Skip to main content
Top
Published in: Hepatology International 3/2022

21-03-2022 | Ultrasound | Original Article

Diagnosis of significant liver fibrosis in patients with chronic hepatitis B using a deep learning-based data integration network

Authors: Zhong Liu, Huiying Wen, Ziqi Zhu, Qinyuan Li, Li Liu, Tianjiao Li, Wencong Xu, Chao Hou, Bin Huang, Zhiyan Li, Changfeng Dong, Xin Chen

Published in: Hepatology International | Issue 3/2022

Login to get access

Abstract

Background and aims

Chronic hepatitis B virus (CHB) infection remains a major global health burden and the non-invasive and accurate diagnosis of significant liver fibrosis (≥ F2) in CHB patients is clinically very important. This study aimed to assess the potential of the joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients’ clinical parameters in a deep learning model to improve the diagnosis of ≥ F2 in CHB patients.

Methods

Of 527 CHB patients who underwent US examination, liver elastography and biopsy, 284 eligible patients were included. We developed a deep learning-based data integration network (DI-Net) to fuse the information of ultrasound images of liver parenchyma, liver stiffness values and patients’ clinical parameters for diagnosing ≥ F2 in CHB patients. The performance of DI-Net was cross-validated in a main cohort (n = 155) of the included patients and externally validated in an independent cohort (n = 129), with comparisons against single-source data-based models and other non-invasive methods in terms of the area under the receiver-operating-characteristic curve (AUC).

Results

DI-Net achieved an AUC of 0.943 (95% confidence interval [CI] 0.893–0.973) in the cross-validation, and an AUC of 0.901 (95% CI 0.834–0.945) in the external validation, which were significantly greater than those of the comparative methods (AUC ranges: 0.774–0.877 and 0.741–0.848 for cross- and external validations, respectively, ps < 0.01).

Conclusion

The joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients’ clinical parameters in a deep learning model could significantly improve the diagnosis of ≥ F2 in CHB patients.

Graphical abstract

Appendix
Available only for authorised users
Literature
1.
go back to reference Vittal A, Ghany MG. WHO guidelines for prevention, care and treatment of individuals infected with HBV a US perspective. Clin Liver Dis. 2019;23(3):417–432CrossRefPubMed Vittal A, Ghany MG. WHO guidelines for prevention, care and treatment of individuals infected with HBV a US perspective. Clin Liver Dis. 2019;23(3):417–432CrossRefPubMed
2.
go back to reference Lampertico P, Agarwal K, Berg T, et al. EASL 2017 clinical practice guidelines on the management of hepatitis B virus infection. J Hepatol. 2017;67(2):370–398CrossRef Lampertico P, Agarwal K, Berg T, et al. EASL 2017 clinical practice guidelines on the management of hepatitis B virus infection. J Hepatol. 2017;67(2):370–398CrossRef
3.
go back to reference Terrault NA, Lok ASF, McMahon BJ, et al. Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. Hepatology. 2018;67(4):1560–1599CrossRefPubMed Terrault NA, Lok ASF, McMahon BJ, et al. Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. Hepatology. 2018;67(4):1560–1599CrossRefPubMed
4.
go back to reference Bedossa P, Poynard T. An algorithm for the grading of activity in chronic hepatitis C. The METAVIR cooperative study group. Hepatology. 1996;24(2):289–293CrossRefPubMed Bedossa P, Poynard T. An algorithm for the grading of activity in chronic hepatitis C. The METAVIR cooperative study group. Hepatology. 1996;24(2):289–293CrossRefPubMed
5.
go back to reference Tan M, Bhadoria AS, Cui F, et al. Estimating the proportion of people with chronic hepatitis B virus infection eligible for hepatitis B antiviral treatment worldwide: a systematic review and meta-analysis. Lancet Gastroenterol. 2021;6(2):106–119 Tan M, Bhadoria AS, Cui F, et al. Estimating the proportion of people with chronic hepatitis B virus infection eligible for hepatitis B antiviral treatment worldwide: a systematic review and meta-analysis. Lancet Gastroenterol. 2021;6(2):106–119
6.
7.
go back to reference Colli A, Fraquelli M, Andreoletti M, et al. Severe liver fibrosis or cirrhosis: accuracy of US for detection—analysis of 300 cases. Radiology. 2003;227(1):89–94CrossRefPubMed Colli A, Fraquelli M, Andreoletti M, et al. Severe liver fibrosis or cirrhosis: accuracy of US for detection—analysis of 300 cases. Radiology. 2003;227(1):89–94CrossRefPubMed
8.
go back to reference Salvatore V, Borghi A, Peri E, et al. Relationship between hepatic haemodynamics assessed by Doppler ultrasound and liver stiffness. Dig Liver Dis. 2012;44(2):154–159CrossRefPubMed Salvatore V, Borghi A, Peri E, et al. Relationship between hepatic haemodynamics assessed by Doppler ultrasound and liver stiffness. Dig Liver Dis. 2012;44(2):154–159CrossRefPubMed
9.
go back to reference Wai CT, Greenson JK, Fontana RJ, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology. 2003;38(2):518–526CrossRefPubMed Wai CT, Greenson JK, Fontana RJ, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology. 2003;38(2):518–526CrossRefPubMed
10.
go back to reference Sterling RK, Lissen E, Clumeck N, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317–1325CrossRefPubMed Sterling RK, Lissen E, Clumeck N, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317–1325CrossRefPubMed
11.
go back to reference Siddiqui MS, Yamada G, Vuppalanchi R, et al. Diagnostic accuracy of noninvasive fibrosis models to detect change in fibrosis stage. Clin Gastroenterol Hepatol. 2019;17(9):1877–1885CrossRefPubMedPubMedCentral Siddiqui MS, Yamada G, Vuppalanchi R, et al. Diagnostic accuracy of noninvasive fibrosis models to detect change in fibrosis stage. Clin Gastroenterol Hepatol. 2019;17(9):1877–1885CrossRefPubMedPubMedCentral
12.
go back to reference Conti F, Serra C, Vukotic R, et al. Assessment of liver fibrosis with elastography point quantification vs other noninvasive methods. Clin Gastroenterol Hepatol. 2019;17(3):510–517CrossRefPubMed Conti F, Serra C, Vukotic R, et al. Assessment of liver fibrosis with elastography point quantification vs other noninvasive methods. Clin Gastroenterol Hepatol. 2019;17(3):510–517CrossRefPubMed
14.
go back to reference Wang K, Lu X, Zhou H, et al. Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut. 2019;68(4):729–741CrossRefPubMed Wang K, Lu X, Zhou H, et al. Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut. 2019;68(4):729–741CrossRefPubMed
15.
go back to reference Lee JH, Joo I, Kang TW, et al. Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network. Eur Radiol. 2020;30(2):1264–1273CrossRefPubMed Lee JH, Joo I, Kang TW, et al. Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network. Eur Radiol. 2020;30(2):1264–1273CrossRefPubMed
17.
go back to reference Petitclerc L, Sebastiani G, Gilbert G, et al. Liver fibrosis: review of current imaging and MRI quantification techniques. J Magn Reson Imaging. 2017;45(5):1276–1295CrossRefPubMed Petitclerc L, Sebastiani G, Gilbert G, et al. Liver fibrosis: review of current imaging and MRI quantification techniques. J Magn Reson Imaging. 2017;45(5):1276–1295CrossRefPubMed
18.
go back to reference Hui AY, Chan HL, Wong VW, et al. Identification of chronic hepatitis B patients without significant liver fibrosis by a simple noninvasive predictive model. Am J Gastroenterol. 2005;100(3):616–623CrossRefPubMed Hui AY, Chan HL, Wong VW, et al. Identification of chronic hepatitis B patients without significant liver fibrosis by a simple noninvasive predictive model. Am J Gastroenterol. 2005;100(3):616–623CrossRefPubMed
19.
go back to reference Zeng MD, Lu LG, Mao YM, et al. Prediction of significant fibrosis in HBeAg-positive patients with chronic hepatitis B by a noninvasive model. Hepatology. 2005;42(6):1437–1445CrossRefPubMed Zeng MD, Lu LG, Mao YM, et al. Prediction of significant fibrosis in HBeAg-positive patients with chronic hepatitis B by a noninvasive model. Hepatology. 2005;42(6):1437–1445CrossRefPubMed
20.
go back to reference He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2016; pp. 770-778 He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2016; pp. 770-778
21.
go back to reference Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26(4):404–413CrossRef Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26(4):404–413CrossRef
22.
go back to reference Delong ER, Delong DM, Clarkepearson DI. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–845CrossRefPubMed Delong ER, Delong DM, Clarkepearson DI. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–845CrossRefPubMed
23.
go back to reference Lee YA, Wallace MC, Friedman SL. Pathobiology of liver fibrosis: a translational success story. Gut. 2015;64(5):830–841CrossRefPubMed Lee YA, Wallace MC, Friedman SL. Pathobiology of liver fibrosis: a translational success story. Gut. 2015;64(5):830–841CrossRefPubMed
24.
go back to reference Xiao G, Yang J, Yan L. Comparison of diagnostic accuracy of aspartate aminotransferase to platelet ratio index and fibrosis-4 index for detecting liver fibrosis in adult patients with chronic hepatitis B virus infection: a systemic review and meta-analysis. Hepatology. 2015;61(1):292–302CrossRefPubMed Xiao G, Yang J, Yan L. Comparison of diagnostic accuracy of aspartate aminotransferase to platelet ratio index and fibrosis-4 index for detecting liver fibrosis in adult patients with chronic hepatitis B virus infection: a systemic review and meta-analysis. Hepatology. 2015;61(1):292–302CrossRefPubMed
26.
go back to reference Martinez SM, Crespo G, Navasa M, et al. Noninvasive assessment of liver fibrosis. Hepatology. 2011;53(1):325–335CrossRefPubMed Martinez SM, Crespo G, Navasa M, et al. Noninvasive assessment of liver fibrosis. Hepatology. 2011;53(1):325–335CrossRefPubMed
27.
go back to reference Ferraioli G, Wong VW-S, Castera L, et al. Liver ultrasound elastography: an update to the world federation for ultrasound in medicine and biology guidelines and recommendations. Ultrasound Med Biol. 2018;44(12):2419–2440CrossRefPubMed Ferraioli G, Wong VW-S, Castera L, et al. Liver ultrasound elastography: an update to the world federation for ultrasound in medicine and biology guidelines and recommendations. Ultrasound Med Biol. 2018;44(12):2419–2440CrossRefPubMed
28.
go back to reference Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Int Conf Learn Represent; 2015; San Diego, CA, US. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Int Conf Learn Represent; 2015; San Diego, CA, US.
29.
go back to reference Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision. In: IEEE Conf Comput Vision Pattern Recognit; 2016; Las Vegas, NV, USA. Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision. In: IEEE Conf Comput Vision Pattern Recognit; 2016; Las Vegas, NV, USA.
30.
go back to reference Huang G, Liu Z, Van der Maaten L, et al. Densely Connected Convolutional Networks. In: IEEE Conf Comput Vision Pattern Recognit; 2017; Honolulu, HI, USA. Huang G, Liu Z, Van der Maaten L, et al. Densely Connected Convolutional Networks. In: IEEE Conf Comput Vision Pattern Recognit; 2017; Honolulu, HI, USA.
Metadata
Title
Diagnosis of significant liver fibrosis in patients with chronic hepatitis B using a deep learning-based data integration network
Authors
Zhong Liu
Huiying Wen
Ziqi Zhu
Qinyuan Li
Li Liu
Tianjiao Li
Wencong Xu
Chao Hou
Bin Huang
Zhiyan Li
Changfeng Dong
Xin Chen
Publication date
21-03-2022
Publisher
Springer India
Published in
Hepatology International / Issue 3/2022
Print ISSN: 1936-0533
Electronic ISSN: 1936-0541
DOI
https://doi.org/10.1007/s12072-021-10294-4

Other articles of this Issue 3/2022

Hepatology International 3/2022 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine
Webinar | 06-02-2024 | 20:00 (CET)

Mastering chronic pancreatitis pain: A multidisciplinary approach and practical solutions

Severe pain is the most common symptom of chronic pancreatitis. In this webinar, experts share the latest insights in pain management for chronic pancreatitis patients. Experts from a range of disciplines discuss pertinent cases and provide practical suggestions for use within clinical practice.

Sponsored by: Viatris

Developed by: Springer Healthcare