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Role of Multiparametric Ultrasound in Evaluating Hepatic Acute Graft-versus-Host Disease: An Animal Study

Ultrasound in Medicine & Biology, ISSN: 0301-5629, Vol: 49, Issue: 6, Page: 1449-1456
2023
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    Citations
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    Usage
  • 1
    Captures
  • 1
    Mentions
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    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Captures
    1
  • Mentions
    1
    • News Mentions
      1
      • 1

Most Recent News

Peking University People's Hospital Reports Findings in Graft-Versus-Host Disease (Role of Multiparametric Ultrasound in Evaluating Hepatic Acute Graft-versus-Host Disease: An Animal Study)

2023 MAR 30 (NewsRx) -- By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News -- New research on Immune

Article Description

Hepatic acute graft-versus-host disease (aGVHD) is a serious complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT) and is one of the leading causes of early non-recurrent death. The current diagnosis is based mainly based on clinical diagnosis, and there is a lack of non-invasive quantitative diagnosis methods. We propose a multiparametric ultrasound (MPUS) imaging method and explore its effectiveness in evaluating hepatic aGVHD. In this study, 48 female Wistar rats were used as receptors and 12 male Fischer 344 rats were used as donors for allo-HSCT to establish aGVHD models. After transplantation, 8 rats were randomly selected for ultrasonic examination weekly, including color Doppler ultrasound, contrast-enhanced ultrasound (CEUS) and shear wave dispersion (SWD) imaging. The values of nine ultrasonic parameters were obtained. Hepatic aGVHD was subsequently diagnosed by histopathological analysis. A classification model for predicting hepatic aGVHD was established using principal component analysis and support vector machines. According to the pathological results, the transplanted rats were categorized into the hepatic aGVHD and non-GVHD (nGVHD) groups. All parameters obtained by MPUS differed statistically between the two groups. The first three contributing percentages of principal component analysis results were resistivity index, peak intensity and shear wave dispersion slope, respectively. The accuracy of classifying aGVHD and nGVHD using support vector machines reached 100%. The accuracy of the multiparameter classifier was significantly higher than that of the single parameter. The MPUS imaging method has proven to be useful in detecting hepatic aGVHD.

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