Zheng Rencheng, Shi Chunzi, Wang Chengyan, Shi Nannan, Qiu Tian, Chen Weibo, Shi Yuxin, Wang He
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.
Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China.
Biomolecules. 2021 Feb 18;11(2):307. doi: 10.3390/biom11020307.
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.
准确的肝纤维化分级能够有效评估肝脏疾病的严重程度，并有助于医生做出恰当的诊断。本研究旨在对接受钆塞酸二钠（Gd-EOB-DTPA）增强磁共振成像并进行动态影像组学分析的乙型肝炎患者进行肝纤维化自动分期。所提出的动态影像组学模型结合了多期动态对比增强（DCE）图像的影像特征和时域信息。影像特征从基于深度学习分割的肝脏体积中提取，并进一步探索时域特征以分析对比增强过程中特征的变化。模型构建和评估基于一个132例的数据集。所提出的模型在测试集中对显著纤维化（纤维化分期S1与S2-S4；准确率（ACC）=0.875，曲线下面积（AUC）=0.867）、进展性纤维化（S1-S2与S3-S4；ACC =0.825，AUC =0.874）和肝硬化（S1-S3与S4；ACC =0.850，AUC =0.900）的分类中表现出色。与传统的基于单相或多相DCE的影像组学模型、肝脏增强标准化以及一些血清学指标相比，它更具优势。发现时域特征在分类模型中发挥着重要作用。动态影像组学模型可用于高度准确的肝纤维化自动分期。
