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  4. 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

文献类型：期刊论文

作者Yu, Qian12; Huang, Yifei15; Li, Xiaoguo15; Pavlides, Michael13; Liu, Dengxiang14; Luo, Hongwu1; Ding, Huiguo3; An, Weimin8; Liu, Fuquan9; Zuo, Changzeng14
刊名CELL REPORTS MEDICINE
出版日期2022-03-15
卷号3期号:3页码:13
ISSN号2666-3791
关键词venous pressure gradient (HVPG)
DOI10.1016/j.xcrm.2022.100563
通讯作者Ju, Shenghong(jsh@seu.edu.cn) ; Qi, Xiaolong(qixiaolong@vip.163.com)
英文摘要The 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.
WOS关键词ESOPHAGEAL-VARICES ; SPLEEN STIFFNESS ; ACCURATE MARKER ; FIBROSIS ; RISK ; DIAGNOSIS ; INDEX ; SCORE
资助项目National Natural Science Foundation of China (NSFC)[81830053] ; National Natural Science Foundation of China (NSFC)[61821002]
WOS研究方向Cell Biology ; Research & Experimental Medicine
语种英语
出版者ELSEVIER
WOS记录号WOS:000787071300017
资助机构National Natural Science Foundation of China (NSFC)
源URL[http://ir.ia.ac.cn/handle/173211/48422]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Ju, Shenghong; Qi, Xiaolong
作者单位1.Cent South Univ, Xiangya Hosp 3, Dept Gen Surg, Changsha, Peoples R China
2.Shanxi Med Univ, Hosp 3, Shanxi Bethune Hosp, Dept Radiol, Taiyuan, Shanxi, Peoples R China
3.Capital Med Univ, Beijing Youan Hosp, Dept Gastroenterol & Hepatol, Beijing, Peoples R China
4.Fudan Univ, Human Phenome Inst, Shanghai, Peoples R China
5.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China
6.Beijing Key Lab Mol Imaging, Beijing, Peoples R China
7.Ankara Univ, Sch Med, Dept Gastroenterol, Ankara, Turkey
8.Fifth Med Ctr PLA Gen Hosp, Dept Radiol, Beijing, Peoples R China
9.Capital Med Univ, Beijing Shijitan Hosp, Dept Intervent Therapy, Beijing, Peoples R China
10.Ankara Univ, Sch Med, Dept Radiol, Ankara, Turkey
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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