LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy

Published Online:https://doi.org/10.1148/radiol.2019182135

The Liver Imaging Reporting and Data System, or LI-RADS, 2017 Treatment Response algorithm has high predictive value for the histopathologic viability of hepatocellular carcinoma treated with bland arterial embolization when observations are assessed as showing Viable or Nonviable disease, with moderate interreader association.

Background

In 2017, the Liver Imaging Reporting and Data System (LI-RADS) included an algorithm for the assessment of hepatocellular carcinoma (HCC) treated with local-regional therapy. The aim of the algorithm was to enable standardized evaluation of treatment response to guide subsequent therapy. However, the performance of the algorithm has not yet been validated in the literature.

Purpose

To evaluate the performance of the LI-RADS 2017 Treatment Response algorithm for assessing the histopathologic viability of HCC treated with bland arterial embolization.

Materials and Methods

This retrospective study included patients who underwent bland arterial embolization for HCC between 2006 and 2016 and subsequent liver transplantation. Three radiologists independently assessed all treated lesions by using the CT/MRI LI-RADS 2017 Treatment Response algorithm. Radiology and posttransplant histopathology reports were then compared. Lesions were categorized on the basis of explant pathologic findings as either completely (100%) or incompletely (<100%) necrotic, and performance characteristics and predictive values for the LI-RADS Treatment Response (LR-TR) Viable and Nonviable categories were calculated for each reader. Interreader association was calculated by using the Fleiss κ.

Results

A total of 45 adults (mean age, 57.1 years ± 8.2; 13 women) with 63 total lesions were included. For predicting incomplete histopathologic tumor necrosis, the accuracy of the LR-TR Viable category for the three readers was 60%–65%, and the positive predictive value was 86%–96%. For predicting complete histopathologic tumor necrosis, the accuracy of the LR-TR Nonviable category was 67%–71%, and the negative predictive value was 81%–87%. By consensus, 17 (27%) of 63 lesions were categorized as LR-TR Equivocal, and 12 of these lesions were incompletely necrotic. Interreader association for the LR-TR category was moderate (κ = 0.55; 95% confidence interval: 0.47, 0.67).

Conclusion

The Liver Imaging Reporting and Data System 2017 Treatment Response algorithm had high predictive value and moderate interreader association for the histopathologic viability of hepatocellular carcinoma treated with bland arterial embolization when lesions were assessed as Viable or Nonviable.

© RSNA, 2019

Online supplemental material is available for this article.

See also the editorial by Gervais in this issue.

References

  • 1. Fraum TJ, Tsai R, Rohe E, et al. Differentiation of hepatocellular carcinoma from other hepatic malignancies in patients at risk: diagnostic performance of the liver imaging reporting and data system version 2014. Radiology 2018;286(1):158–172.
  • 2. Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015;136(5):E359–E386.
  • 3. Kulik L, Heimbach JK, Zaiem F, et al. Therapies for patients with hepatocellular carcinoma awaiting liver transplantation: a systematic review and meta-analysis. Hepatology 2018;67(1):381–400.
  • 4. Dhanasekaran R, Khanna V, Kooby DA, et al. The effectiveness of locoregional therapies versus supportive care in maintaining survival within the Milan criteria in patients with hepatocellular carcinoma. J Vasc Interv Radiol 2010;21(8):1197–1204; quiz 204.
  • 5. Gaba RC, Lokken RP, Hickey RM, et al. Quality improvement guidelines for transarterial chemoembolization and embolization of hepatic malignancy. J Vasc Interv Radiol 2017;28(9):1210–1223.e3.
  • 6. Hodavance MS, Vikingstad EM, Griffin AS, et al. Effectiveness of transarterial embolization of hepatocellular carcinoma as a bridge to transplantation. J Vasc Interv Radiol 2016;27(1):39–45.
  • 7. American College of Radiology (ACR). Liver Imaging Reporting and Data System (LI-RADS). ACR website. https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/LI-RADS. Published 2017. Accessed April 3, 2018.
  • 8. Elsayes KM, Hooker JC, Agrons MM, et al. 2017 version of LI-RADS for CT and MR imaging: an update. RadioGraphics 2017;37(7):1994–2017.
  • 9. Kielar A, Fowler KJ, Lewis S, et al. Locoregional therapies for hepatocellular carcinoma and the new LI-RADS treatment response algorithm. Abdom Radiol (NY) 2018;43(1):218–230.
  • 10. Lencioni R, Llovet JM. Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis 2010;30(1):52–60.
  • 11. Kielar AZ, Chernyak V, Bashir MR, et al. LI-RADS 2017: an update. J Magn Reson Imaging 2018;47(6):1459–1474.
  • 12. OPTN policies. policy 9.3.F.vii extensions of HCC exceptions. https://optn.transplant.hrsa.gov/media/1200/optn_policies.pdf. Published June 13, 2018. Accessed August 8, 2018.
  • 13. Kambadakone AR, Fung A, Gupta RT, et al. LI-RADS technical requirements for CT, MRI, and contrast-enhanced ultrasound. Abdom Radiol (NY) 2018;43(1):56–74 [Published correction appears in Abdom Radiol (NY) 2018;43(1):240.] https://doi.org/10.1007/s00261-017-1325-y.
  • 14. Harris PA, Taylor R, Thielke R, et al. Research electronic data capture (REDCap): a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381.
  • 15. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33(1):159–174.
  • 16. Weng Z, Ertle J, Zheng S, et al. Choi criteria are superior in evaluating tumor response in patients treated with transarterial radioembolization for hepatocellular carcinoma. Oncol Lett 2013;6(6):1707–1712.
  • 17. Davenport MS, Khalatbari S, Liu PSC, et al. Repeatability of diagnostic features and scoring systems for hepatocellular carcinoma by using MR imaging. Radiology 2014;272(1):132–142.
  • 18. Bashir MR, Huang R, Mayes N, et al. Concordance of hypervascular liver nodule characterization between the organ procurement and transplant network and liver imaging reporting and data system classifications. J Magn Reson Imaging 2015;42(2):305–314.

Article History

Received: Sept 14 2018
Revision requested: Nov 21 2018
Revision received: Feb 27 2019
Accepted: Mar 14 2019
Published online: Apr 30 2019
Published in print: July 2019