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Reader agreement and accuracy of ultrasound features for hepatic steatosis

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Abstract

Purpose

The purpose of the study is to assess the reader agreement and accuracy of eight ultrasound imaging features for classifying hepatic steatosis in adults with known or suspected hepatic steatosis.

Methods

This was an IRB-approved, HIPAA-compliant prospective study of adult patients with known or suspected hepatic steatosis. All patients signed written informed consent. Ultrasound images (Siemens S3000, 6C1HD, and 4C1 transducers) were acquired by experienced sonographers following a standard protocol. Eight readers independently graded eight features and their overall impression of hepatic steatosis on ordinal scales using an electronic case report form. Duplicated images from the 6C1HD transducer were read twice to assess intra-reader agreement. Intra-reader, inter-transducer, and inter-reader agreement were assessed using intraclass correlation coefficients (ICC). Features with the highest intra-reader agreement were selected as predictors for dichotomized histological steatosis using Classification and Regression Tree (CART) analysis, and the accuracy of the decision rule was compared to the accuracy of the radiologists’ overall impression.

Results

45 patients (18 males, 27 females; mean age 56 ± 12 years) scanned from September 2015 to July 2016 were included. Mean intra-reader ICCs ranged from 0.430 to 0.777, inter-transducer ICCs ranged from 0.228 to 0.640, and inter-reader ICCs ranged from 0.014 to 0.561. The CART decision rule selected only large hepatic vein blurring and achieved similar accuracy to the overall impression (74% to 75% and 68% to 72%, respectively).

Conclusions

Large hepatic vein blurring, liver–kidney contrast, and overall impression provided the highest reader agreement. Large hepatic vein blurring may provide the highest classification accuracy for dichotomized grading of hepatic steatosis.

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Abbreviations

NAFLD:

Nonalcoholic Fatty Liver Disease

NASH CRN:

Nonalcoholic Steatohepatitis Clinical Research Network

ICC:

Intraclass correlation coefficients

CART:

Classification and Regression Tree

MRI:

Magnetic resonance imaging

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Authors and Affiliations

  1. Department of Radiology, University of California San Diego, 200 W. Arbor Drive #8756, San Diego, CA, 92103-8756, USA

    Cheng William Hong, Soudabeh Fazeli Dekhordy, Alexandra N. Schlein, Elise Housman, Lisa H. Deiranieh, Charles Q. Li, Giovanna Casola, Mary O’Boyle, Katherine M. Richman, Michael Andre & Claude B. Sirlin

  2. School of Medicine, University of California San Diego, La Jolla, CA, USA

    Austin Marsh

  3. Computational and Applied Statistics Laboratory, University of California San Diego, La Jolla, CA, USA

    Tanya Wolfson

  4. Department of Radiology, University of Washington, Seattle, WA, USA

    Jeremy Paige

  5. Department of Radiology, University of Michigan, Ann Arbor, MI, USA

    Ashish P. Wasnik

  6. Department of Radiology, University of Toronto, Toronto, ON, Canada

    Hyun-Jung Jang

  7. Department of Internal Medicine 2, Caritas-Krankenhaus Bad Mergentheim, Bad Mergentheim, Germany

    Christoph F. Dietrich

  8. Department of Internal Medicine, Università di Bologna, Bologna, Italy

    Fabio Piscaglia

  9. Department of Pathology, University of California San Diego, La Jolla, CA, USA

    Mark A. Valasek

  10. NAFLD Research Center, Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA

    Rohit Loomba

Authors
  1. Cheng William Hong
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  2. Austin Marsh
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  3. Tanya Wolfson
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  4. Jeremy Paige
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Corresponding author

Correspondence to Claude B. Sirlin.

Ethics declarations

Funding

The authors acknowledge Grant support from the National Institutes of Health T32 EB005970-09 and R01 DK106419-03. The study was also supported in part by an investigator-initiated research grant from Siemens to the senior and second senior author. No author of this study is an employee or consultant to Siemens. All data belonged to the authors who had complete control of the manuscript content. The senior author has research grants from Siemens, GE, Philips, and Bayer.

Conflict of interest

C.W.H., A.W., T.W., J.P., S.F.D., A.S.N., E.H., L.D., C.Q.L., A.W, H.J.J., C.F.D., G.C., M.O.B., K.M.R., M.A.V., and M.A. declare that they have no conflicts of interest. F.P. is on the advisory board and speaker bureau of Bayer, on the advisory board of EISAI, on the speaker bureau of Bracco, and has a research contract with ESAOTE. R.L. receives grant funding from Adheron, Arora, BMS, Daiichi-Sankyo Inc., Galectin, Galmed, GE, Genfit, Gilead, Immuron, Intercept, Janssen Inc, KineMed, Madrigal, Merck, NGM, Promedior, Prometheus, Siemens, Sirius, and Tobira and is a consultant or on the advisory committee for Bird Rock Bio, BMS, Boehringer Ingelheim, Celgene, Conatus, Eli Lily, Enanta, Gilead, GRI Bio, Madrigal, Metacrine, NGM, Pfizer, Receptos, Sanofi, Scholar Rock, Zafgen, Arrowhead Research, Conatus, Galmed, Gilead, Intercept, NGM, Nimbus, Octeta, Tobira, GIR, Inc., and Metacrine, Inc. C.S. receives grant funding from Gilead, GE Healthcare, Siemens, GE MRI, Bayer AMRI, GE Digital, GE US, ACR Innovation, performs contracted research for ICON Medical Imaging/Enanta, Philips, Gilead, Shire, VirtualScopics, Intercept, Synageva, is a consultant for Boehringer Ingelheim, is a member of the external advisory board of AMRA Medical and Guerbet (fee paid to the University of California Regents), and a speaker for GE Healthcare and Bayer (fee paid to the University of California Regents).

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Hong, C.W., Marsh, A., Wolfson, T. et al. Reader agreement and accuracy of ultrasound features for hepatic steatosis. Abdom Radiol 44, 54–64 (2019). https://doi.org/10.1007/s00261-018-1683-0

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