Skip to main content
SpringerLink
Log in

A deep learning model with data integration of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical parameters for diagnosing significant liver fibrosis in patients with chronic hepatitis B

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

A Correction to this article was published on 24 June 2023

This article has been updated

Abstract

Objective

To develop and investigate a deep learning model with data integration of ultrasound contrast-enhanced micro-flow (CEMF) cines, B-mode images, and patients’ clinical parameters to improve the diagnosis of significant liver fibrosis (≥ F2) in patients with chronic hepatitis B (CHB).

Methods

Of 682 CHB patients who underwent ultrasound and histopathological examinations between October 2016 and May 2020, 218 subjects were included in this retrospective study. We devised a data integration-based deep learning (DIDL) model for assessing ≥ F2 in CHB patients. The model contained three convolutional neural network branches to automatically extract features from ultrasound CEMF cines, B-mode images, and clinical data. The extracted features were fused at the backend of the model for decision-making. The diagnostic performance was evaluated across fivefold cross-validation and compared against the other methods in terms of the area under the receiver operating characteristic curve (AUC), with histopathological results as the reference standard.

Results

The mean AUC achieved by the DIDL model was 0.901 [95% CI, 0.857–0.939], which was significantly higher than those of the comparative methods, including the models trained by using only CEMF cines (0.850 [0.794–0.893]), B-mode images (0.813 [0.754–0.862]), or clinical data (0.757 [0.694–0.812]), as well as the conventional TIC method (0.752 [0.689–0.808]), APRI (0.792 [0.734–0.845]), FIB-4 (0.776 [0.714–0.829]), and visual assessments of two radiologists (0.812 [0.754–0.862], and 0.800 [0.739–0.849]), all ps < 0.01, DeLong test.

Conclusion

The DIDL model with data integration of ultrasound CEMF cines, B-mode images, and clinical parameters showed promising performance in diagnosing significant liver fibrosis for CHB patients.

Key Points

• The combined use of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical data in a deep learning model has potential to improve the diagnosis of significant liver fibrosis.

• The deep learning model with the fusion of features extracted from multimodality data outperformed the conventional methods including mono-modality data-based models, the time-intensity curve-based recognizer, fibrosis biomarkers, and visual assessments by experienced radiologists.

• The interpretation of the feature attention maps in the deep learning model may help radiologists get better understanding of liver fibrosis-related features and hence potentially enhancing their diagnostic capacities.

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

Access this article

Log in via an institution

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
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

Data were available upon reasonable request to the corresponding authors.

Change history

Abbreviations

 ≥ F2:

Significant liver fibrosis

APRI:

Aspartate transaminase to platelet ratio index

AUC :

Area under the receiver operating characteristic curve

CA:

Contrast agent

CEMF:

Contrast-enhanced micro-flow

CHB:

Chronic hepatitis B

CI :

Confidence interval

CT:

Computed tomography

DIDL:

Data integration-based deep learning

FC:

Fully connected

FIB-4:

Fibrosis index based on four factors

MR:

Magnetic resonance

ROC:

Receiver operating characteristic

ROI :

Region of interest

SD:

Standard deviation

TIC:

Time-intensity curve

US:

Ultrasound

References

  1. Vittal A, Ghany MG (2019) WHO guidelines for prevention, care and treatment of individuals infected with HBV A US perspective. Clin Liver Dis 23(3):417–432

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lampertico P, Agarwal K, Berg T et al (2017) EASL 2017 Clinical Practice Guidelines on the management of hepatitis B virus infection. J Hepatol 67(2):370–398

    Article  Google Scholar 

  3. Terrault NA, Lok ASF, McMahon BJ et al (2018) Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. Hepatology 67(4):1560–1599

    Article  PubMed  Google Scholar 

  4. Tan M, Bhadoria AS, Cui F et al (2021) 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 6(2):106–119

    Google Scholar 

  5. Ferraioli G, Wong VW-S, Castera L et al (2018) Liver ultrasound elastography: an update to the World Federation for Ultrasound in Medicine and Biology guidelines and recommendations. Ultrasound Med Biol 44(12):2419–2440

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

    Article  CAS  PubMed  Google Scholar 

  7. European Association for the Study of the Liver (2017) EASL 2017 Clinical Practice Guidelines on the management of hepatitis B virus infection. J Hepatol 67(2):370–398

  8. Wong SN, Lok AS (2006) Treatment of hepatitis B: who, when, and how? Arch Intern Med 166(1):9–12

    Article  PubMed  Google Scholar 

  9. Regev A, Berho M, Jeffers LJ et al (2002) Sampling error and intraobserver variation in liver biopsy in patients with chronic HCV infection. Am J Gastroenterol 97(10):2614–2618

    Article  PubMed  Google Scholar 

  10. Bedossa P, Dargere D, Paradis V (2003) Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology 38(6):1449–1457

    Article  PubMed  Google Scholar 

  11. Bravo AA, Sheth SG, Chopra S (2001) Current concepts: liver biopsy. N Engl J Med 344(7):495–500

    Article  CAS  PubMed  Google Scholar 

  12. Zheng R-Q, Wang Q-H, Lu M-D et al (2003) Liver fibrosis in chronic viral hepatitis: an ultrasonographic study. World J Gastroenterol 911:2484

    Article  Google Scholar 

  13. Colli A, Fraquelli M, Andreoletti M et al (2003) Severe liver fibrosis or cirrhosis: accuracy of US for detection—analysis of 300 cases. Radiology 227(1):89–94

    Article  PubMed  Google Scholar 

  14. Salvatore V, Borghi A, Peri E et al (2012) Relationship between hepatic haemodynamics assessed by Doppler ultrasound and liver stiffness. Dig Liver Dis 44(2):154–159

    Article  PubMed  Google Scholar 

  15. Wai CT, Greenson JK, Fontana RJ et al (2003) A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology 38(2):518–526

    Article  PubMed  Google Scholar 

  16. Sterling RK, Lissen E, Clumeck N et al (2006) Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 43(6):1317–1325

    Article  CAS  PubMed  Google Scholar 

  17. Wang K, Lu X, Zhou H et al (2019) Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut 68(4):729–741

    Article  CAS  PubMed  Google Scholar 

  18. Lee JH, Joo I, Kang TW et al (2020) Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network. Eur Radiol 30(2):1264–1273

    Article  PubMed  Google Scholar 

  19. Anteby R, Klang E, Horesh N, et al 2021 Deep learning for noninvasive liver fibrosis classification: a systematic review. Liver Int 41(10):2269–2278

  20. Lu X, Zhou H, Wang K, et al 2021 Comparing radiomics models with different inputs for accurate diagnosis of significant fibrosis in chronic liver disease. Eur Radiol 31(11):8743–8754

  21. Li W, Huang Y, Zhuang B-W et al (2019) Multiparametric ultrasomics of significant liver fibrosis: a machine learning-based analysis. Eur Radiol 29(3):1496–1506

    Article  PubMed  Google Scholar 

  22. Claudon M, Dietrich CF, Choi BI et al (2013) Guidelines and good clinical practice recommendations for contrast enhanced ultrasound (CEUS) in the liver—update 2012: a WFUMB-EFSUMB initiative in cooperation with representatives of AFSUMB, AIUM, ASUM, FLAUS and ICUS. Ultrasound Med Biol 39(2):187–210

    Article  PubMed  Google Scholar 

  23. Li W, Wang W, Liu GJ et al (2015) Differentiation of atypical hepatocellular carcinoma from focal nodular hyperplasia: diagnostic performance of contrast-enhanced US and microflow imaging. Radiology 275(3):870–879

    Article  PubMed  Google Scholar 

  24. Hui AY, Chan HL, Wong VW et al (2005) Identification of chronic hepatitis B patients without significant liver fibrosis by a simple noninvasive predictive model. Am J Gastroenterol 100(3):616–623

    Article  PubMed  Google Scholar 

  25. Zeng MD, Lu LG, Mao YM et al (2005) Prediction of significant fibrosis in HBeAg-positive patients with chronic hepatitis B by a noninvasive model. Hepatology 42(6):1437–1445

    Article  PubMed  Google Scholar 

  26. Bedossa P, Poynard T (1996) An algorithm for the grading of activity in chronic hepatitis C. The METAVIR Cooperative Study Group. Hepatology 24(2):289–293

    Article  CAS  PubMed  Google Scholar 

  27. Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128

    Article  PubMed  Google Scholar 

  28. Tran D, Bourdev L, Fergus R, Torresani L, Paluri M. 2015 Learning spatiotemporal features with 3d convolutional networks. Proceedings of the IEEE international conference on computer vision. 4489–4497

  29. He K, Zhang X, Ren S, Sun J. 2016 Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778

  30. Hung CH, Lu SN, Wang JH et al (2003) Correlation between ultrasonographic and pathologic diagnoses of hepatitis B and C virus-related cirrhosis. J Gastroenterol 38(2):153–157

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  32. Delong ER, Delong DM, Clarkepearson DI (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837–845

    Article  CAS  PubMed  Google Scholar 

  33. Selvaraju RR, Cogswell M, Das A et al (2020) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vision 128(2):336–359

    Article  Google Scholar 

  34. Sugimoto K, Shiraishi J, Moriyasu F et al (2010) Analysis of intrahepatic vascular morphological changes of chronic liver disease for assessment of liver fibrosis stages by micro-flow imaging with contrast-enhanced ultrasound: preliminary experience. Eur Radiol 20(11):2749–2757

    Article  PubMed  Google Scholar 

  35. Huang S, Liang P, Yu X et al (2018) The application of parametric micro-flow imaging in the evaluation of liver fibrosis. Ultrasound Q 34(3):148–155

    Article  PubMed  Google Scholar 

  36. Dietrich CF, Nolsøe CP, Barr RG et al (2020) Guidelines and good clinical practice recommendations for contrast enhanced ultrasound (CEUS) in the liver–update 2020–WFUMB in cooperation with EFSUMB, AFSUMB, AIUM, and FLAUS. Ultraschall in der Medizin-European Journal of Ultrasound 41(05):562–585

    Article  Google Scholar 

  37. Rockey DC, Caldwell SH, Goodman ZD, Nelson RC, Smith AD (2009) Liver biopsy. Hepatology 49(3):1017–1044

    Article  PubMed  Google Scholar 

  38. Neuberger J, Patel J, Caldwell H et al (2020) Guidelines on the use of liver biopsy in clinical practice from the British Society of Gastroenterology, the Royal College of Radiologists and the Royal College of Pathology. Gut 69(8):1382–1403

    Article  PubMed  Google Scholar 

Download references

Funding

This study has received funding supports from National Natural Science Foundation of China under Grants 81871429, 81971630, and 61901282, and Shenzhen Basic Science Research under Grant JCYJ20210324093006017.

Author information

Author notes

  1. Zhong Liu and Wei Li contibuted 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 Medical School, Shenzhen University, 1066 Xueyuan Road, Shenzhen, 518055, People’s Republic of China

    Zhong Liu, Ziqi Zhu, Huiying Wen, Chao Hou, Bin Huang, Yudi Luo & Xin Chen

  2. Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People’s Republic of China

    Wei Li, Ming-de Li, Hui Shen & Wei Wang

Authors
  1. Zhong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziqi Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huiying Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming-de Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chao Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hui Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Bin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yudi Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Wei Wang or Xin Chen.

Ethics declarations

Guarantor

The guarantor of this manuscript is Prof. Xin Chen, Head of School of Biomedical Engineering, Shenzhen University, 1066 Xueyuan Road, Nanshan District, Shenzhen, P. R. China (Email: chenxin@szu.edu.cn).

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Prof. Wei Wang (one of the authors of the manuscript, Email: wangw73@mail.sysu.edu.cn) who is an expert at the First Affiliated Hospital of Sun Yat-Sen University provided statistical advice for this manuscript.

Informed consent

Written informed consent from the patients was exempted due to the retrospective nature of the study.

Ethical approval

Institutional Review Board approval was obtained from the First Affiliated Hospital of Sun Yat-Sen University.

Methodology

• retrospective

• diagnostic study

• performed at one institution

Additional information

Publisher's note

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

The original online version of this article was revised: due to a typesetting mistake the equal contribution statement "Zhong Liu and Wei Li contibuted equally to this work" was missing from the original article.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOC 2034 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Z., Li, W., Zhu, Z. et al. A deep learning model with data integration of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical parameters for diagnosing significant liver fibrosis in patients with chronic hepatitis B. Eur Radiol 33, 5871–5881 (2023). https://doi.org/10.1007/s00330-023-09436-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-09436-z

Keywords

Search

Navigation