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

JOURNAL OF MAGNETIC RESONANCE IMAGING(2017)

引用 15|浏览10
暂无评分
摘要
PurposeTo evaluate the usefulness of the Bayesian method for hepatic fibrosis staging with magnetic resonance elastography (MRE). Materials and MethodsThe 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. ResultsThe 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). ConclusionThe 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
更多
查看译文
关键词
magnetic resonance elastography,the Bayesian method,hepatic fibrosis,posterior probability,stiffness
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要