Comparing radiomics models with different inputs for accurate diagnosis of significant fibrosis in chronic liver disease

doi:10.1007/s00330-021-07934-6

	Comparing radiomics models with different inputs for accurate diagnosis of significant fibrosis in chronic liver disease
	Lu, Xue 1; Zhou, Hui2,3 ; Wang, Kun2,3 ; Jin, Jieyang 1,4; Meng, Fankun 5; Mu, Xiaojie 5; Li, Shuoyang 6; Zheng, Rongqin 1; Tian, Jie 2,3,7
发表期刊	EUROPEAN RADIOLOGY
ISSN	0938-7994
	2021-04-21
页码	12
通讯作者	Zheng, Rongqin(zhengrq@mail.sysu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
摘要	Objective 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.
关键词	Liver disease Hepatic cirrhosis Elasticity imaging techniques Deep learning
DOI	10.1007/s00330-021-07934-6
关键词[WOS]	HEPATITIS ; ELASTOGRAPHY ; GUIDELINES
收录类别	SCI
语种	英语
资助项目	National 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]
项目资助者	National Natural Science Foundation of China ; Chinese Academy of Sciences
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000642056700007
出版者	SPRINGER
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44480
专题	中国科学院分子影像重点实验室
通讯作者	Zheng, Rongqin; Tian, Jie
作者单位	1.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
通讯作者单位	中国科学院自动化研究所
推荐引用方式 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.