Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B
Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B
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DOI：
10.1016/j.compbiomed.2017.07.012
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发表时间：
2017-10-01
影响因子：
7.7
通讯作者：
Yan, Hongmei
中科院分区：
文献类型：
--
作者：
Chen, Yang;Luo, Yan;Yan, Hongmei
肝纤维化是慢性肝病病理过程中常见的中期阶段。在肝纤维化的早期阶段进行临床干预，可以减缓肝硬变的发展，降低发生肝癌的风险。肝活检是病毒性肝病治疗的黄金标准，但存在侵入性和相对较高的抽样错误率等缺点。实时组织弹性成像(RTE)是最新发展的技术之一，由于它既是无创的，又能提供准确的肝纤维化评估，因此可能是一种很有前途的成像技术。然而，从RTE图像中判断肝纤维化的分期在临床上是一项具有挑战性的任务。在本研究中，针对以往的肝纤维化指数(LFI)方法通过RTE图像和多元回归分析来预测诊断阶段的方法，我们采用了四种经典的分类器(即支持向量机、朴素贝叶斯、随机森林和K最近邻)来构建决策支持系统，以提高对乙肝的分期诊断性能。在这项多中心合作研究中，从513名接受肝活检的受试者中获得了11个RTE图像特征。实验结果表明，所采用的分类器的性能明显优于LFI方法，并且在四种机器算法中，随机森林(RF)分类器的平均准确率最高。这一结果表明，复杂的机器学习方法可以成为评估肝纤维化分期的有力工具，并显示出临床应用的前景。
Hepatic fibrosis is a common middle stage of the pathological processes of chronic liver diseases. Clinical intervention during the early stages of hepatic fibrosis can slow the development of liver cirrhosis and reduce the risk of developing liver cancer. Performing a liver biopsy, the gold standard for viral liver disease management, has drawbacks such as invasiveness and a relatively high sampling error rate. Real-time tissue elastography (RTE), one of the most recently developed technologies, might be promising imaging technology because it is both noninvasive and provides accurate assessments of hepatic fibrosis. However, determining the stage of liver fibrosis from RTE images in a clinic is a challenging task. In this study, in contrast to the previous liver fibrosis index (LFI) method, which predicts the stage of diagnosis using RTE images and multiple regression analysis, we employed four classical classifiers (i.e., Support Vector Machine, Naive Bayes, Random Forest and K-Nearest Neighbor) to build a decision-support system to improve the hepatitis B stage diagnosis performance. Eleven RTE image features were obtained from 513 subjects who underwent liver biopsies in this multicenter collaborative research. The experimental results showed that the adopted classifiers significantly outperformed the LFI method and that the Random Forest(RF) classifier provided the highest average accuracy among the four machine algorithms. This result suggests that sophisticated machine-learning methods can be powerful tools for evaluating the stage of hepatic fibrosis and show promise for clinical applications.