An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension

doi:10.1016/j.xcrm.2022.100563

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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, Qian 13; Huang, Yifei 12; Li, Xiaoguo 12; Pavlides, Michael 11; Liu, Dengxiang 10; Luo, Hongwu 9; Ding, Huiguo 8; An, Weimin 1; Liu, Fuquan 6; Zuo, Changzeng 10
刊名	CELL REPORTS MEDICINE
	2022-03-15
卷号	3 期号:3 页码:13
关键词	venous pressure gradient (HVPG)
ISSN号	2666-3791
DOI	10.1016/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.
资助项目	National Natural Science Foundation of China (NSFC)[81830053] ; National Natural Science Foundation of China (NSFC)[61821002]
WOS关键词	ESOPHAGEAL-VARICES ; SPLEEN STIFFNESS ; ACCURATE MARKER ; FIBROSIS ; RISK ; DIAGNOSIS ; INDEX ; SCORE
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)
内容类型	期刊论文
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.Fifth Med Ctr PLA Gen Hosp, Dept Radiol, Beijing, Peoples R China 2.Fudan Univ, Human Phenome Inst, Shanghai, Peoples R China 3.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China 4.Beijing Key Lab Mol Imaging, Beijing, Peoples R China 5.Ankara Univ, Sch Med, Dept Gastroenterol, Ankara, Turkey 6.Capital Med Univ, Beijing Shijitan Hosp, Dept Intervent Therapy, Beijing, Peoples R China 7.Shanxi Med Univ, Hosp 3, Shanxi Bethune Hosp, Dept Radiol, Taiyuan, Shanxi, Peoples R China 8.Capital Med Univ, Beijing Youan Hosp, Dept Gastroenterol & Hepatol, Beijing, Peoples R China 9.Cent South Univ, Xiangya Hosp 3, Dept Gen Surg, Changsha, Peoples R China 10.Xingtai Peoples Hosp, CHESS Working Party, Xingtai, Peoples R China
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.