Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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 | |
发表期刊 | CELL REPORTS MEDICINE |
ISSN | 2666-3791 |
2022-03-15 | |
卷号 | 3期号:3页码:13 |
通讯作者 | Ju, Shenghong(jsh@seu.edu.cn) ; Qi, Xiaolong(qixiaolong@vip.163.com) |
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. |
关键词 | venous pressure gradient (HVPG) |
DOI | 10.1016/j.xcrm.2022.100563 |
关键词[WOS] | ESOPHAGEAL-VARICES ; SPLEEN STIFFNESS ; ACCURATE MARKER ; FIBROSIS ; RISK ; DIAGNOSIS ; INDEX ; SCORE |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China (NSFC)[81830053] ; National Natural Science Foundation of China (NSFC)[61821002] |
项目资助者 | National Natural Science Foundation of China (NSFC) |
WOS研究方向 | Cell Biology ; Research & Experimental Medicine |
WOS类目 | Cell Biology ; Medicine, Research & Experimental |
WOS记录号 | WOS:000787071300017 |
出版者 | ELSEVIER |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48423 |
专题 | 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.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 |
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. |
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