中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Deep Convolutional Neural Network-Aided Detection of Portal Hypertension in Patients With Cirrhosis

文献类型:期刊论文

作者Liu, Yanna25; Ning, Zhenyuan24; Ormeci, Necati23; An, Weimin21; Yu, Qian20; Han, Kangfu24; Huang, Yifei25; Liu, Dengxiang18; Liu, Fuquan17; Li, Zhiwei9
刊名CLINICAL GASTROENTEROLOGY AND HEPATOLOGY
出版日期2020-12-01
卷号18期号:13页码:2998-+
ISSN号1542-3565
关键词HVPG Diagnostic Deep Learning AI
DOI10.1016/j.cgh.2020.03.034
通讯作者Ju, Shenghong(jsh0836@126.com) ; Zhang, Yu(yuzhang@smu.edu.cn) ; Qi, Xiaolong(qixiaolong@vip.163.com)
英文摘要BACKGROUND & AIMS: Noninvasive and accurate methods are needed to identify patients with clinically significant portal hypertension (CSPH). We investigated the ability of deep convolutional neural network (CNN) analysis of computed tomography (CT) or magnetic resonance (MR) to identify patients with CSPH. METHODS: We collected liver and spleen images from patients who underwent contrast-enhanced CT or MR analysis within 14 days of transjugular catheterization for hepatic venous pressure gradient measurement. The CT cohort comprised participants with cirrhosis in the CHESS1701 study, performed at 4 university hospitals in China from August 2016 through September 2017. The MR cohort comprised participants with cirrhosis in the CHESS1802 study, performed at 8 university hospitals in China and 1 in Turkey from December 2018 through April 2019. Patients with CSPH were identified as those with a hepatic venous pressure gradient of 10 mm Hg or higher. In total, we analyzed 10,014 liver images and 899 spleen images collected from 679 participants who underwent CT analysis, and 45,554 liver and spleen images from 271 par-ticipants who underwent MR analysis. For each cohort, participants were shuffled and then sampled randomly and equiprobably for 6 times into training, validation, and test data sets (ratio, 3:1:1). Therefore, a total of 6 deep CNN models for each cohort were developed for identification of CSPH. RESULTS: The CT-based CNN analysis identified patients with CSPH with an area under the receiver operating characteristic curve (AUC) value of 0.998 in the training set (95% CI, 0.996-1.000), an AUC of 0.912 in the validation set (95% CI, 0.854-0.971), and an AUC of 0.933 (95% CI, 0.883-0.984) in the test data sets. The MR-based CNN analysis identified patients with CSPH with an AUC of 1.000 in the training set (95% CI, 0.999-1.000), an AUC of 0.924 in the validation set (95% CI, 0.833-1.000), and an AUC of 0.940 in the test data set (95% CI, 0.880-0.999). When the model development procedures were repeated 6 times, AUC values for all CNN analyses were 0.888 or greater, with no significant differences between rounds (P > .05). CONCLUSIONS: We developed a deep CNN to analyze CT or MR images of liver and spleen from patients with cirrhosis that identifies patients with CSPH with an AUC value of 0.9. This provides a noninvasive and rapid method for detection of CSPH
WOS关键词ACCURATE MARKER ; FIBROSIS ; ELASTOGRAPHY ; DIAGNOSIS ; VARICES ; INDEX ; SCORE ; SERUM ; RISK
资助项目National Natural Science Foundation for Distinguished Young Scholars of China[81525014] ; National Natural Science Foundation of China[81600510] ; National Natural Science Foundation of China[81830053] ; Guangdong Science Fund for Distinguished Young Scholars[2018B030306019] ; Guangzhou Industry-Academia-Research Collaborative Innovation Major Project[201704020015]
WOS研究方向Gastroenterology & Hepatology
语种英语
出版者ELSEVIER SCIENCE INC
WOS记录号WOS:000589825300028
资助机构National Natural Science Foundation for Distinguished Young Scholars of China ; National Natural Science Foundation of China ; Guangdong Science Fund for Distinguished Young Scholars ; Guangzhou Industry-Academia-Research Collaborative Innovation Major Project
源URL[http://ir.ia.ac.cn/handle/173211/41786]  
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; Zhang, Yu; Qi, Xiaolong
作者单位1.Shandong Univ, Shandong Prov Hosp, Dept Gastroenterol, Jinan, Peoples R China
2.Zhejiang Univ, Lishui Hosp, Key Lab Imaging Diag & Minimally Invas Intervent, Lishui, Peoples R China
3.Zhengzhou Univ, Affiliated Hosp 1, Dept Vasc & Endovasc Surg, Zhengzhou, Peoples R China
4.Southern Med Univ, Shunde Hosp, Dept Hepatobiliary Surg, Foshan, Peoples R China
5.Shanghai Publ Hlth Clin Ctr, Dept Intervent Radiol, Shanghai, Peoples R China
6.Guangdong Gen Hosp, Guangdong Acad Med Sci, Dept Radiol, Guangzhou, Peoples R China
7.Lanzhou Univ, Sch Basic Med Sci, Evidence Based Med Ctr, Lanzhou, Peoples R China
8.Southern Med Univ, Nanfang Hosp, Dept Hepatol Unit, Guangzhou, Peoples R China
9.Third Peoples Hosp Shenzhen, Dept Hepatobiliary Surg, Shenzhen, Peoples R China
10.Sun Yat Sen Univ, Affiliated Hosp 1, Organ Transplant Ctr, Guangzhou, Peoples R China
推荐引用方式
GB/T 7714
Liu, Yanna,Ning, Zhenyuan,Ormeci, Necati,et al. Deep Convolutional Neural Network-Aided Detection of Portal Hypertension in Patients With Cirrhosis[J]. CLINICAL GASTROENTEROLOGY AND HEPATOLOGY,2020,18(13):2998-+.
APA Liu, Yanna.,Ning, Zhenyuan.,Ormeci, Necati.,An, Weimin.,Yu, Qian.,...&Qi, Xiaolong.(2020).Deep Convolutional Neural Network-Aided Detection of Portal Hypertension in Patients With Cirrhosis.CLINICAL GASTROENTEROLOGY AND HEPATOLOGY,18(13),2998-+.
MLA Liu, Yanna,et al."Deep Convolutional Neural Network-Aided Detection of Portal Hypertension in Patients With Cirrhosis".CLINICAL GASTROENTEROLOGY AND HEPATOLOGY 18.13(2020):2998-+.

入库方式: OAI收割

来源:自动化研究所

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