Literature DB >> 27859956

Noninvasive hepatic fibrosis staging using mr elastography: The usefulness of the bayesian prediction method.

Shintaro Ichikawa1, Utaroh Motosugi1, Nobuyuki Enomoto2, Masanori Matsuda3, Hiroshi Onishi1.   

Abstract

PURPOSE: To evaluate the usefulness of the Bayesian method for hepatic fibrosis staging with magnetic resonance elastography (MRE).
MATERIALS AND METHODS: The sample of this retrospective study comprised patients with chronic liver disease (n = 309), in whom histopathological fibrosis staging and MRE using either a 1.5T (n = 214) or a 3T magnetic resonance imaging (MRI) system (n = 95) had been performed. The optimal cutoff stiffness value was determined and used to calculate the discrimination ability of fibrosis staging by the cutoff method. The Bayesian method calculated post-MRE probability of each fibrosis stage, yielding MRE-based fibrosis staging without a cutoff value as well as the confidence of staging. We compared the discrimination ability in all patients and in a subgroup of patients with high (≥90%) posterior probability.
RESULTS: The discrimination ability for hepatic fibrosis staging was comparable between the Bayesian method and the cutoff method in all patients because the accuracy of staging with the Bayesian method and the cutoff method in all patients was not different (P = 1.0000). However, in patients with high posterior probability by the Bayesian method, the accuracy of staging with the Bayesian method was significantly improved compared with that of the cutoff method in all patients; for discriminating stage ≥F2 from F0-F1 (98.9% vs. 94.8%, P = 0.0069); for ≥F3 (99.6% vs. 92.6%, P < 0.0001); and for F4 (100% vs. 94.2%, P = 0.0002).
CONCLUSION: The Bayesian method has a highly accurate discrimination ability for noninvasive hepatic fibrosis staging using MRE, if the posterior probability is high. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:375-382.
© 2016 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  hepatic fibrosis; magnetic resonance elastography; posterior probability; stiffness; the Bayesian method

Mesh:

Year:  2016        PMID: 27859956     DOI: 10.1002/jmri.25551

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  5 in total

1.  Diagnostic value of spleen stiffness by magnetic resonance elastography for prediction of esophageal varices in cirrhotic patients.

Authors:  Zih-En Jhang; Kuan-Lin Wu; Chia-Bang Chen; Yao-Li Chen; Ping-Yi Lin; Chen-Te Chou
Journal:  Abdom Radiol (NY)       Date:  2020-07-16

Review 2.  Comparison of gradient-recalled echo and spin-echo echo-planar imaging MR elastography in staging liver fibrosis: a meta-analysis.

Authors:  Yong Seek Kim; Yu Na Jang; Ji Soo Song
Journal:  Eur Radiol       Date:  2017-11-21       Impact factor: 5.315

3.  Magnetic resonance elastography can predict development of hepatocellular carcinoma with longitudinally acquired two-point data.

Authors:  Shintaro Ichikawa; Utaroh Motosugi; Nobuyuki Enomoto; Hiroshi Onishi
Journal:  Eur Radiol       Date:  2018-07-24       Impact factor: 5.315

4.  Feasibility of measuring spleen stiffness with MR elastography and splenic volume to predict hepatic fibrosis stage.

Authors:  Yi-Wen Cheng; Ya-Chien Chang; Yao-Li Chen; Ran-Chou Chen; Chen-Te Chou
Journal:  PLoS One       Date:  2019-05-31       Impact factor: 3.240

5.  Prediction of Hepatocellular Carcinoma by Liver Stiffness Measurements Using Magnetic Resonance Elastography After Eradicating Hepatitis C Virus.

Authors:  Takashi Kumada; Hidenori Toyoda; Satoshi Yasuda; Yasuhiro Sone; Sadanobu Ogawa; Kenji Takeshima; Toshifumi Tada; Takanori Ito; Yoshio Sumida; Junko Tanaka
Journal:  Clin Transl Gastroenterol       Date:  2021-04-23       Impact factor: 4.396

  5 in total

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