Deep Convolutional Neural Network-Aided Detection of Portal Hypertension in Patients With Cirrhosis

doi:10.1016/j.cgh.2020.03.034

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

	Deep Convolutional Neural Network-Aided Detection of Portal Hypertension in Patients With Cirrhosis
	Liu, Yanna 6; Ning, Zhenyuan 5; Ormeci, Necati 4; An, Weimin 2; Yu, Qian 10; Han, Kangfu 5; Huang, Yifei 6; Liu, Dengxiang 25; Liu, Fuquan 13; Li, Zhiwei 22
刊名	CLINICAL GASTROENTEROLOGY AND HEPATOLOGY
	2020-12-01
卷号	18 期号:13 页码:2998-+
关键词	HVPG Diagnostic Deep Learning AI
ISSN号	1542-3565
DOI	10.1016/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
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关键词	ACCURATE MARKER ; FIBROSIS ; ELASTOGRAPHY ; DIAGNOSIS ; VARICES ; INDEX ; SCORE ; SERUM ; RISK
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
内容类型	期刊论文
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.Southern Med Univ, Nanfang Hosp, Dept Hepatol Unit, Guangzhou, Peoples R China 2.Chinese Peoples Liberat Army Gen Hosp, Med Ctr 5, Dept Radiol, Beijing, Peoples R China 3.Ankara Univ, Sch Med, Dept Radiol, Ankara, Turkey 4.Ankara Univ, Sch Med, Dept Gastroenterol, Ankara, Turkey 5.Southern Med Univ, Sch Biomed Engn, Guangzhou, Peoples R China 6.Lanzhou Univ, Inst Portal Hypertens, Chinese Portal Hypertens Diag & Monitoring Study, Hosp 1, Lanzhou, Peoples R China 7.Cent South Univ, Dept Gen Surg, Xiangya Hosp 3, Changsha, Peoples R China 8.Shandong Univ, Shandong Prov Hosp, Dept Gastroenterol, Jinan, Peoples R China 9.Zhejiang Univ, Lishui Hosp, Key Lab Imaging Diag & Minimally Invas Intervent, Lishui, Peoples R China 10.Southeast Univ, Med Sch, Zhongda Hosp, Dept Radiol, Nanjing, 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-+.