CASIA OpenIR  > 中国科学院分子影像重点实验室
Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study
Wang, Kun1,2; Lu, Xue1; Zhou, Hui2,3; Gao, Yongyan4; Zheng, Jian1,5; Tong, Minghui6; Wu, Changjun7; Liu, Changzhu8; Huang, Liping9; Jiang, Tian'an10; Meng, Fankun11; Lu, Yongping12; Ai, Hong13; Xie, Xiao-Yan14; Yin, Li-Ping15; Liang, Ping3; Tian, Jie2,3; Zheng, Rongqin1
Source PublicationGUT
ISSN0017-5749
2019-04-01
Volume68Issue:4Pages:729-741
Corresponding AuthorLiang, Ping(liangping301@hotmail.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Zheng, Rongqin(zhengrq@mail.sysu.edu.cn)
AbstractObjective 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. Design A prospective multicentre study was conducted to assess its accuracy in patients with chronic hepatitis B, in comparison with 2D-SWE, aspartate transaminase-to-platelet ratio index and fibrosis index based on four factors, by using liver biopsy as the reference standard. Its accuracy and robustness were also investigated by applying different number of acquisitions and different training cohorts, respectively. Data of 654 potentially eligible patients were prospectively enrolled from 12 hospitals, and finally 398 patients with 1990 images were included. Analysis of receiver operating characteristic (ROC) curves was performed to calculate the optimal area under the ROC curve (AUC) for cirrhosis (F4), advanced fibrosis (>= F3) and significance fibrosis (>= F2). Results AUCs of DLRE were 0.97 for F4 (95% CI 0.94 to 0.99), 0.98 for >= F3 (95% CI 0.96 to 1.00) and 0.85 (95% CI 0.81 to 0.89) for >= F2, which were significantly better than other methods except 2D-SWE in >= F2. Its diagnostic accuracy improved as more images (especially >= 3 images) were acquired from each individual. No significant variation of the performance was found if different training cohorts were applied. Conclusion DLRE shows the best overall performance in predicting liver fibrosis stages compared with 2D-SWE and biomarkers. It is valuable and practical for the non-invasive accurate diagnosis of liver fibrosis stages in HBV-infected patients.
DOI10.1136/gutjnl-2018-316204
WOS KeywordCONVOLUTIONAL NEURAL-NETWORKS ; SIMPLE NONINVASIVE INDEX ; ACCURACY ; STIFFNESS ; CLASSIFICATION ; ALGORITHM ; PREDICT ; UPDATE
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFA0205200] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[61231004] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[61401462] ; Beijing Municipal Science and Technology Commission[Z161100002616022]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Municipal Science and Technology Commission
WOS Research AreaGastroenterology & Hepatology
WOS SubjectGastroenterology & Hepatology
WOS IDWOS:000471830300018
PublisherBMJ PUBLISHING GROUP
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Cited Times:18[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25829
Collection中国科学院分子影像重点实验室
Corresponding AuthorLiang, Ping; Tian, Jie; Zheng, Rongqin
Affiliation1.Sun Yat Sen Univ, Dept Med Ultrasound, Guangdong Key Lab Liver Dis Res, Affiliated Hosp 3, Guangzhou 510630, Guangdong, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Dept Artificial Intelligence Technol, Beijing, Peoples R China
4.Chinese Peoples Liberat Army Gen Hosp, Dept Intervent Ultrasound, Beijing 100853, Peoples R China
5.Third Hosp Longgang, Dept Med Ultrason, Shenzhen, Peoples R China
6.Lanzhou Univ, Funct Examinat Dept, Hosp 2, Childrens Hosp, Lanzhou, Gansu, Peoples R China
7.Harbin Med Univ, Dept Ultrasound, Affiliated Hosp 1, Harbin, Heilongjiang, Peoples R China
8.Guangzhou Eighth Peoples Hosp, Ultrasound Dept, Guangzhou, Guangdong, Peoples R China
9.China Med Univ, Dept Ultrasound, Shengjing Hosp, Shenyang, Liaoning, Peoples R China
10.Zhejiang Univ, Affiliated Hosp 1, Dept Ultrasonog, Med Coll, Hangzhou, Zhejiang, Peoples R China
11.Capital Med Univ, Beijing Youan Hosp, Funct Diag Ctr, Beijing, Peoples R China
12.Second Peoples Hosp Yunnan Prov, Ultrasound Dept, Kunming, Yunnan, Peoples R China
13.Xi An Jiao Tong Univ, Ultrasound Dept, Affiliated Hosp 1, Xian, Shaanxi, Peoples R China
14.Sun Yat sen Univ, Inst Diagnost & Intervent Ultrasound, Dept Med Ultrason, Affiliated Hosp 1, Guangzhou, Guangdong, Peoples R China
15.Nanjing Univ TCM, Jiangsu Prov Hosp TCM, Dept Ultrasound, Affiliated Hosp, Nanjing, Jiangsu, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Kun,Lu, Xue,Zhou, Hui,et al. Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study[J]. GUT,2019,68(4):729-741.
APA Wang, Kun.,Lu, Xue.,Zhou, Hui.,Gao, Yongyan.,Zheng, Jian.,...&Zheng, Rongqin.(2019).Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study.GUT,68(4),729-741.
MLA Wang, Kun,et al."Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study".GUT 68.4(2019):729-741.
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