Assessment of liver fibrosis in chronic hepatitis B via multimodal data
Assessment of liver fibrosis in chronic hepatitis B via multimodal data
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通过多模态数据评估慢性乙型肝炎肝纤维化
DOI：
10.1016/j.neucom.2016.09.128
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发表时间：
2017-08
期刊：
影响因子：
6
通讯作者：
Wen Zeng
中科院分区：
文献类型：
--
作者：
Baiying Lei;Yingxia Liu;Changfeng Dong;Xin Chen;Xinyu Zhang;Xianfen Diao;Guilin Yang;Jing Liu;Simin Yao;Hanying Li;Jing Yuan;Shaxi Li;Xiaohua Le;Yimin Lin;Wen Zeng
评估慢性乙型肝炎（CHB）患者的肝纤维化非常重要。评估肝纤维化的一些非侵入性方法包括血液检查和超声弹性成像。如何有效地结合多种方法来提高诊断性能仍然是一个具有挑战性的问题。本文的主要目标是使用基于多模态数据的特征选择和机器学习方法来评估慢性乙型肝炎的肝纤维化并对其进行分期。探索了流行的机器学习方法（例如支持向量机（SVM））和特征选择（FS）来分阶段 CHB。根据瞬时弹性成像 (TE) 和声辐射力脉冲成像 (ARFI) 数据，对 16 名志愿者和 92 名慢性乙型肝炎患者进行了肝纤维化分期研究。使用 FS 和 SVM 分类器的分期结果的准确性分别为：显着纤维化 (≥F2) 的准确度为 90.68%，肝硬化 (F4) 的准确度为 93.52%。该方法还提高了显着纤维化和肝硬化诊断的敏感性、特异性和曲线下面积（AUC）值，这对于根据多模态信息对肝纤维化进行分期非常有希望。它优于任何单一方法及其线性组合，并且还实现了最先进的性能。
Assessing liver fibrosis with chronic hepatitis B (CHB) in patients is quite important. Some non-invasive approaches for evaluating liver fibrosis include blood tests and ultrasound elastography. How to effectively combine multiple methods to improve the diagnostic performance remains a challenging problem. The main goal of this paper is to assess and stage liver fibrosis in CHB using feature selection and machine learning methods based on multimodal data. Popular machine learning approaches (e.g., support vector machine (SVM)) and feature selection (FS) were explored to stage the CHB. 16 volunteers and 92 patients with CHB were investigated for liver fibrosis staging based on transient elastography (TE) and acoustical radiation force impulse imaging (ARFI) data. The accuracy of the staging result using FS and a SVM classifier was an accuracy of 90.68% for significant fibrosis (≥F2) and an accuracy of 93.52% for cirrhosis (F4), respectively. The proposed method also increased the sensitivity, specificity, and area under curve (AUC) values for both significant fibrosis and cirrhosis diagnosis, which is very promising for staging liver fibrosis from multimodal information. It outperforms any single method and their linear combination and also achieves a state-of-the-art performance.
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