CASIA OpenIR  > 中国科学院分子影像重点实验室
Deep Convolutional Neural Network-Aided Detection of Portal Hypertension in Patients With Cirrhosis
Liu, Yanna1; Ning, Zhenyuan2; Ormeci, Necati3; An, Weimin5; Yu, Qian6; Han, Kangfu2; Huang, Yifei1; Liu, Dengxiang8; Liu, Fuquan9; Li, Zhiwei11; Ding, Huiguo10; Luo, Hongwu12; Zuo, Changzeng8; Liu, Changchun5; Wang, Jitao8; Zhang, Chunqing13; Ji, Jiansong14; Wang, Wenhui1; Wang, Zhiwei15; Wang, Weidong16; Yuan, Min17; Li, Lei1; Zhao, Zhongwei14; Wang, Guangchuan13; Li, Mingxing15; Liu, Qingbo16; Lei, Junqiang1; Liu, Chuan1; Tang, Tianyu6; Akcalar, Seray4; Celebioglu, Emrecan4; Ustuner, Evren4; Bilgic, Sadik4; Ellik, Zeynep3; Asiller, Ozgun Omer3; Liu, Zaiyi18; Teng, Gaojun7; Chen, Yaolong19; Hou, Jinlin20,21; Li, Xun1; He, Xiaoshun22; Dong, Jiahong23; Tian, Jie24; Liang, Ping25; Ju, Shenghong6; Zhang, Yu2; Qi, Xiaolong1
Source PublicationCLINICAL GASTROENTEROLOGY AND HEPATOLOGY
ISSN1542-3565
2020-12-01
Volume18Issue:13Pages:2998-+
Corresponding AuthorJu, Shenghong(jsh0836@126.com) ; Zhang, Yu(yuzhang@smu.edu.cn) ; Qi, Xiaolong(qixiaolong@vip.163.com)
AbstractBACKGROUND & 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
KeywordHVPG Diagnostic Deep Learning AI
DOI10.1016/j.cgh.2020.03.034
WOS KeywordACCURATE MARKER ; FIBROSIS ; ELASTOGRAPHY ; DIAGNOSIS ; VARICES ; INDEX ; SCORE ; SERUM ; RISK
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaGastroenterology & Hepatology
WOS SubjectGastroenterology & Hepatology
WOS IDWOS:000589825300028
PublisherELSEVIER SCIENCE INC
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/41787
Collection中国科学院分子影像重点实验室
Corresponding AuthorJu, Shenghong; Zhang, Yu; Qi, Xiaolong
Affiliation1.Lanzhou Univ, Inst Portal Hypertens, Chinese Portal Hypertens Diag & Monitoring Study, Hosp 1, Lanzhou, Peoples R China
2.Southern Med Univ, Sch Biomed Engn, Guangzhou, Peoples R China
3.Ankara Univ, Sch Med, Dept Gastroenterol, Ankara, Turkey
4.Ankara Univ, Sch Med, Dept Radiol, Ankara, Turkey
5.Chinese Peoples Liberat Army Gen Hosp, Med Ctr 5, Dept Radiol, Beijing, Peoples R China
6.Southeast Univ, Med Sch, Zhongda Hosp, Dept Radiol, Nanjing, Peoples R China
7.Southeast Univ, Med Sch, Zhongda Hosp, Dept Intervent Radiol, Nanjing, Peoples R China
8.Xingtai Peoples Hosp, Chinese Portal Hypertens Diag & Monitoring Study, Xingtai, Peoples R China
9.Capital Med Univ, Dept Intervent Therapy, Beijing Shijitan Hosp, Beijing, Peoples R China
10.Capital Med Univ, Beijing Youan Hosp, Dept Gastroenterol & Hepatol, Beijing, Peoples R China
11.Third Peoples Hosp Shenzhen, Dept Hepatobiliary Surg, Shenzhen, Peoples R China
12.Cent South Univ, Dept Gen Surg, Xiangya Hosp 3, Changsha, Peoples R China
13.Shandong Univ, Shandong Prov Hosp, Dept Gastroenterol, Jinan, Peoples R China
14.Zhejiang Univ, Lishui Hosp, Key Lab Imaging Diag & Minimally Invas Intervent, Lishui, Peoples R China
15.Zhengzhou Univ, Affiliated Hosp 1, Dept Vasc & Endovasc Surg, Zhengzhou, Peoples R China
16.Southern Med Univ, Shunde Hosp, Dept Hepatobiliary Surg, Foshan, Peoples R China
17.Shanghai Publ Hlth Clin Ctr, Dept Intervent Radiol, Shanghai, Peoples R China
18.Guangdong Gen Hosp, Guangdong Acad Med Sci, Dept Radiol, Guangzhou, Peoples R China
19.Lanzhou Univ, Sch Basic Med Sci, Evidence Based Med Ctr, Lanzhou, Peoples R China
20.Southern Med Univ, Nanfang Hosp, Dept Hepatol Unit, Guangzhou, Peoples R China
21.Southern Med Univ, Nanfang Hosp, Infect Dis, Guangzhou, Peoples R China
22.Sun Yat Sen Univ, Affiliated Hosp 1, Organ Transplant Ctr, Guangzhou, Peoples R China
23.Tsinghua Univ, Beijing Tsinghua Changgung Hosp, Sch Clin Med, Dept Hepatopancreatobiliary Surg, Beijing, Peoples R China
24.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China
25.Chinese Peoples Liberat Army Gen Hosp, Dept Intervent Ultrasound, Beijing, Peoples R China
Recommended Citation
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-+.
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