Assessment of liver fibrosis in chronic hepatitis B via multimodal data

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.