Noninvasive Assessment of Liver Fibrosis and Inflammation in Chronic Hepatitis B:A Dual-task Convolutional Neural Network(DtCNN)Model Based on Ultrasound Shear Wave Elastography  

作　　者：Chengyan Wang Lili Zheng Yan Li Shujun Xia Jun Lv Xumei Hu Weiwei Zhan Fuhua Yan Ruokun Li Xinping Ren 

机构地区：[1]Human Phenome Institute,Fudan University,Shanghai,China [2]Ultrasound Department,Ruijin Hospital Wuxi Branch,Shanghai Jiao Tong University School of Medicine,Wuxi,Jiangsu,China [3]Department of Radiology,Ruijin Hospital,Shang-hai Jiao Tong University School of Medicine,Shanghai,China [4]Ultrasound Department,Ruijin Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai,China [5]School of Computer and Control Engineering,Yantai University,Yantai,Shandong,China

出　　处：《Journal of Clinical and Translational Hepatology》2022年第6期1077-1085,共9页临床与转化肝病杂志（英文版）

基　　金：the National Natural Science Foundation of China(No.62001120);the Shanghai Sail-ing Program(No.20YF1402400).

摘　　要：Background and Aims:Liver stiffness(LS)measured by shear wave elastography(SWE)is often influenced by hepat-ic inflammation.The aim was to develop a dual-task convo-lutional neural network(DtCNN)model for the simultaneous staging of liver fibrosis and inflammation activity using 2D-SWE.Methods:A total of 532 patients with chronic hepatitis B(CHB)were included to develop and validate the DtCNN model.An additional 180 consecutive patients between De-cember 2019 and April 2021 were prospectively included for further validation.All patients underwent 2D-SWE examina-tion and serum biomarker assessment.A DtCNN model con-taining two pathways for the staging of fibrosis and inflam-mation was used to improve the classification of significant fibrosis(≥F2),advanced fibrosis(≥F3)as well as cirrhosis(F4).Results:Both fibrosis and inflammation affected LS measurements by 2D-SWE.The proposed DtCNN performed the best among all the classification models for fibrosis stage[significant fibrosis AUC=0.89(95%CI:0.87-0.92),ad-vanced fibrosis AUC=0.87(95%CI:0.84-0.90),liver cirrho-sis AUC=0.85(95%CI:0.81-0.89)].The DtCNN-based pre-diction of inflammation activity achieved AUCs of 0.82(95%CI:0.78-0.86)for grade≥A1,0.88(95%CI:0.85-0.90)grade≥A2 and 0.78(95%CI:0.75-0.81)for grade≥A3,which were significantly higher than the AUCs of the single-task groups.Similar findings were observed in the prospec-tive study.Conclusions:The proposed DtCNN improved di-agnostic performance compared with existing fibrosis staging models by including inflammation in the model,which sup-ports its potential clinical application.

关 键 词：FIBROSIS INFLAMMATION Shear wave elastography Chronic hepati-tis B Dual-task convolutional neural network. 

分 类 号：R512.62[医药卫生—内科学]