CASIA OpenIR  > 中国科学院分子影像重点实验室
An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension
Yu, Qian1; Huang, Yifei2; Li, Xiaoguo2; Pavlides, Michael3; Liu, Dengxiang4; Luo, Hongwu5; Ding, Huiguo6; An, Weimin7; Liu, Fuquan8; Zuo, Changzeng4; Lu, Chunqiang1; Tang, Tianyu1; Wang, Yuancheng1; Huang, Shan1; Liu, Chuan2; Zheng, Tianlei2; Kang, Ning2; Liu, Changchun7; Wang, Jitao4; Akcalar, Seray9; Celebioglu, Emrecan9; Ustuner, Evren9; Bilgic, Sadik9; Fang, Qu10; Fu, Chi-Cheng10; Zhang, Ruiping11; Wang, Chengyan12; Wei, Jingwei13,14; Tian, Jie13,14; Ormeci, Necati15; Ellik, Zeynep15; Asiller, Ozgun Omer15; Ju, Shenghong1; Qi, Xiaolong2
Source PublicationCELL REPORTS MEDICINE
ISSN2666-3791
2022-03-15
Volume3Issue:3Pages:13
Corresponding AuthorJu, Shenghong(jsh@seu.edu.cn) ; Qi, Xiaolong(qixiaolong@vip.163.com)
AbstractThe hepatic venous pressure gradient (HVPG) is the gold standard for cirrhotic portal hypertension (PHT), but it is invasive and specialized. Alternative non-invasive techniques are needed to assess the hepatic venous pressure gradient (HVPG). Here, we develop an auto-machine-learning CT radiomics HVPG quantitative model (aHVPG), and then we validate the model in internal and external test datasets by the area under the receiver operating characteristic curves (AUCs) for HVPG stages (>10, >12, >16, and >20 mm Hg) and compare the model with imaging-and serum-based tools. The final aHVPG model achieves AUCs over 0.80 and outperforms other non-invasive tools for assessing HVPG. The model shows performance improvement in identifying the severity of PHT, which may help non-invasive HVPG primary prophylaxis when transjugular HVPG measurements are not available.
Keywordvenous pressure gradient (HVPG)
DOI10.1016/j.xcrm.2022.100563
WOS KeywordESOPHAGEAL-VARICES ; SPLEEN STIFFNESS ; ACCURATE MARKER ; FIBROSIS ; RISK ; DIAGNOSIS ; INDEX ; SCORE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[81830053] ; National Natural Science Foundation of China (NSFC)[61821002]
Funding OrganizationNational Natural Science Foundation of China (NSFC)
WOS Research AreaCell Biology ; Research & Experimental Medicine
WOS SubjectCell Biology ; Medicine, Research & Experimental
WOS IDWOS:000787071300017
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48423
Collection中国科学院分子影像重点实验室
Corresponding AuthorJu, Shenghong; Qi, Xiaolong
Affiliation1.Southeast Univ, Zhongda Hosp, Sch Med, Dept Radiol, Nanjing, Peoples R China
2.Lanzhou Univ, Hosp 1, Inst Portal Hypertens, CHESS Ctr, Lanzhou, Peoples R China
3.Univ Oxford, John Radcliffe Hosp, Oxford Ctr Magnet Resonance Res, Radcliffe Dept Med, Oxford, England
4.Xingtai Peoples Hosp, CHESS Working Party, Xingtai, Peoples R China
5.Cent South Univ, Xiangya Hosp 3, Dept Gen Surg, Changsha, Peoples R China
6.Capital Med Univ, Beijing Youan Hosp, Dept Gastroenterol & Hepatol, Beijing, Peoples R China
7.Fifth Med Ctr PLA Gen Hosp, Dept Radiol, Beijing, Peoples R China
8.Capital Med Univ, Beijing Shijitan Hosp, Dept Intervent Therapy, Beijing, Peoples R China
9.Ankara Univ, Sch Med, Dept Radiol, Ankara, Turkey
10.Shanghai Aitrox Technol Corp, Shanghai, Peoples R China
11.Shanxi Med Univ, Hosp 3, Shanxi Bethune Hosp, Dept Radiol, Taiyuan, Shanxi, Peoples R China
12.Fudan Univ, Human Phenome Inst, Shanghai, Peoples R China
13.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China
14.Beijing Key Lab Mol Imaging, Beijing, Peoples R China
15.Ankara Univ, Sch Med, Dept Gastroenterol, Ankara, Turkey
Recommended Citation
GB/T 7714
Yu, Qian,Huang, Yifei,Li, Xiaoguo,et al. An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension[J]. CELL REPORTS MEDICINE,2022,3(3):13.
APA Yu, Qian.,Huang, Yifei.,Li, Xiaoguo.,Pavlides, Michael.,Liu, Dengxiang.,...&Qi, Xiaolong.(2022).An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension.CELL REPORTS MEDICINE,3(3),13.
MLA Yu, Qian,et al."An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension".CELL REPORTS MEDICINE 3.3(2022):13.
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