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The value of quantitative MR elastography-based stiffness for assessing the microvascular invasion grade in hepatocellular carcinoma

  • Magnetic Resonance
  • Published:
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Abstract

Objectives

To evaluate the potential diagnostic value of MR elastography (MRE)–based stiffness to noninvasively predict the microvascular invasion (MVI) grade in hepatocellular carcinoma (HCC).

Methods

One hundred eighty-five patients with histopathology-proven HCC who underwent MRI and MRE examinations before hepatectomy were retrospectively enrolled. According to the three-tiered MVI grading system, the MVI was divided into negative-MVI (n = 89) and positive-MVI (n = 96) groups, and the latter group was categorized into mild-MVI (n = 49) and severe-MVI (n = 47) subgroups. Logistic regression and area under the receiver operating characteristic curve (AUC) analyses were used to determine the predictors associated with MVI grade and analyze their performances, respectively.

Results

Among the 185 patients, tumor size ≥ 50 mm (p = 0.031), tumor stiffness (TS)/liver stiffness (LS) > 1.47 (p = 0.001), TS > 4.33 kPa (p < 0.001), and nonsmooth tumor margin (p = 0.006) were significant independent predictors for positive-MVI. Further analyzing the subgroups, tumor size ≥ 50 mm (p < 0.001), TS > 5.35 kPa (p = 0.001), and AFP level > 400 ng/mL (p = 0.044) were independently associated with severe-MVI. The models incorporating MRE and clinical-radiological features together performed better for evaluating positive-MVI (AUC: 0.846) and severe-MVI (AUC: 0.802) than the models using clinical-radiological predictors alone (AUC: positive-/severe-MVI, 0.737/0.743). Analysis of recurrence-free survival and overall survival showed the predicted positive-MVI/severe-MVI groups based on combined models had significantly poorer prognoses than predicted negative-MVI/mild-MVI groups, respectively (all p < 0.05).

Conclusions

MRE-based stiffness was an independent predictor for both the positive-MVI and severe-MVI. The combination of MRE and clinical-radiological models might be a useful tool for evaluating HCC patients’ prognoses underwent hepatectomy by preoperatively predicting the MVI grade.

Key Points

• The severe-microvascular invasion (MVI) grade had the highest tumor stiffness (TS), followed by mild-MVI and non-MVI, and there were significances among the three different MVI grades.

• MR elastography (MRE)–based stiffness value was an independent predictor of positive-MVI and severe-MVI in hepatocellular carcinoma (HCC) preoperatively.

• When combined with clinical-radiological models, MRE could significantly improve the predictive performance for MVI grade. Patients with predicted positive-MVI/severe-MVI based on the combined models had worse recurrence-free survival and overall survival than those with negative-MVI/mild-MVI, respectively.

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Abbreviations

ADC:

Apparent diffusion coefficient

HCC:

Hepatocellular carcinoma

LI-RADS:

Liver Imaging Reporting and Data System

LS:

Liver stiffness

MRE:

Magnetic resonance elastography

MVI:

Microvascular invasion

OS:

Overall survival

RFS:

Recurrence-free survival

ROI:

Region of interest

TS:

Tumor stiffness

References

  1. Llovet JM, Kelley RK, Villanueva A et al (2021) Hepatocellular carcinoma. Nat Rev Dis Primers 7:6

    Article  PubMed  Google Scholar 

  2. Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249

    Article  PubMed  Google Scholar 

  3. Bruix J, Gores GJ, Mazzaferro V (2014) Hepatocellular carcinoma: clinical frontiers and perspectives. Gut 63:844–855

    Article  CAS  PubMed  Google Scholar 

  4. Erstad DJ, Tanabe KK (2019) Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma. Ann Surg Oncol 26:1474–1493

    Article  PubMed  Google Scholar 

  5. An C, Kim DW, Park YN, Chung YE, Rhee H, Kim MJ (2015) Single hepatocellular carcinoma: preoperative MR imaging to predict early recurrence after curative resection. Radiology 276:433–443

    Article  PubMed  Google Scholar 

  6. Cong WM, Bu H, Chen J et al (2016) Practice guidelines for the pathological diagnosis of primary liver cancer: 2015 update. World J Gastroenterol 22:9279–9287

    Article  PubMed  PubMed Central  Google Scholar 

  7. Iguchi T, Shirabe K, Aishima S et al (2015) New pathologic stratification of microvascular invasion in hepatocellular carcinoma: predicting prognosis after living-donor liver transplantation. Transplantation 99:1236–1242

    Article  CAS  PubMed  Google Scholar 

  8. Sumie S, Nakashima O, Okuda K et al (2014) The significance of classifying microvascular invasion in patients with hepatocellular carcinoma. Ann Surg Oncol 21:1002–1009

    Article  PubMed  Google Scholar 

  9. Wang H, Du PC, Wu MC, Cong WM (2018) Postoperative adjuvant transarterial chemoembolization for multinodular hepatocellular carcinoma within the Barcelona Clinic Liver Cancer early stage and microvascular invasion. Hepatobiliary Surg Nutr 7:418–428

    Article  PubMed  PubMed Central  Google Scholar 

  10. Chong HH, Yang L, Sheng RF et al (2021) Multi-scale and multi-parametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma </= 5 cm. Eur Radiol 31:4824–4838

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chen J, Zhou J, Kuang S et al (2019) Liver imaging reporting and data system category 5: MRI predictors of microvascular invasion and recurrence after hepatectomy for hepatocellular carcinoma. AJR Am J Roentgenol 213:821–830

    Article  PubMed  Google Scholar 

  12. Wang WT, Yang L, Yang ZX et al (2018) Assessment of microvascular invasion of hepatocellular carcinoma with diffusion kurtosis imaging. Radiology 286:571–580

    Article  PubMed  Google Scholar 

  13. Renzulli M, Brocchi S, Cucchetti A et al (2016) Can current preoperative imaging be used to detect microvascular invasion of hepatocellular carcinoma? Radiology 279:432–442

    Article  PubMed  Google Scholar 

  14. Lei Z, Li J, Wu D et al (2016) Nomogram for preoperative estimation of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma within the Milan criteria. JAMA Surg 151:356–363

    Article  PubMed  Google Scholar 

  15. Min JH, Lee MW, Park HS et al (2020) Interobserver variability and diagnostic performance of gadoxetic acid-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma. Radiology 297:573–581

    Article  PubMed  Google Scholar 

  16. Idilman IS, Li J, Yin M, Venkatesh SK (2020) MR elastography of liver: current status and future perspectives. Abdom Radiol (NY) 45:3444–3462

    Article  PubMed  Google Scholar 

  17. Garteiser P, Doblas S, Daire JL et al (2012) MR elastography of liver tumours: value of viscoelastic properties for tumour characterisation. Eur Radiol 22:2169–2177

    Article  PubMed  Google Scholar 

  18. Park SJ, Yoon JH, Lee DH, Lim WH, Lee JM (2021) Tumor stiffness measurements on MR elastography for single nodular hepatocellular carcinomas can predict tumor recurrence after hepatic resection. J Magn Reson Imaging 53:587–596

    Article  PubMed  Google Scholar 

  19. Wang J, Shan Q, Liu Y et al (2019) 3D MR elastography of hepatocellular carcinomas as a potential biomarker for predicting tumor recurrence. J Magn Reson Imaging 49:719–730

    Article  PubMed  Google Scholar 

  20. Zhang L, Chen J, Jiang H et al (2022) MR elastography as a biomarker for prediction of early and late recurrence in HBV-related hepatocellular carcinoma patients before hepatectomy. Eur J Radiol 152:110340

    Article  PubMed  Google Scholar 

  21. Qayyum A, Hwang KP, Stafford J et al (2019) Immunotherapy response evaluation with magnetic resonance elastography (MRE) in advanced HCC. J Immunother Cancer 7:329

    Article  PubMed  PubMed Central  Google Scholar 

  22. Li M, Yin Z, Hu B et al (2022) MR elastography-based shear strain mapping for assessment of microvascular invasion in hepatocellular carcinoma. Eur Radiol 32:5024–5032

    Article  CAS  PubMed  Google Scholar 

  23. Lee S, Kim SH, Lee JE, Sinn DH, Park CK (2017) Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma. J Hepatol 67:526–534

    Article  CAS  PubMed  Google Scholar 

  24. Rhee H, An C, Kim HY, Yoo JE, Park YN, Kim MJ (2019) Hepatocellular carcinoma with irregular Rim-like arterial phase hyperenhancement: more aggressive pathologic features. Liver Cancer 8:24–40

    Article  CAS  PubMed  Google Scholar 

  25. Yang L, Gu D, Wei J et al (2019) A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer 8:373–386

    Article  CAS  PubMed  Google Scholar 

  26. Hu HT, Shen SL, Wang Z et al (2018) Peritumoral tissue on preoperative imaging reveals microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol (NY) 43:3324–3330

    Article  PubMed  Google Scholar 

  27. Chernyak V, Fowler KJ, Kamaya A et al (2018) Liver imaging reporting and data system (LI-RADS) version 2018: imaging of hepatocellular carcinoma in at-risk patients. Radiology 289:816–830

    Article  PubMed  Google Scholar 

  28. 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  PubMed  Google Scholar 

  29. Bedossa P, Poynard T (1996) An algorithm for the grading of activity in chronic hepatitis C. The METAVIR Cooperative Study Group. Hepatology 24:289–293

    Article  CAS  PubMed  Google Scholar 

  30. Sheng X, Ji Y, Ren GP et al (2020) A standardized pathological proposal for evaluating microvascular invasion of hepatocellular carcinoma: a multicenter study by LCPGC. Hepatol Int 14:1034–1047

    Article  PubMed  Google Scholar 

  31. Pepin KM, Ehman RL, McGee KP (2015) Magnetic resonance elastography (MRE) in cancer: technique, analysis, and applications. Prog Nucl Magn Reson Spectrosc 90-91:32–48

    Article  CAS  PubMed  Google Scholar 

  32. Heldin CH, Rubin K, Pietras K, Ostman A (2004) High interstitial fluid pressure - an obstacle in cancer therapy. Nat Rev Cancer 4:806–813

    Article  CAS  PubMed  Google Scholar 

  33. Nia HT, Munn LL, Jain RK (2020) Physical traits of cancer. Science 370

  34. Swartz MA, Lund AW (2012) Lymphatic and interstitial flow in the tumour microenvironment: linking mechanobiology with immunity. Nat Rev Cancer 12:210–219

    Article  CAS  PubMed  Google Scholar 

  35. Yang ZF, Poon RT (2008) Vascular changes in hepatocellular carcinoma. Anat Rec (Hoboken) 291:721–734

    Article  CAS  PubMed  Google Scholar 

  36. Schlichtemeier SM, Pang TC, Williams NE et al (2016) A pre-operative clinical model to predict microvascular invasion and long-term outcome after resection of hepatocellular cancer: the Australian experience. Eur J Surg Oncol 42:1576–1583

    Article  CAS  PubMed  Google Scholar 

  37. Xu X, Zhang HL, Liu QP et al (2019) Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol 70:1133–1144

    Article  PubMed  Google Scholar 

  38. Wu Y, Zhu M, Liu Y, Cao X, Zhang G, Yin L (2022) Peritumoral imaging manifestations on Gd-EOB-DTPA-enhanced MRI for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Front Oncol 12:907076

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hong SB, Choi SH, Kim SY et al (2021) MRI features for predicting microvascular invasion of hepatocellular carcinoma: a systematic review and meta-analysis. Liver Cancer 10:94–106

    Article  PubMed  PubMed Central  Google Scholar 

  40. Kennedy P, Lewis S, Bane O et al (2021) Early effect of (90)Y radioembolisation on hepatocellular carcinoma and liver parenchyma stiffness measured with MR elastography: initial experience. Eur Radiol 31:5791–5801

    Article  PubMed  Google Scholar 

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Funding

The authors state that this study has received funding by National Natural Science Foundation of China grant number 91959118 (JW) 82271973 (JW) and 82101994 (LqZhang), Key Research and Development Program of Guangdong Province 2019B020235002 (JW), Guangdong Basic and Applied Basic Research Foundation, 2021A1515010582 (JW), SKY Radiology Department International Medical Research Foundation of China Z-2014-07-2101 (JW), and Clinical Research Foundation of the 3rd Affiliated Hospital of Sun Yat-sen University YHJH201901 (JW).

Author information

Author notes

  1. Lina Zhang and Mengsi Li contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No. 600, Tianhe Road, Guangzhou, Guangdong, 510630, People’s Republic of China

    Lina Zhang, Mengsi Li, Jie Zhu, Yao Zhang, Yuanqiang Xiao, Mengshi Dong, Linqi Zhang & Jin Wang

  2. Department of Nuclear Medicine, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 78 Hengzhigang Rd, Guangzhou, Guangdong, 510095, People’s Republic of China

    Linqi Zhang

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  1. Lina Zhang
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Correspondence to Jin Wang.

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Zhang, L., Li, M., Zhu, J. et al. The value of quantitative MR elastography-based stiffness for assessing the microvascular invasion grade in hepatocellular carcinoma. Eur Radiol 33, 4103–4114 (2023). https://doi.org/10.1007/s00330-022-09290-5

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