Evaluation of treatment response in hepatocellular carcinoma in the explanted liver with Liver Imaging Reporting and Data System version 2017

  • Nieun Seo
  • Myoung Soo Kim
  • Mi-Suk ParkEmail author
  • Jin-Young Choi
  • Richard K. G. Do
  • Kyunghwa Han
  • Myeong-Jin Kim



To investigate the performance of Liver Imaging Reporting and Data System (LI-RADS) v2017 treatment response algorithm for predicting hepatocellular carcinoma (HCC) viability after locoregional therapy (LRT) using the liver explant as reference.


One hundred fourteen patients with 206 HCCs who underwent liver transplantation (LT) after LRT for HCCs were included in this retrospective study. Two radiologists independently evaluated tumor viability using the LI-RADS and modified RECIST (mRECIST) with CT and MRI, respectively. The sensitivity and specificity of arterial phase hyperenhancement (APHE) and LR-TR viable criteria (any of three findings: APHE, washout, and enhancement pattern similar to pretreatment imaging) were compared using logistic regression. Receiver operating characteristics (ROC) analysis was used to compare the diagnostic performance between LI-RADS and mRECIST and between CT and MRI.


The sensitivity and specificity for diagnosing viable tumor were not significantly different between APHE alone and LR-TR viable criteria on CT (p = 0.054 and p = 0.317) and MRI (p = 0.093 and p = 0.603). On CT, the area under the ROC curve (AUC) of LI-RADS was significantly higher than that of mRECIST (0.733 vs. 0.657, p < 0.001). On MRI, there was no significant difference in AUCs between LI-RADS and mRECIST (0.802 vs. 0.791, p = 0.500). Intra-individual comparison of CT and MRI showed comparable AUCs using LI-RADS (0.783 vs. 0.795, p = 0.776).


LI-RADS v2017 treatment response algorithm showed better diagnostic performance than mRECIST on CT. With LI-RADS, CT and MRI were comparable to diagnose tumor viability of HCC after LRT.

Key Points

Using Liver Imaging Reporting and Data System (LI-RADS) v2017 treatment response algorithm, the viability of hepatocellular carcinoma (HCC) after locoregional therapy (LRT) can be accurately diagnosed.

LI-RADS v2017 treatment response algorithm is superior to modified Response Evaluation Criteria in Solid Tumors for evaluating HCC viability using CT.

Either CT or MRI can be performed to assess tumor viability after LRT using LI-RADS v2017 treatment response algorithm.


Liver transplantation Hepatocellular carcinoma Multidetector computed tomography Magnetic resonance imaging Therapeutic chemoembolization 



Arterial phase hyperenhancement


Computed tomography


European Association for the Study of the Liver


Generalized estimating equation


Hepatocellular carcinoma


Liver Imaging Reporting and Data System


Locoregional therapy


Liver transplantation


Modified Response Criteria in Solid Tumors


Magnetic resonance imaging


Picture archiving and communication system


Radiofrequency ablation


Transarterial chemoembolization


World Health Organization



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Mi-Suk Park.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Kyunghwa Han performed statistical analysis, who is one of the coauthors.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

Supplementary material

330_2019_6376_MOESM1_ESM.docx (19 kb)
ESM 1 (DOCX 19 kb)


  1. 1.
    Yao FY, Fidelman N (2016) Reassessing the boundaries of liver transplantation for hepatocellular carcinoma: where do we stand with tumor down-staging? Hepatology 63:1014–1025CrossRefGoogle Scholar
  2. 2.
    Miller AB, Hoogstraten B, Staquet M, Winkler A (1981) Reporting results of cancer treatment. Cancer 47:207–214CrossRefGoogle Scholar
  3. 3.
    Therasse P, Arbuck SG, Eisenhauer EA et al (2000) New guidelines to evaluate the response to treatment in solid tumors. J Natl Cancer Inst 92:205–216CrossRefGoogle Scholar
  4. 4.
    Bruix J, Sherman M, Llovet JM et al (2001) Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 EASL conference. European Association for the Study of the Liver. J Hepatol 35:421–430CrossRefGoogle Scholar
  5. 5.
    Lencioni R, Llovet JM (2010) Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis 30:52–60CrossRefGoogle Scholar
  6. 6.
    Shim JH, Lee HC, Kim SO et al (2012) Which response criteria best help predict survival of patients with hepatocellular carcinoma following chemoembolization? A validation study of old and new models. Radiology 262:708–718CrossRefGoogle Scholar
  7. 7.
    Jung ES, Kim JH, Yoon EL et al (2013) Comparison of the methods for tumor response assessment in patients with hepatocellular carcinoma undergoing transarterial chemoembolization. J Hepatol 58:1181–1187CrossRefGoogle Scholar
  8. 8.
    Kim HD, Shim JH, Kim GA et al (2015) Optimal methods for measuring eligibility for liver transplant in hepatocellular carcinoma patients undergoing transarterial chemoembolization. J Hepatol 62:1076–1084CrossRefGoogle Scholar
  9. 9.
    Gordic S, Corcuera-Solano I, Stueck A et al (2017) Evaluation of HCC response to locoregional therapy: validation of MRI-based response criteria versus explant pathology. J Hepatol 67:1213–1221CrossRefGoogle Scholar
  10. 10.
    American College of Radiology. Liver imaging reporting and data system (LI-RADS). American College of Radiology. Web site. Accessed 1 June 2017
  11. 11.
    Kielar A, Fowler KJ, Lewis S et al (2018) Locoregional therapies for hepatocellular carcinoma and the new LI-RADS treatment response algorithm. Abdom Radiol (NY) 43:218–230CrossRefGoogle Scholar
  12. 12.
    Seo N, Kim MS, Park MS et al (2019) Optimal criteria for hepatocellular carcinoma diagnosis using CT in patients undergoing liver transplantation. Eur Radiol 29:1022–1031CrossRefGoogle Scholar
  13. 13.
    Luca A, Caruso S, Milazzo M et al (2010) Multidetector-row computed tomography (MDCT) for the diagnosis of hepatocellular carcinoma in cirrhotic candidates for liver transplantation: prevalence of radiological vascular patterns and histological correlation with liver explants. Eur Radiol 20:898–907CrossRefGoogle Scholar
  14. 14.
    Elsayes KM, Hooker JC, Agrons MM et al (2017) 2017 version of LI-RADS for CT and MR imaging: an update. Radiographics 37:1994–2017CrossRefGoogle Scholar
  15. 15.
    Edmondson HA, Steiner PE (1954) Primary carcinoma of the liver: a study of 100 cases among 48,900 necropsies. Cancer 7:462–503CrossRefGoogle Scholar
  16. 16.
    Hillis SL, Berbaum KS, Metz CE (2008) Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis. Acad Radiol 15:647–661CrossRefGoogle Scholar
  17. 17.
    Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRefGoogle Scholar
  18. 18.
    American College of Radiology. Liver Imaging Reporting and Data System. Accessed 6 May 2019
  19. 19.
    Bargellini I, Vignali C, Cioni R et al (2010) Hepatocellular carcinoma: CT for tumor response after transarterial chemoembolization in patients exceeding Milan criteria--selection parameter for liver transplantation. Radiology 255:289–300CrossRefGoogle Scholar
  20. 20.
    Hunt SJ, Yu W, Weintraub J, Prince MR, Kothary N (2009) Radiologic monitoring of hepatocellular carcinoma tumor viability after transhepatic arterial chemoembolization: estimating the accuracy of contrast-enhanced cross-sectional imaging with histopathologic correlation. J Vasc Interv Radiol 20:30–38CrossRefGoogle Scholar
  21. 21.
    Kloeckner R, Otto G, Biesterfeld S, Oberholzer K, Dueber C, Pitton MB (2010) MDCT versus MRI assessment of tumor response after transarterial chemoembolization for the treatment of hepatocellular carcinoma. Cardiovasc Intervent Radiol 33:532–540CrossRefGoogle Scholar
  22. 22.
    Shim JH, Han S, Shin YM et al (2013) Optimal measurement modality and method for evaluation of responses to transarterial chemoembolization of hepatocellular carcinoma based on enhancement criteria. J Vasc Interv Radiol 24:316–325CrossRefGoogle Scholar
  23. 23.
    Cha DI, Jang KM, Kim SH, Kang TW, Song KD (2017) Liver Imaging Reporting and Data System on CT and gadoxetic acid-enhanced MRI with diffusion-weighted imaging. Eur Radiol 27:4394–4405CrossRefGoogle Scholar
  24. 24.
    Cha DI, Lee MW, Kim YK et al (2014) Assessing patients with hepatocellular carcinoma meeting the Milan criteria: is liver 3 tesla MR with gadoxetic acid necessary in addition to liver CT? J Magn Reson Imaging 39:842–852CrossRefGoogle Scholar
  25. 25.
    Kakihara D, Nishie A, Harada N et al (2014) Performance of gadoxetic acid-enhanced MRI for detecting hepatocellular carcinoma in recipients of living-related-liver-transplantation: comparison with dynamic multidetector row computed tomography and angiography-assisted computed tomography. J Magn Reson Imaging 40:1112–1120CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of Radiology, Severance HospitalYonsei University College of MedicineSeoulSouth Korea
  2. 2.Department of SurgeryYonsei University College of MedicineSeoulSouth Korea
  3. 3.Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkUSA
  4. 4.Department of Radiology, Research Institute of Radiological ScienceYonsei Biomedical Research InstituteSeoulSouth Korea

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