Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI.

Rencheng Zheng,Weibo Chen,Tian Qiu,Chunzi Shi,Nannan Shi,He Wang,Chengyan Wang,Yuxin Shi

doi:10.3390/biom11020307

Abstract

Accurate grading of liver fibrosis can effectively assess the severity of liver disease and help doctors make an appropriate diagnosis. This study aimed to perform the automatic staging of hepatic fibrosis on patients with hepatitis B, who underwent gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging with dynamic radiomics analysis. The proposed dynamic radiomics model combined imaging features from multi-phase dynamic contrast-enhanced (DCE) images and time-domain information. Imaging features were extracted from the deep learning-based segmented liver volume, and time-domain features were further explored to analyze the variation in features during contrast enhancement. Model construction and evaluation were based on a 132-case data set. The proposed model achieved remarkable performance in significant fibrosis (fibrosis stage S1 vs. S2–S4; accuracy (ACC) = 0.875, area under the curve (AUC) = 0.867), advanced fibrosis (S1–S2 vs. S3–S4; ACC = 0.825, AUC = 0.874), and cirrhosis (S1–S3 vs. S4; ACC = 0.850, AUC = 0.900) classifications in the test set. It was more dominant compared with the conventional single-phase or multi-phase DCE-based radiomics models, normalized liver enhancement, and some serological indicators. Time-domain features were found to play an important role in the classification models. The dynamic radiomics model can be applied for highly accurate automatic hepatic fibrosis staging.

Highlights

Hepatic fibrosis is a common pathological process in a variety of chronic liver diseases.It reflects the response to liver damage due to various causes
Park et al [18] assessed a large data set including patients with 436 pathologically proven liver fibrosis and performed a radiomics analysis in the hepatobiliary phase (HBP); the radiomics model significantly outperformed for clinical parameters commonly used for liver fibrosis assessment
This study developed a new automatic regions of interest (ROI) extraction approach based on 3D liver segmentation and post-processing, which was different from previous manual ROI drawing modes

Summary

Introduction

Hepatic fibrosis is a common pathological process in a variety of chronic liver diseases. It reflects the response to liver damage due to various causes. During hepatic stellate cell proliferation, large amounts of extracellular matrix components are deposited in the extravascular space to cause hepatic fibrosis [1]. Evidence indicates that treatment is necessary for patients with hepatic fibrosis Scheuer–Ludwig ≥S2 [2]. Liver fibrosis can be reversed with antiviral and antifibrotic treatment, even in early cirrhosis [3,4]. Accurate diagnosis of different grades of liver fibrosis is the prerequisite for evaluating the liver disease status in patients and providing effective and reasonable treatment

Objectives

Methods

Results

Discussion

Conclusion