Hepatitis Research Today is a free monthly online journal that collates and summarizes the latest research about Hepatitis, including details on hepatitis a, b, c, causes, symptoms.

Compensated hepatitis C: unenhanced MR imaging correlated with pathologic grading and staging.

Mitchell DG, Navarro VJ, Herrine SK, Bergin D, Parker L, Frangos A, McCue P, Rubin R

Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA. donald.mitchell@jefferson.edu

BACKGROUND: We prospectively examined unenhanced MR imaging findings in relation to pathologic fibrosis, inflammation and steatosis in patients with compensated chronic hepatitis C viral infection (HCV). METHODS: Unenhanced MRI at 1.5 T was obtained within one month of core liver biopsy in 64 consecutive candidates for antiviral therapy for compensated HCV. Two pathologists independently graded inflammatory activity index (HAI) and steatosis, and staged fibrosis (grades 0-6). Morphologic MRI findings of cirrhosis, periportal lymph nodes, and MR fat signal ratio from dual gradient echo images were assessed independently by two radiologists blinded to clinical data. MRI and laboratory liver function results were correlated with pathologic results, using Spearman correlation coefficient and stepwise multiple regression. RESULTS: MR fat signal ratio correlation coefficient with pathologic steatosis was 0.71 (p < 0.0001). Coefficients with fibrosis stage were highest for surface nodularity (r (s) = 47, p < 0.0001) and expanded gallbladder fossa (r (s) = 0.42, p = 0.0006). Coefficients with HAI were highest for lymph node size (r (s) = 0.355, p = 0.0040), surface nodularity (r = 0.47, p < 0.0001), expanded gallbladder fossa (r = 0.332, p = 0.0073), and caudate/right lobe ratio (r = 0.326, p = 0.0110). Combined lab and MRI variables provided the best prediction of fibrosis stage (r (2) = 0.656) and HAI (r (2) = 0.597). CONCLUSIONS: A combination of MRI and laboratory findings was most predictive of fibrosis and inflammation.

Published 21 December 2007 in Abdom Imaging, 33(1): 58-64.
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