasive diagnosis of HCC in high-risk patients. However, on-site diagnosis by an experienced examiner achieves an almost equal diagnostic accuracy compared to CEUS-based diagnostic algorithms. Thieme. All rights reserved.
BACKGROUND: This prospective multicenter study funded by the DEGUM assesses the diagnostic accuracy of standardized contrast-enhanced ultrasound (CEUS) for the noninvasive diagnosis of hepatocellular carcinoma (HCC) in high-risk patients. METHODS: Patients at high risk for HCC with a histologically proven focal liver lesion on B-mode ultrasound were recruited prospectively in a multicenter approach. Clinical and imaging data were entered via online entry forms. The diagnostic accuracies for the noninvasive diagnosis of HCC were compared for the conventional interpretation of standardized CEUS at the time of the examination (= CEUS on-site) and the two CEUS algorithms ESCULAP (Erlanger Synopsis for Contrast-enhanced Ultrasound for Liver lesion Assessment in Patients at risk) and CEUS LI-RADS (Contrast-Enhanced UltraSound Liver Imaging Reporting and Data System). RESULTS: 321 patients were recruited in 43 centers; 299 (93.1 %) had liver cirrhosis. The diagnosis according to histology was HCC in 256 cases, and intrahepatic cholangiocarcinoma (iCCA) in 23 cases. In the subgroup of cirrhotic patients (n = 299), the highest sensitivity for the diagnosis of HCC was achieved with the CEUS algorithm ESCULAP (94.2 %) and CEUS on-site (90.9 %). The lowest sensitivity was reached with the CEUS LI-RADS algorithm (64 %; p < 0.001). However, the specificity of CEUS LI-RADS (78.9 %) was superior to that of ESCULAP (50.9 %) and CEUS on-site (64.9 %; p < 0.001). At the same time, the negative predictive value (NPV) of CEUS LI-RADS was significantly inferior to that of ESCULAP (34.1 % vs. 67.4 %; p < 0.001) and CEUS on-site (62.7 %; p < 0.001). The positive predictive values of all modalities were high (around 90 %), with the best results seen for CEUS LI-RADS and CEUS on-site. CONCLUSION: This is the first multicenter, prospective comparison of standardized CEUS and the recently developed CEUS-based algorithms in histologically proven liver lesions in cirrhotic patients. Our results reaffirm the excellent diagnostic accuracy of CEUS for the noninvasive diagnosis of HCC in high-risk patients. However, on-site diagnosis by an experienced examiner achieves an almost equal diagnostic accuracy compared to CEUS-based diagnostic algorithms. Thieme. All rights reserved.
Authors: Da Xu; Rong Liu; Huiping Xu; Zhijian Zhang; Wei Li; Yi Zhang; Wenjun Zhang Journal: Comput Math Methods Med Date: 2022-04-30 Impact factor: 2.238
Authors: Tudor Voicu Moga; Ciprian David; Alina Popescu; Raluca Lupusoru; Darius Heredea; Ana M Ghiuchici; Camelia Foncea; Adrian Burdan; Roxana Sirli; Mirela Danilă; Iulia Ratiu; Teofana Bizerea-Moga; Ioan Sporea Journal: J Pers Med Date: 2021-12-20