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

Yanna Liu, Zhenyuan Ning, Necati Örmeci, Weimin An, Qian Yu, Kangfu Han, Yifei Huang, Dengxiang Liu, Fuquan Liu, Zhiwei Li, Huiguo Ding, Hongwu Luo, Changzeng Zuo, Changchun Liu, Jitao Wang, Chunqing Zhang, Jiansong Ji, Wenhui Wang, Zhiwei Wang, Weidong WangMin Yuan, Lei Li, Zhongwei Zhao, Guangchuan Wang, Mingxing Li, Qingbo Liu, Junqiang Lei, Chuan Liu, Tianyu Tang, Seray Akçalar, Emrecan Çelebioğlu, Evren Üstüner, Sadık Bilgiç, Zeynep Ellik, Özgün Ömer Asiller, Zaiyi Liu, Gaojun Teng, Yaolong Chen, Jinlin Hou, Xun Li, Xiaoshun He, Jiahong Dong, Jie Tian, Ping Liang, Shenghong Ju, Yu Zhang, Xiaolong Qi

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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 participants 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 (ClincialTrials.gov numbers: NCT03138915 and NCT03766880).

Original languageEnglish (US)
Pages (from-to)2998-3007.e5
JournalClinical Gastroenterology and Hepatology
Volume18
Issue number13
DOIs
StatePublished - Dec 2020

Keywords

  • AI
  • Deep Learning
  • Diagnostic
  • HVPG

ASJC Scopus subject areas

  • Hepatology
  • Gastroenterology

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