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

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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.

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Data availability

Data are available upon reasonable request to the corresponding authors.

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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
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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.

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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

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

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