Published online Jun 7, 2021. doi: 10.3748/wjg.v27.i21.2910
Peer-review started: February 13, 2021
First decision: March 14, 2021
Revised: April 1, 2021
Accepted: April 20, 2021
Article in press: April 20, 2021
Published online: June 7, 2021
The burden of continuous inflammatory injury of hepatocytes is the main risk factor for the development of liver fibrosis, cirrhosis, and even hepatocellular carcinoma. Thus, it is essential to accurately evaluate the degree of hepatic inflammation and effectively reverse disease progression in chronic hepatitis B (CHB) patients.
Recent studies have identified that serum quantitative hepatitis B core antibody (qAnti-HBc) levels have potential clinical value in assessing the degree of hepatitis B-related hepatic inflammation in CHB patients. However, the optimal diagnostic efficacy may not be obtained by using qAnti-HBc alone, and its combination with other biomarkers potentially offers great clinical application value.
The objective of this study was to build an effective and robust noninvasive model for predicting hepatitis B-related hepatic inflammation.
Serum qAnti-HBc levels and 21 immune-related inflammatory factors were measured quantitatively in 650 treatment-naïve CHB patients who underwent liver biopsy. A backward feature elimination (BFE) algorithm utilizing Random Forest (RF) was used to select optional features and construct a combined model. The diagnostic abilities of the model or variables were evaluated based on estimated area under the receiver operating characteristics curve (AUROC) and compared using the DeLong test.
Four features (qAnti-HBc, ALT, AST, and CXCL11) were selected and incorporated into the model to establish a novel I-3A index. The AUROC of the I-3A index to predict moderate-to-severe liver inflammation was significantly increased compared with qAnti-HBc alone in all CHB patients. The I-3A index showed significantly improved diagnostic performance for predicting moderate-to-severe inflammation in HBeAg-positive and HBeAg-negative CHB patients.
The selected features of the I-3A index constructed using the RF-BFE algorithm can effectively predict moderate-to-severe liver inflammation in CHB patients.
The novel I-3A index is a promising non-invasive tool to predict liver inflammation. A longitudinal study is needed to verify our results and more emerging strategies such as radiomics are needed to further achieve increased diagnostic efficiency.