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LI-RADS version 2018 treatment response algorithm on extracellular contrast-enhanced MRI in patients treated with transarterial chemoembolization for hepatocellular carcinoma: diagnostic performance and the added value of ancillary features

  • Hepatobiliary
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Abstract

Background

The Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Algorithm (TRA) (LI-RADS TRA) is used for assessing response of HCC to locoregional therapy (LRT), however, the value of ancillary features (AFs) for TACE-treated HCCs has not been extensively investigated on extracellular agent MRI (ECA-MRI).

Purpose

To evaluate the diagnostic performance of LI-RADS v2018 TRA on ECA-MRI for HCC treated with transarterial chemoembolization (TACE) and the value of ancillary features.

Methods

This retrospective study included patients who underwent TACE for HCC and then followed by hepatic surgery between January 2019 and June 2023 with both pre- and post-TACE contrast-enhanced MRI available. Two radiologists independently evaluated the post-treated lesions on MRI using LI-RADS treatment response (TR) (LR-TR) algorithm and modified LR-TR (mLR-TR) algorithm in which ancillary features (restricted diffusion and intermediate T2-weighted hyperintensity) were added, respectively. Lesions were categorized as complete pathologic necrosis (100%, CPN) and non-complete pathologic necrosis (< 100%, non-CPN) on the basis of surgical pathology. The diagnostic performance in predicting viable and non-viable tumors based on LR-TR and mLR-TR algorithms was compared using the McNemar test. Interreader agreement was calculated by using Cohen’s weighted and unweighted κ.

Results

A total of 61 patients [mean age 59 years ± 10 (standard deviation); 47 men] with 79 lesions (57 pathologically viable) were included. For non-CPN prediction, the sensitivity, specificity of LR-TR viable and mLR-TR viable category were 75% (43 of 57), 82% (18 of 22) and 88% (50 of 57), 77% (17 of 22), respectively, the sensitivity of mLR-TR was significantly higher than that of LR-TR (P = 0.016) without difference in specificity (P = 1.000). Interreader agreement for LR-TR and mLR-TR category was moderate (k = 0.50, 95% confidence interval 0.33, 0.67, k = 0.42, 95% confidence interval 0.20, 0.63). The sensitivity of both LR-TR and mLR-TR algorithms in predicting viable tumors between conventional TACE (cTACE) and drug-eluting beads TACE (DEB-TACE) did not have significant difference (cTACE: 76%, 89% vs. DEB-TACE: 73%, 82%).

Conclusions

On ECA-MRI, applying ancillary features to LI-RADS v2018 TRA can improve the sensitivity in predicting pathologic tumor viability in patients treated with TACE for hepatocellular carcinoma with no significant difference in specificity.

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Abbreviations

AF:

Ancillary feature

APHE:

Arterial phase hyperenhancement

cTACE:

Conventional TACE

CPN:

Complete pathologic necrosis

DEB-TACE:

Drug-eluting beads TACE

ECA:

Extracellular contrast agent

HCC:

Hepatocellular carcinoma

LI-RADS:

Liver Imaging Reporting and Data System

LRT:

Locoregional therapy

LR-TR:

LI-RADS treatment response

NMLIT:

Nodular, masslike, or irregular thick tissue in or along the treated lesions

TACE:

Transarterial chemoembolization

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209–249. https://doi.org/10.3322/caac.21660

  2. Kielar, A., Fowler, K. J., Lewis, S., Yaghmai, V., Miller, F. H., Yarmohammadi, H., Kim, C., Chernyak, V., Yokoo, T., Meyer, J., Newton, I., & Do, R. K. (2018). Locoregional therapies for hepatocellular carcinoma and the new LI-RADS treatment response algorithm. Abdominal Radiology (New York), 43(1), 218–230. https://doi.org/https://doi.org/10.1007/s00261-017-1281-6

    Article  PubMed  Google Scholar 

  3. Reig, M., Forner, A., Rimola, J., Ferrer-Fàbrega, J., Burrel, M., Garcia-Criado, Á., Kelley, R. K., Galle, P. R., Mazzaferro, V., Salem, R., Sangro, B., Singal, A. G., Vogel, A., Fuster, J., Ayuso, C., & Bruix, J. (2022). BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update. Journal of Hepatology, 76(3), 681–693. https://doi.org/https://doi.org/10.1016/j.jhep.2021.11.018

    Article  PubMed  Google Scholar 

  4. Chernyak V, Sirlin CB (eds) (2018) The LI-RADS® v2018 Manual. American College of Radiology Committee on LI-RADS®. American College of Radiology, Virginia. https://www.acr.org/-/media/ACR/Files/Clinical-Resources/LIRADS/LI-RADS-2018-Manual-5Dec18.pdf

  5. Shropshire, E. L., Chaudhry, M., Miller, C. M., Allen, B. C., Bozdogan, E., Cardona, D. M., King, L. Y., Janas, G. L., Do, R. K., Kim, C. Y., Ronald, J., & Bashir, M. R. (2019). LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy. Radiology, 292(1), 226–234. https://doi.org/https://doi.org/10.1148/radiol.2019182135

    Article  PubMed  Google Scholar 

  6. Mendiratta-Lala, M., Aslam, A., Maturen, K. E., Westerhoff, M., Maurino, C., Parikh, N. D., Sun, Y., Sonnenday, C. J., Stein, E. B., Shampain, K. L., Kaza, R. K., Cuneo, K., Masch, W., Do, R. K. G., Lawrence, T. S., & Owen, D. (2022). LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy with Radiologic–Pathologic Explant Correlation in Patients with SBRT-Treated Hepatocellular Carcinoma. International Journal of Radiation Oncology, Biology, Physics, 112(3), 704–714. https://doi.org/https://doi.org/10.1016/j.ijrobp.2021.10.006

    Article  PubMed  Google Scholar 

  7. Chaudhry, M., McGinty, K. A., Mervak, B., Lerebours, R., Li, C., Shropshire, E., Ronald, J., Commander, L., Hertel, J., Luo, S., Bashir, M. R., & Burke, L. M. B. (2020). The LI-RADS Version 2018 MRI Treatment Response Algorithm: Evaluation of Ablated Hepatocellular Carcinoma. Radiology, 294(2), 320–326. https://doi.org/https://doi.org/10.1148/radiol.2019191581

    Article  PubMed  Google Scholar 

  8. Park, S., Joo, I., Lee, D. H., Bae, J. S., Yoo, J., Kim, S. W., & Lee, J. M. (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(3), 554–561. https://doi.org/https://doi.org/10.1148/radiol.2020192797

    Article  PubMed  Google Scholar 

  9. Kim, S. W., Joo, I., Kim, H. C., Ahn, S. J., Kang, H. J., Jeon, S. K., & Lee, J. M. (2020). LI-RADS treatment response categorization on gadoxetic acid-enhanced MRI: diagnostic performance compared to mRECIST and added value of ancillary features. European Radiology, 30(5), 2861–2870. https://doi.org/https://doi.org/10.1007/s00330-019-06623-9

    Article  CAS  PubMed  Google Scholar 

  10. Kim, Y. Y., Kim, M. J., Yoon, J. K., Shin, J., & Roh, Y. H. (2022). Incorporation of Ancillary MRI Features into the LI-RADS Treatment Response Algorithm: Impact on Diagnostic Performance After Locoregional Treatment of Hepatocellular Carcinoma. American Journal of Roentgenology, 218(3), 484–493. https://doi.org/https://doi.org/10.2214/AJR.21.26677

    Article  PubMed  Google Scholar 

  11. Kierans, A. S., Najjar, M., Dutruel, S. P., Gavlin, A., Chen, C., Lee, M. J., Askin, G., & Halazun, K. J. (2021). Evaluation of the LI-RADS treatment response algorithm in hepatocellular carcinoma after trans-arterial chemoembolization. Clinical Imaging, 80, 117–122. https://doi.org/https://doi.org/10.1016/j.clinimag.2021.06.009

    Article  PubMed  Google Scholar 

  12. Motosugi, U., Bannas, P., Bookwalter, C. A., Sano, K., & Reeder, S. B. (2016). An Investigation of Transient Severe Motion Related to Gadoxetic Acid-Enhanced MR Imaging. Radiology, 279(1), 93–102. https://doi.org/https://doi.org/10.1148/radiol.2015150642

    Article  PubMed  Google Scholar 

  13. Huh, Y. J., Kim, D. H., Kim, B., Choi, J. I., & Rha, S. E. (2021). Per-Feature Accuracy of Liver Imaging Reporting and Data System Locoregional Treatment Response Algorithm: A Systematic Review and Meta-Analysis. Cancers, 13(17), 4432. https://doi.org/https://doi.org/10.3390/cancers13174432

    Article  PubMed  PubMed Central  Google Scholar 

  14. Bae, J. S., Lee, J. M., Yoon, J. H., Kang, H. J., Jeon, S. K., Joo, I., Lee, K. B., & Kim, H. (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(2), 336–345. https://doi.org/https://doi.org/10.1148/radiol.2021203537

    Article  PubMed  Google Scholar 

  15. Polikoff, A., Wessner, C. E., Balasubramanya, R., Dulka, S., Liu, J. B., Machado, P., Savsani, E., Lyshchik, A., Shaw, C. M., & Eisenbrey, J. R. (2022). Imaging appearance of residual HCC following incomplete trans-arterial chemoembolization on contrast-enhanced imaging. Abdominal Radiology (New York), 47(1), 152–160. https://doi.org/https://doi.org/10.1007/s00261-021-03298-z

    Article  PubMed  Google Scholar 

  16. Seo, N., Kim, M. S., Park, M. S., Choi, J. Y., Do, R. K. G., Han, K., & Kim, M. J. (2020). Evaluation of treatment response in hepatocellular carcinoma in the explanted liver with Liver Imaging Reporting and Data System version 2017. European Radiology, 30(1), 261–271. https://doi.org/https://doi.org/10.1007/s00330-019-06376-5

    Article  PubMed  Google Scholar 

  17. Kim TH, Woo S, Joo I, Bashir MR, Park MS, Burke LMB, Mendiratta-Lala M, Do RKG. LI-RADS treatment response algorithm for detecting incomplete necrosis in hepatocellular carcinoma after locoregional treatment: a systematic review and meta-analysis using individual patient data. Abdom Radiol (NY). 2021 Aug;46(8):3717–3728. doi: https://doi.org/10.1007/s00261-021-03122-8

  18. Voizard, N., Cerny, M., Assad, A., Billiard, J. S., Olivié, D., Perreault, P., Kielar, A., Do, R. K. G., Yokoo, T., Sirlin, C. B., & Tang, A. (2019). Assessment of hepatocellular carcinoma treatment response with LI-RADS: a pictorial review. Insights into Imaging, 10(1), 121. https://doi.org/https://doi.org/10.1186/s13244-019-0801-z

    Article  PubMed  PubMed Central  Google Scholar 

  19. Golfieri, R., Giampalma, E., Renzulli, M., Cioni, R., Bargellini, I., Bartolozzi, C., Breatta, A. D., Gandini, G., Nani, R., Gasparini, D., Cucchetti, A., Bolondi, L., Trevisani, F., & Precision Italia Study Group (2014). Randomised controlled trial of doxorubicin-eluting beads vs conventional chemoembolisation for hepatocellular carcinoma. British Journal of Cancer, 111(2), 255–264. https://doi.org/10.1038/bjc.2014.199

  20. Kwan, S. W., Fidelman, N., Ma, E., Kerlan, R. K., Jr, & Yao, F. Y. (2012). Imaging predictors of the response to transarterial chemoembolization in patients with hepatocellular carcinoma: a radiological–pathological correlation. Liver Transplantation: Official Publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society, 18(6), 727–736. https://doi.org/https://doi.org/10.1002/lt.23413

    Article  PubMed  Google Scholar 

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Funding

This study was funded by Shanghai 2022 “Science and Technology Innovation Action Plan” Medical Innovation Research Special Project (Grant Number 22Y11910900), and Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-074C).

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Authors and Affiliations

  1. Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, No. 83 Jintang Road, Hedong District, Tianjin, 300170, China

    Di Wang, Rong Lyu & Kefeng Jia

  2. Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China

    Di Wang, Yang Zhang & Peng-Ju Xu

  3. Department of Radiology, Dongying People’s Hospital Shandong, Dongying, China

    Yang Zhang

  4. Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China

    Peng-Ju Xu

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  1. Di Wang
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Correspondence to Peng-Ju Xu.

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Ethical approval

Ethical approval was waived by the Local Ethics Committee of Zhongshan Hospital Fudan University in view of the retrospective nature of the study and all the procedures being performed were part of the routine care.

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The Institutional Review Board approved this study and waived informed consent because of retrospective study.

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Wang, D., Zhang, Y., Lyu, R. et al. LI-RADS version 2018 treatment response algorithm on extracellular contrast-enhanced MRI in patients treated with transarterial chemoembolization for hepatocellular carcinoma: diagnostic performance and the added value of ancillary features. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04275-y

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