Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610000, China; Department of Ultrasound, West China Hospital, Sichuan University, Chengdu 610000, China.
Department of Ultrasound, West China Hospital, Sichuan University, Chengdu 610000, China.
Comput Biol Med. 2017 Oct 1;89:18-23. doi: 10.1016/j.compbiomed.2017.07.012. Epub 2017 Jul 20.
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, Naïve 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.
肝纤维化是慢性肝病病理过程的常见中间阶段。在肝纤维化的早期阶段进行临床干预可以减缓肝硬化的发展并降低肝癌的风险。肝活检是病毒性肝病管理的金标准，但存在侵袭性和相对较高的采样误差率等缺点。实时组织弹性成像（RTE）是最近开发的技术之一，它是一种有前途的成像技术，因为它既非侵入性又能准确评估肝纤维化。然而，从 RTE 图像中确定肝纤维化的阶段在临床上是一项具有挑战性的任务。在这项研究中，我们采用了四种经典分类器（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，以提高乙型肝炎阶段诊断性能，与之前使用 RTE 图像和多元回归分析预测诊断阶段的肝纤维化指数（LFI）方法不同。从 513 名接受肝活检的患者中获得了 11 个 RTE 图像特征。实验结果表明，所采用的分类器明显优于 LFI 方法，而随机森林（RF）分类器在四种机器算法中提供了最高的平均准确性。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阶段的有力工具，并有望在临床应用中得到应用。
