Short-term changes observed in multiparametric liver MRI following therapy with direct-acting antivirals in chronic hepatitis C virus patients

Abstract

Methods: We applied multiparametric MRI to assess changes in liver composition, perfusion and blood flow in 17 patients before direct-acting antiviral (DAA) therapy and after treatment completion (within 12 weeks of last DAA tablet swallowed). Results: We observed changes in hepatic composition indicated by a reduction in both liver longitudinal relaxation time (T1, 35 ± 4 ms), transverse relaxation time (T2, 2.5 ± 0.8 ms; T2* 3.0 ± 0.7 ms), and liver perfusion (28.1 ± 19.7 ml/100 g/min) which we suggest are linked to reduced pro-inflammatory milieu, including interstitial oedema, within the liver. No changes were observed in liver or spleen blood flow, splenic perfusion, or superior mesenteric artery blood flow. Conclusion: For the first time, our study has shown that treatment of HCV with DAAs in patients with cirrhosis leads to an acute reduction in liver T1, T2 and T2* and an increase in liver perfusion measured using MR parameters. The ability of MRI to characterise changes in the angio-architecture of patients with cirrhosis after intervention in the short term will enhance our understanding of the natural history of regression of liver disease and potentially influence clinical decision algorithms. Key Points: • DAAs have revolutionised the treatment of hepatitis C and achieve sustained virological response in over 95% of patients, even with liver cirrhosis. • Currently available non-invasive measures of liver fibrosis are not accurate after HCV treatment with DAAs, this prospective single-centre study has shown that MRI can sensitively measure changes within the liver, which could reflect the reduction in inflammation with viral clearance. • The ability of MRI to characterise changes in structural and haemodynamic MRI measures in the liver after intervention will enhance our understanding of the progression/regression of liver disease and could potentially influence clinical decision algorithms.