Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study

• Published in: Gut Vol. 68; no. 4; pp. 729 - 741
• Main Authors: Wang, Kun, Lu, Xue, Zhou, Hui, Gao, Yongyan, Zheng, Jian, Tong, Minghui, Wu, Changjun, Liu, Changzhu, Huang, Liping, Jiang, Tian’an, Meng, Fankun, Lu, Yongping, Ai, Hong, Xie, Xiao-Yan, Yin, Li-ping, Liang, Ping, Tian, Jie, Zheng, Rongqin
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group LTD 01.04.2019; BMJ Publishing Group
• Series: Original article
• Subjects: Accuracy; Adult; Aspartate transaminase; Automation; Bile; Biomarkers; Biopsy; China; Cirrhosis; Classification; Deep Learning; Diagnosis, Differential; Elasticity Imaging Techniques - methods; Female; Fibrosis; Hepatitis; Hepatitis B; Hepatitis B, Chronic - complications; Hepatology; Histology; Humans; Interferon; Laboratories; Liver; Liver cirrhosis; Liver Cirrhosis - diagnostic imaging; Liver Cirrhosis - pathology; Liver diseases; Male; Methods; Neural networks; Patients; Prospective Studies; Quantitative analysis; Radiomics; Studies; Surveillance; Transaminase; Ultrasonic imaging; China; Beijing China; hepatitis B; ultrasonography; cirrhosis

We aimed to evaluate the performance of the newly developed deep learning Radiomics of elastography (DLRE) for assessing liver fibrosis stages. DLRE adopts the radiomic strategy for quantitative analysis of the heterogeneity in two-dimensional shear wave elastography (2D-SWE) images. A prospective m...