Skip to main content
Springer Nature Link
Log in

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

  • Original Article
  • Published:
Hepatology International Aims and scope Submit manuscript

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

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

Data are available upon reasonable request to the corresponding authors.

References

  1. 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):417–432

    Article  PubMed  Google Scholar 

  2. 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):370–398

    Article  Google Scholar 

  3. 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):1560–1599

    Article  PubMed  Google Scholar 

  4. Bedossa P, Poynard T. An algorithm for the grading of activity in chronic hepatitis C. The METAVIR cooperative study group. Hepatology. 1996;24(2):289–293

    Article  CAS  PubMed  Google Scholar 

  5. 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

    Google Scholar 

  6. Zheng R-Q, Wang Q-H, Lu M-D, et al. Liver fibrosis in chronic viral hepatitis: an ultrasonographic study. World J Gastroenterol. 2003;9(11):2484

    Article  PubMed  PubMed Central  Google Scholar 

  7. 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):89–94

    Article  PubMed  Google Scholar 

  8. 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):154–159

    Article  PubMed  Google Scholar 

  9. 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):518–526

    Article  PubMed  Google Scholar 

  10. 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):1317–1325

    Article  CAS  PubMed  Google Scholar 

  11. 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):1877–1885

    Article  PubMed  PubMed Central  Google Scholar 

  12. 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):510–517

    Article  PubMed  Google Scholar 

  13. Kakegawa T, Sugimoto K, Kuroda H, et al. Diagnostic accuracy of two-dimensional shear wave elastography for liver fibrosis: a multicenter prospective study. Clin Gastroenterol Hepatol. 2021. https://doi.org/10.1016/j.cgh.2021.08.021

    Article  PubMed  Google Scholar 

  14. 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):729–741

    Article  CAS  PubMed  Google Scholar 

  15. 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):1264–1273

    Article  PubMed  Google Scholar 

  16. Ruan D, Shi Y, Jin L, et al. An ultrasound image-based deep multi-scale texture network for liver fibrosis grading in patients with chronic HBV infection. Liver Int. 2021. https://doi.org/10.1111/liv.14999

    Article  PubMed  Google Scholar 

  17. 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):1276–1295

    Article  PubMed  Google Scholar 

  18. 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):616–623

    Article  PubMed  Google Scholar 

  19. 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):1437–1445

    Article  PubMed  Google Scholar 

  20. 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. Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26(4):404–413

    Article  Google Scholar 

  22. 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):837–845

    Article  CAS  PubMed  Google Scholar 

  23. Lee YA, Wallace MC, Friedman SL. Pathobiology of liver fibrosis: a translational success story. Gut. 2015;64(5):830–841

    Article  CAS  PubMed  Google Scholar 

  24. 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):292–302

    Article  PubMed  Google Scholar 

  25. Patel K, Sebastiani G. Limitations of non-invasive tests for assessment of liver fibrosis. JHEP Rep. 2020;2(2):100067

    Article  PubMed  PubMed Central  Google Scholar 

  26. Martinez SM, Crespo G, Navasa M, et al. Noninvasive assessment of liver fibrosis. Hepatology. 2011;53(1):325–335

    Article  PubMed  Google Scholar 

  27. 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):2419–2440

    Article  PubMed  Google Scholar 

  28. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Int Conf Learn Represent; 2015; San Diego, CA, US.

  29. 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. 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.

Download references

Acknowledgements

The authors would like to thank all of the patients for their participation in this study.

Funding

This work was partially supported by National Natural Science Foundation of China under Grants 81871429 and 61901282, and Key-Area Research and Development Program of Guangdong Province under Grant 2020B1111130002.

Author information

Author notes

  1. Zhong Liu, Huiying Wen, and Ziqi Zhu contributed equally to this work.

Authors and Affiliations

  1. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, People’s Republic of China

    Zhong Liu, Huiying Wen, Ziqi Zhu, Tianjiao Li, Wencong Xu, Chao Hou, Bin Huang & Xin Chen

  2. First Medical College of Guangdong Medical University, Zhanjiang, People’s Republic of China

    Qinyuan Li

  3. Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, People’s Republic of China

    Li Liu

  4. National Clinical Research Centre for Infectious Disease, Department of Ultrasound, Shenzhen Third People’s Hospital, Shenzhen, People’s Republic of China

    Zhiyan Li & Changfeng Dong

Authors
  1. Zhong Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Huiying Wen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ziqi Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Qinyuan Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Li Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Tianjiao Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Wencong Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Chao Hou
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Bin Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Zhiyan Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Changfeng Dong
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Xin Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

ZL and HW designed the study. ZL, CD and XC had full access to all the data. ZL, HW, ZZ and QL analyzed the data. ZL and HW wrote the manuscript. CD and XC fully supervised the study. All authors provided substantial comments on drafts and approved the final report.

Corresponding authors

Correspondence to Changfeng Dong or Xin Chen.

Ethics declarations

Conflict of interest

Zhong Liu, Huiying Wen, Ziqi Zhu, Qinyuan Li, Li Liu, Tianjiao Li, Wencong Xu, Chao Hou, Bin Huang, Zhiyan Li, Changfeng Dong, and Xin Chen have no conflicts of interest to disclose.

Animal research

Not applicable.

Consent to participate

This study was approved by the hospital’s ethical review board (Shenzhen Third People’s Hospital, Shenzhen, China) and fully complied with the 1964 Helsinki declaration and its later amendments. Informed consent was obtained from all the patients enrolled.

Consent to publish

All authors approved the publication of the manuscript.

Plant reproducibility

Not applicable.

Clinical trial registration

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 348 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Z., Wen, H., Zhu, Z. et al. Diagnosis of significant liver fibrosis in patients with chronic hepatitis B using a deep learning-based data integration network. Hepatol Int 16, 526–536 (2022). https://doi.org/10.1007/s12072-021-10294-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12072-021-10294-4

Keywords