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Background <p>Elastography techniques such as two-dimensional shear wave elastography (2D-SWE) often result in missed or misdiagnoses when distinguishing between intermediate stages of liver fibrosis in clinical practice for patients with chronic hepatitis B (CHB).</p> Methods <p>Between January 2020 and August 2023, we prospectively enrolled 964 potentially eligible CHB patients from 6 hospitals who underwent liver biopsy. Finally, 598 patients with 2139 high-frequency ultrasound (HF-US) images were included. LS-Net, a deep learning network based on paired liver-spleen HF-US images was trained for distinguishing different stages of liver fibrosis against a comparison network(L-Net), 2D-SWE, and radiologist. We further simulated potential clinical utility across three clinical guidelines and conducted subgroup analyses for potential confounding factors.</p> Results <p>LS-Net demonstrated consistently superior performance for all-stage liver fibrosis classification in the validation set (AUROC: 0.94, 0.87, 0.92; <i>p</i> &lt; 0.05) compared to L-Net, 2D-SWE, and radiologist assessment. In our clinical simulation focused on CHB patients, LS-Net reduced the biopsy rate to 9.9% for cirrhosis detection, increased essential referral by 40.3% for advanced fibrosis, and substantially improved treatment decision-making for significant fibrosis compared to 2D-SWE. The model maintained stable performance across subgroups (BMI, inflammation, ALT, antiviral status).</p> Conclusions <p>In this development and internal validation study in the CHB patient cohort, LS-Net demonstrated significantly higher diagnostic performance for all-stage liver fibrosis classification compared to L-Net, 2D-SWE and radiologist. Our findings indicate that LS-Net could offer potential clinical value for CHB management by reducing unnecessary biopsy rate, increasing essential referral rate, and promoting timely treatment.</p>

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Paired liver–spleen high-frequency ultrasound deep learning network for full-stage liver fibrosis classification and clinical benefit compared with 2D-SWE in chronic hepatitis B cohort: a prospective multicenter study

  • 期刊论文

  • 发表日期：

    2025年12月23日

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  • Linlin Zhang,
  • Xing Zhao,
  • Ling Song,
  • Hongjin Xiang,
  • Hongyu Zhou,
  • Lichao Mou,
  • YiLei Shi,
  • Lin Ma,
  • Yan Luo

摘要

Background

Elastography techniques such as two-dimensional shear wave elastography (2D-SWE) often result in missed or misdiagnoses when distinguishing between intermediate stages of liver fibrosis in clinical practice for patients with chronic hepatitis B (CHB).

Methods

Between January 2020 and August 2023, we prospectively enrolled 964 potentially eligible CHB patients from 6 hospitals who underwent liver biopsy. Finally, 598 patients with 2139 high-frequency ultrasound (HF-US) images were included. LS-Net, a deep learning network based on paired liver-spleen HF-US images was trained for distinguishing different stages of liver fibrosis against a comparison network(L-Net), 2D-SWE, and radiologist. We further simulated potential clinical utility across three clinical guidelines and conducted subgroup analyses for potential confounding factors.

Results

LS-Net demonstrated consistently superior performance for all-stage liver fibrosis classification in the validation set (AUROC: 0.94, 0.87, 0.92; p < 0.05) compared to L-Net, 2D-SWE, and radiologist assessment. In our clinical simulation focused on CHB patients, LS-Net reduced the biopsy rate to 9.9% for cirrhosis detection, increased essential referral by 40.3% for advanced fibrosis, and substantially improved treatment decision-making for significant fibrosis compared to 2D-SWE. The model maintained stable performance across subgroups (BMI, inflammation, ALT, antiviral status).

Conclusions

In this development and internal validation study in the CHB patient cohort, LS-Net demonstrated significantly higher diagnostic performance for all-stage liver fibrosis classification compared to L-Net, 2D-SWE and radiologist. Our findings indicate that LS-Net could offer potential clinical value for CHB management by reducing unnecessary biopsy rate, increasing essential referral rate, and promoting timely treatment.