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Optimal imaging criteria and modality to determine Milan criteria for the prediction of post-transplant HCC recurrence after locoregional treatment

  • Hepatobiliary-Pancreas
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Abstract

Objectives

We aimed to investigate the optimal radiologic method to determine Milan criteria (MC) for the prediction of recurrence in patients who underwent locoregional treatment (LRT) for hepatocellular carcinoma (HCC) and subsequent liver transplantation (LT).

Methods

This retrospective study included 121 HCC patients who underwent LRT and had both liver dynamic CT and MRI. They were classified with MC using four cross combinations of two imaging modalities (CT and MRI) and two diagnostic criteria (modified Response Evaluation Criteria in Solid Tumors [mRECIST] and Liver Imaging Reporting and Data System treatment response algorithm [LI-RADS TRA]). Competing risk regression was performed to analyze the time to recurrence after LT. The predictive abilities of the four methods for recurrence were evaluated using the time-dependent area under the curve (AUC).

Results

Competing risk regression analyses found that beyond MC determined by MRI with mRECIST was independently associated with recurrence (hazard ratio, 6.926; p = 0.001). With mRECIST, MRI showed significantly higher AUCs than CT at 3 years and 5 years after LT (0.597 vs. 0.756, p = 0.012 at 3 years; and 0.588 vs. 0.733, p = 0.024 at 5 years). Using the pathologic reference standard, MRI with LI-RADS TRA showed higher sensitivity (61.5%) than CT with LI-RADS TRA (30.8%, p < 0.001) or MRI with mRECIST (38.5%, p < 0.001).

Conclusions

MRI with mRECIST was the optimal radiologic method to determine MC for the prediction of post-LT recurrence in HCC patients with prior LRT.

Key Points

• MRI with modified RECIST (mRECIST) is the optimal preoperative method to determine Milan criteria for the prediction of post-transplant HCC recurrence in patients with prior locoregional treatment.

• With mRECIST, MRI was better than CT for the prediction of post-transplant recurrence.

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Abbreviations

AFP:

Alpha fetoprotein

AUC:

Area under the curve

ECA:

Extracellular agent

HBA:

Hepatobiliary agent

HCC:

Hepatocellular carcinoma

LI-RADS:

Liver Imaging Reporting and Data System

LRT:

Locoregional treatment

LT:

Liver transplantation

MC:

Milan criteria

mRECIST:

Modified Response Evaluation Criteria in Solid Tumors

OS:

Overall survival

RFS:

Recurrence-free survival

TRA:

Treatment response algorithm

References

  1. Clavien PA, Lesurtel M, Bossuyt PM, Gores GJ, Langer B, Perrier A (2012) Recommendations for liver transplantation for hepatocellular carcinoma: an international consensus conference report. Lancet Oncol 13:e11–e22

    Article  Google Scholar 

  2. Yu SJ (2016) A concise review of updated guidelines regarding the management of hepatocellular carcinoma around the world: 2010-2016. Clin Mol Hepatol 22:7–17

    Article  Google Scholar 

  3. Yao FY, Fidelman N (2016) Reassessing the boundaries of liver transplantation for hepatocellular carcinoma: where do we stand with tumor down-staging? Hepatology 63:1014–1025

    Article  Google Scholar 

  4. Kim M, Rhu J, Choi GS, Kim JM, Joh JW (2021) Risk factors for poor survival after recurrence of hepatocellular carcinoma after liver transplantation. Ann Surg Treat Res 101:28–36

    Article  Google Scholar 

  5. Fernandez-Sevilla E, Allard MA, Selten J et al (2017) Recurrence of hepatocellular carcinoma after liver transplantation: is there a place for resection? Liver Transpl 23:440–447

    Article  Google Scholar 

  6. Kim JM (2021) Can hepatocellular carcinoma recurrence be prevented after liver transplantation? Clin Mol Hepatol 27:562–563

    Article  Google Scholar 

  7. Mazzaferro V, Regalia E, Doci R et al (1996) Liver transplantation for the treatment of small hepatocellular carcinomas in patients with cirrhosis. N Engl J Med 334:693–699

    Article  CAS  Google Scholar 

  8. Yao FY, Mehta N, Flemming J et al (2015) Downstaging of hepatocellular cancer before liver transplant: long-term outcome compared to tumors within Milan criteria. Hepatology 61:1968–1977

    Article  Google Scholar 

  9. Kardashian A, Florman SS, Haydel B et al (2020) Liver transplantation outcomes in a U.S. multicenter cohort of 789 patients with hepatocellular carcinoma presenting beyond Milan criteria. Hepatology 72:2014–2028

    Article  CAS  Google Scholar 

  10. Lencioni R, Llovet JM (2010) Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis 30:52–60

    Article  CAS  Google Scholar 

  11. American College of Radiology. Liver Imaging Reporting and Data System. https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/LI-RADS. Accessed May 6, 2019.

  12. 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) 43:218–230

    Article  Google Scholar 

  13. 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 33:532–540

    Article  Google Scholar 

  14. 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 24:316–325

    Article  Google Scholar 

  15. 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 39:842–852

    Article  Google Scholar 

  16. 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 40:1112–1120

    Article  Google Scholar 

  17. Bae JS, Lee JM, Yoon JH et al (2021) Evaluation of LI-RADS version 2018 treatment response algorithm for hepatocellular carcinoma in liver transplant candidates: intraindividual comparison between CT and hepatobiliary agent-enhanced MRI. Radiology 299:336–345

    Article  Google Scholar 

  18. Edmondson HA, Steiner PE (1954) Primary carcinoma of the liver: a study of 100 cases among 48,900 necropsies. Cancer 7:462–503

    Article  CAS  Google Scholar 

  19. Prentice RL, Kalbfleisch JD, Peterson AV Jr, Flournoy N, Farewell VT, Breslow NE (1978) The analysis of failure times in the presence of competing risks. Biometrics 34:541–554

    Article  CAS  Google Scholar 

  20. Blanche P, Dartigues JF, Jacqmin-Gadda H (2013) Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med 32:5381–5397

    Article  Google Scholar 

  21. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174

    Article  CAS  Google Scholar 

  22. Seo N, Kim MS, Park MS et al (2020) Evaluation of treatment response in hepatocellular carcinoma in the explanted liver with Liver Imaging Reporting and Data System version 2017. Eur Radiol 30:261–271

    Article  Google Scholar 

  23. 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 67:1213–1221

    Article  Google Scholar 

  24. Kim S, Mannelli L, Hajdu CH et al (2010) Hepatocellular carcinoma: assessment of response to transarterial chemoembolization with image subtraction. J Magn Reson Imaging 31:348–355

    Article  Google Scholar 

  25. Ram R, Kampalath R, Shenoy-Bhangle AS, Arora S, Kielar AZ, Mendiratta-Lala M (2021) LI-RADS treatment response lexicon: review, refresh and resolve with emerging data. Abdom Radiol (NY) 46:3549–3557

    Article  Google Scholar 

  26. Shropshire EL, Chaudhry M, Miller CM et al (2019) LI-RADS treatment response algorithm: performance and diagnostic accuracy. Radiology. https://doi.org/10.1148/radiol.2019182135:182135

  27. Youn SY, Kim DH, Choi SH et al (2021) Diagnostic performance of Liver Imaging Reporting and Data System treatment response algorithm: a systematic review and meta-analysis. Eur Radiol. https://doi.org/10.1007/s00330-020-07464-7

  28. Pelizzaro F, Gambato M, Gringeri E et al (2021) Management of hepatocellular carcinoma recurrence after liver transplantation. Cancers (Basel) 13(19):4882

    Article  Google Scholar 

  29. Rimola J, Davenport MS, Liu PS et al (2019) Diagnostic accuracy of MRI with extracellular vs. hepatobiliary contrast material for detection of residual hepatocellular carcinoma after locoregional treatment. Abdom Radiol (NY) 44:549–558

    Article  Google Scholar 

  30. Watanabe H, Kanematsu M, Goshima S et al (2012) Is gadoxetate disodium-enhanced MRI useful for detecting local recurrence of hepatocellular carcinoma after radiofrequency ablation therapy? AJR Am J Roentgenol 198:589–595

    Article  Google Scholar 

  31. Schelhorn J, Best J, Reinboldt MP et al (2015) Therapy response assessment after radioembolization of patients with hepatocellular carcinoma--comparison of MR imaging with gadolinium ethoxybenzyl diethylenetriamine penta-acetic acid and gadobutrol. J Vasc Interv Radiol 26:972–979

    Article  Google Scholar 

  32. Aslam A, Kamath A, Spieler B et al (2021) Assessing locoregional treatment response to hepatocellular carcinoma: comparison of hepatobiliary contrast agents to extracellular contrast agents. Abdom Radiol (NY) 46:3565–3578

    Article  Google Scholar 

  33. Cruite I, Schroeder M, Merkle EM, Sirlin CB (2010) Gadoxetate disodium-enhanced MRI of the liver: part 2, protocol optimization and lesion appearance in the cirrhotic liver. AJR Am J Roentgenol 195:29–41

    Article  Google Scholar 

  34. Mazzaferro V, Llovet JM, Miceli R et al (2009) Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis. Lancet Oncol 10:35–43

    Article  Google Scholar 

  35. Yao FY, Ferrell L, Bass NM et al (2001) Liver transplantation for hepatocellular carcinoma: expansion of the tumor size limits does not adversely impact survival. Hepatology 33:1394–1403

    Article  CAS  Google Scholar 

  36. Kim AY, Sinn DH, Jeong WK et al (2018) Hepatobiliary MRI as novel selection criteria in liver transplantation for hepatocellular carcinoma. J Hepatol. https://doi.org/10.1016/j.jhep.2018.01.024

  37. Park S, Joo I, Lee DH et al (2020) Diagnostic performance of LI-RADS treatment response algorithm for hepatocellular carcinoma: adding ancillary features to MRI compared with enhancement patterns at CT and MRI. Radiology 296:554–561

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea

    Nieun Seo, Mi-Suk Park, Seung-seob Kim, Yong Eun Chung, **-Young Choi & Myeong-** Kim

  2. Department of Surgery, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea

    Dong ** Joo & Myoung Soo Kim

  3. Department of Biomedical Systems Informatics, Biostatistics Collaboration Unit, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea

    Hye Jung Shin

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  1. Nieun Seo
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  2. Dong ** Joo
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  4. Seung-seob Kim
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Corresponding author

Correspondence to Mi-Suk Park.

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Guarantor

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

Hye Jung Shin, one of our coauthors (from Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine), performed statistical analyses.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Seo, N., Joo, D.J., Park, MS. et al. Optimal imaging criteria and modality to determine Milan criteria for the prediction of post-transplant HCC recurrence after locoregional treatment. Eur Radiol 33, 501–511 (2023). https://doi.org/10.1007/s00330-022-08977-z

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  • DOI: https://doi.org/10.1007/s00330-022-08977-z

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