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Artificial intelligence in assessment of hepatocellular carcinoma treatment response

  • Special Section: HCC Treatment
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

A Correction to this article was published on 24 May 2021

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Abstract

Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).

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Abbreviations

TACE:

Transarterial chemoembolization

CT:

Computed tomography

HCC:

Hepatocellular carcinoma

GLCM:

Gray level co-occurrence matrix

DEB-TACE:

Drug-eluting bead transarterial chemoembolization

vRECIST:

Volumetric response evaluation criteria in solid tumors

CNN:

Convolutional neural network

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Acknowledgements

We are grateful to the members of the LI-RADS Treatment Response (TR LI-RADS) work group for their guidance and helpful discussions.

Funding

None.

Author information

Authors and Affiliations

  1. Department of Radiology, Louisiana State University Health Sciences Center, 1542 Tulane Avenue, Rm 343, New Orleans, LA, 70112, USA

    Bradley Spieler

  2. Department of Medical Imaging, College of Medicine, University of Arizona, P.O. Box 245067, Tucson, AZ, 85724-5067, USA

    Carl Sabottke

  3. Department of Imaging Physics, College of Medicine, The University of Texas, P.O. Box 301402, Houston, TX, 77230-1402, USA

    Ahmed W. Moawad

  4. Department of Interventional Radiology, OHSU and Tanta University, Egypt, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3011, USA

    Ahmed M. Gabr

  5. Department of Radiology, Duke University Medical Center, Box 3808, Durham, NC, 27710, USA

    Mustafa R. Bashir

  6. Department of Radiology, 300 East 66th Street Floors 5-6, New York, NY, 10065, USA

    Richard Kinh Gian Do

  7. Department of Radiology, University of California, Irvine, 101 City Drive South Bldg. 1, Rt. 140, Orange, CA, 92868, USA

    Vahid Yaghmai

  8. Department of Radiology, Thunder Bay Regional Health Sciences Centre, 980 Oliver Road, Thunder Bay, ON, Canada

    Radu Rozenberg

  9. Department of Radiology and Medical Imaging, Loyola University Medical Center, 2160 S. First Ave, Maywood, IL, 60153, USA

    Marielia Gerena

  10. Department of Radiology, Georgetown University, 3800 Reservoir Rd NW, Washington, DC, 20007, USA

    Joseph Yacoub

  11. Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX, 77030, USA

    Khaled M. Elsayes

Authors
  1. Bradley Spieler
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  2. Carl Sabottke
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  3. Ahmed W. Moawad
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  4. Ahmed M. Gabr
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  5. Mustafa R. Bashir
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  6. Richard Kinh Gian Do
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  7. Vahid Yaghmai
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  8. Radu Rozenberg
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  9. Marielia Gerena
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  10. Joseph Yacoub
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  11. Khaled M. Elsayes
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Contributions

BS and CS analyzed the data and wrote the manuscript; AM, MGA, MB, RKG, VY, RR, MG, and JY made critical revisions related to important intellectual content of the manuscript; CS, AM, and KE analyzed the literature, wrote the manuscript and made critical revisions related to important intellectual content of the manuscript; and all authors have read and approve the final manuscript

Corresponding author

Correspondence to Bradley Spieler.

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The authors declare that they have no conflict of interest.

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Spieler, B., Sabottke, C., Moawad, A.W. et al. Artificial intelligence in assessment of hepatocellular carcinoma treatment response. Abdom Radiol 46, 3660–3671 (2021). https://doi.org/10.1007/s00261-021-03056-1

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  • DOI: https://doi.org/10.1007/s00261-021-03056-1

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