CASIA OpenIR  > 中国科学院分子影像重点实验室
Comparing radiomics models with different inputs for accurate diagnosis of significant fibrosis in chronic liver disease
Lu, Xue1; Zhou, Hui2,3; Wang, Kun2,3; Jin, Jieyang1,4; Meng, Fankun5; Mu, Xiaojie5; Li, Shuoyang6; Zheng, Rongqin1; Tian, Jie2,3,7
Source PublicationEUROPEAN RADIOLOGY
ISSN0938-7994
2021-04-21
Pages12
Corresponding AuthorZheng, Rongqin(zhengrq@mail.sysu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractObjective The non-invasive discrimination of significant fibrosis (>= F2) in patients with chronic liver disease (CLD) is clinically critical but technically challenging. We aimed to develop an updated deep learning radiomics model of elastography (DLRE2.0) based on our previous DLRE model to achieve significantly improved performance in >= F2 evaluation. Methods This was a retrospective multicenter study with 807 CLD patients and 4842 images from three hospitals. All of these patients have liver biopsy results as referenced standard. Multichannel deep learning radiomics models were developed. Elastography images, gray-scale images of the liver capsule, gray-scale images of the liver parenchyma, and serological results were gradually integrated to establish different diagnosis models, and the optimal model was selected for assessing >= F2. Its accuracy was thoroughly investigated by applying different F0-1 prevalence cohorts and independent external test cohorts. Analysis of receiver operating characteristic (ROC) curves was performed to calculate the area under the ROC curve (AUC) for significance of fibrosis (>= F2) and cirrhosis (F4). Results The AUC of the DLRE2.0 model significantly increased to 0.91 compared with the DLRE model (AUC 0.83) when evaluating >= F2 (p = 0.0167). However, it did not show statistically significant differences as integrating gray-scale images and serological data into the DLRE2.0 model. AUCs of DLRE and DLRE2.0 increased, when there was higher F0-1 prevalence. All radiomics models had good robustness in the independent external test cohort. Conclusions DLRE2.0 was the most suitable model for staging significant fibrosis while considering the balance of diagnostic accuracy and clinical practicability.
KeywordLiver disease Hepatic cirrhosis Elasticity imaging techniques Deep learning
DOI10.1007/s00330-021-07934-6
WOS KeywordHEPATITIS ; ELASTOGRAPHY ; GUIDELINES
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[81827802] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61231004] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[61401462] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Chinese Academy of Sciences[XDB32030200]
Funding OrganizationNational Natural Science Foundation of China ; Chinese Academy of Sciences
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000642056700007
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44480
Collection中国科学院分子影像重点实验室
Corresponding AuthorZheng, Rongqin; Tian, Jie
Affiliation1.Sun Yat Sen Univ, Affiliated Hosp 3, Guangdong Key Lab Liver Dis Res, Dept Ultrasound, 600 Tianhe Rd, Guangzhou 510630, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, 19 A Yuquan Rd, Beijing 100049, Peoples R China
4.Sun Yat Sen Univ, Affiliated Hosp 3, Lingnan Hosp, Dept Ultrasound, 2693 Kaichuang Rd, Guangzhou 510700, Peoples R China
5.Capital Med Univ, Beijing Youan Hosp, Funct Diag Ctr, Beijing 100069, Peoples R China
6.Univ Wollongong, Fac Engn & Informat Sci EIS, Wollongong, NSW, Australia
7.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, 95 Zhongguancun East Rd, Beijing, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Lu, Xue,Zhou, Hui,Wang, Kun,et al. Comparing radiomics models with different inputs for accurate diagnosis of significant fibrosis in chronic liver disease[J]. EUROPEAN RADIOLOGY,2021:12.
APA Lu, Xue.,Zhou, Hui.,Wang, Kun.,Jin, Jieyang.,Meng, Fankun.,...&Tian, Jie.(2021).Comparing radiomics models with different inputs for accurate diagnosis of significant fibrosis in chronic liver disease.EUROPEAN RADIOLOGY,12.
MLA Lu, Xue,et al."Comparing radiomics models with different inputs for accurate diagnosis of significant fibrosis in chronic liver disease".EUROPEAN RADIOLOGY (2021):12.
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