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肝纤维化无创诊断的进展及展望

  • 蒋永芳;李耐萍
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  • 中南大学湘雅二医院感染科,长沙 410007

收稿日期: 2010-12-24

  修回日期: 2011-05-09

  网络出版日期: 2011-06-08

Progress in Noninvasive Diagnosis of Liver Fibrosis in Patients

  • JIANG Yongfang;LI Naiping
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  • Department of Infectious Diseases, Second Xiangya Hospital, Central South University, Changsha 410007, China

Received date: 2010-12-24

  Revised date: 2011-05-09

  Online published: 2011-06-08

摘要

及时准确地评估肝纤维化程度,对慢性肝病的治疗及预后具有重要意义。目前最可靠的肝组织活检诊断本身存在许多问题,诸如肝脏病变的不均匀性而导致的取样误差,存在一定的损伤性使患者难以接受,很难反复取材,故而不能动态地观察肝纤维化及纤维化形成的情况。另外,目前还没有可靠的办法确定肝组织胶原的含量,仅根据肝内纤维增生的情况进行大概的估计,具有一定的局限性。近年来,组合应用多项指标诊断与评价肝纤维化,建立肝纤维化无创性综合指标诊断模型成为当前肝纤维化诊断的研究热点。为了更好地理解和应用这些诊断模型,本文对目前主要无创诊断的若干研究进展进行系统地总结分析,主要从血清学诊断和影像学诊断两个方面展开。在此基础上,对目前无创诊断的局限性进行总结,最后对肝纤维化无创诊断可能的发展方向进行展望。

本文引用格式

蒋永芳;李耐萍 . 肝纤维化无创诊断的进展及展望[J]. 科技导报, 2011 , 29(16) : 75 -79 . DOI: 10.3981/j.issn.1000-7857.2011.16.010

Abstract

For the prognosis of treatment it is important to timely and accurately evaluate the liver fibrosis in a chronic liver disease. The most reliable diagnosis of liver biopsy available has many limitations, such as the inhomogeneity of liver damage would result in sampling errors and some damage will be done to patients. It is difficult to dynamically observe the process of hepatic fibrosis and fibrosis formation. There is also no reliable way to determine the content of collagen in liver. The diagnose of liver fibrosis based only on a rough estimate has a limited applications. In recent years, a number of combined indicators of liver fibrosis were applied to diagnosis and evaluation. Comprehensive index models for hepatic fibrosis non-invasive diagnosis attract much attention. In order to better understand and apply these diagnostic models, this paper reviews the major progress of non-invasive diagnosis, especially, from two aspects: the serological diagnosis and imaging diagnosis. The limitations and development directions of the current non-invasive diagnosis are discussed. Some food for thought is given for the development of new better comprehensive index models.
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