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Quantitative ultrasound and machine learning for assessment of steatohepatitis in a rat model

  • Ultrasound
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Abstract

Objectives

To develop a machine learning model based on quantitative ultrasound (QUS) parameters to improve classification of steatohepatitis with shear wave elastography in rats by using histopathology scoring as the reference standard.

Methods

This study received approval from the institutional animal care committee. Sixty male Sprague-Dawley rats were either fed a standard chow or a methionine- and choline-deficient diet. Ultrasound-based radiofrequency images were recorded in vivo to generate QUS and elastography maps. Random forests classification models and a bootstrap method were used to identify the QUS parameters that improved the classification accuracy of elastography. Receiver-operating characteristic analyses were performed.

Results

For classification of not steatohepatitis vs borderline or steatohepatitis, the area under the receiver-operating characteristic curve (AUC) increased from 0.63 for elastography alone to 0.72 for a model that combined elastography and QUS techniques (p < 0.001). For detection of liver steatosis grades 0 vs ≥ 1, ≤ 1 vs ≥ 2, ≤ 2 vs 3, respectively, the AUCs increased from 0.70, 0.65, and 0.69 to 0.78, 0.78, and 0.75 (p < 0.001). For detection of liver inflammation grades 0 vs ≥ 1, ≤ 1 vs ≥ 2, ≤ 2 vs 3, respectively, the AUCs increased from 0.58, 0.77, and 0.78 to 0.66, 0.84, and 0.87 (p < 0.001). For staging of liver fibrosis grades 0 vs ≥ 1, ≤ 1 vs ≥ 2, and ≤ 2 vs ≥ 3, respectively, the AUCs increased from 0.79, 0.92, and 0.91 to 0.85, 0.98, and 0.97 (p < 0.001).

Conclusion

QUS parameters improved the classification accuracy of steatohepatitis, liver steatosis, inflammation, and fibrosis compared to shear wave elastography alone.

Key Points

• Quantitative ultrasound and shear wave elastography improved classification accuracy of liver steatohepatitis and its histological features (liver steatosis, inflammation, and fibrosis) compared to elastography alone.

• A machine learning approach based on random forest models and incorporating local attenuation and homodyned-K tissue modeling shows promise for classification of nonalcoholic steatohepatitis.

• Further research should be performed to demonstrate the applicability of this multi-parametric QUS approach in a human cohort and to validate the combinations of parameters providing the highest classification accuracy.

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Abbreviations

AUC:

Area under the receiver-operating characteristic curve

CAP:

Controlled attenuation parameter

HKD:

Homodyned-K distributions

HPS:

Hematoxylin phloxine saffron

IQR:

Inter-quartile range

MCD:

Methionine and choline deficient

NAFLD:

Nonalcoholic fatty liver disease

NASH:

Nonalcoholic steatohepatitis

QUS:

Quantitative ultrasound

ROC:

Receiver operating characteristic

ROI:

Region of interest

SH:

Steatohepatitis

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Acknowledgments

We thank Mr. Jamal Ait Ichou for his assistance in the literature review.

Funding

This study has received funding by Fonds de Recherche du Québec—Nature et Technologies (FRQNT) (PR-174387), Canadian Institutes of Health Research, Institute of Nutrition, Metabolism, and Diabetes (grant nos. 273738 and 301520), Quebec Bio-imaging Network (QBIN/RBIQ #5886), New Researcher Startup Grant from the Centre de Recherche du Centre Hospitalier de l’Université de Montréal, and by a Chercheur-Boursier Junior 2 from the Fonds de Recherche du Québec en Santé and Fondation de l’association des radiologistes du Québec (FRQS-ARQ #34939) to An Tang.

Author information

Authors and Affiliations

  1. Department of Radiology, Centre hospitalier de l’Université de Montréal (CHUM), Montréal, Québec, Canada

    An Tang

  2. Department of Radiology, Radio-oncology and Nuclear Medicine, Université de Montréal and CRCHUM, 1000 rue Saint-Denis, Montréal, Québec, H2X 0C2, Canada

    An Tang & Guy Cloutier

  3. Laboratory of Medical Image Analysis, Centre de recherche du Centre hospitalier de l’Université de Montréal (CRCHUM), Montréal, Québec, Canada

    An Tang

  4. Université de Montréal, Institute of Biomedical Engineering, Montréal, Québec, Canada

    An Tang, Julian Garcia-Duitama & Guy Cloutier

  5. Laboratory of Biorheology and Medical Ultrasonics (LBUM), Centre de recherche du Centre hospitalier de l’Université de Montréal (CRCHUM), Montréal, Québec, Canada

    François Destrempes, Julian Garcia-Duitama & Guy Cloutier

  6. School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran

    Siavash Kazemirad

  7. Department of Pathology, Centre hospitalier de l’Université de Montréal (CHUM), Montréal, Québec, Canada

    Bich N. Nguyen

  8. Department of Pathology and Cellular Biology, Université de Montréal, Montréal, Québec, Canada

    Bich N. Nguyen

Authors
  1. An Tang
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  2. François Destrempes
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  3. Siavash Kazemirad
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  4. Julian Garcia-Duitama
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  5. Bich N. Nguyen
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  6. Guy Cloutier
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Corresponding author

Correspondence to An Tang.

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Guarantor

The scientific guarantor if this publication is An Tang.

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Approval from the institutional animal care committee was obtained.

Ethical approval

Institutional review board approval was obtained.

Study subjects or cohorts overlap

The study cohorts have been previously reported in [24]. That prior article focused on a comparison between low-frequency versus high-frequency ultrasonographic shear wave elastography for the detection of steatohepatitis. In contrast, the present article investigates a machine learning model based on quantitative ultrasound parameters to improve classification of steatohepatitis compared to shear wave elastography alone.

Methodology

• Diagnostic study

• Experimental

• Performed at one institution

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Tang, A., Destrempes, F., Kazemirad, S. et al. Quantitative ultrasound and machine learning for assessment of steatohepatitis in a rat model. Eur Radiol 29, 2175–2184 (2019). https://doi.org/10.1007/s00330-018-5915-z

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  • DOI: https://doi.org/10.1007/s00330-018-5915-z

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