Role of baseline volumetric functional MRI in predicting histopathologic grade and patients’ survival in hepatocellular carcinoma



We aimed to evaluate the role of volumetric ADC (vADC) and volumetric venous enhancement (vVE) in predicting the grade of tumor differentiation in hepatocellular carcinoma (HCC).


The study population included 136 HCC patients (188 lesions) who had baseline MR imaging and histopathological report. Measurements of vVE and vADC were performed on baseline MRI. Tumors were histologically classified into low-grade and high-grade groups. The parameters between the two groups were compared using Mann-Whitney U and chi-square tests for continuous and categorical parameters, respectively. Area under receiver operating characteristic (AUROC) was calculated to investigate the accuracy of vADC and vVE. Logistic regression and multivariable Cox regression were used to unveil the potential parameters associated with high-grade HCC and patient’s survival, respectively.


Lesions with higher vADC values and a higher absolute vADC skewness were more likely to be high grade on histopathology assessment (p = 0.001 and p = 0.0291, respectively). Also, vVE showed a trend to be higher in low-grade lesions (p = 0.079). Adjusted multivariable model including vADC, vVE, and vADC skewness could strongly predict HCC degree of differentiation (AUROC = 83%). Additionally, a higher Child-Pugh score (HR = 2.39 [p = 0.02] for score 2 and HR = 3.47 [p = 0.001] for score 3), vADC skewness (HR = 1.52, p = 0.02; per increments in skewness), and tumor volume (HR = 1.1, p = 0.001; per 100 cm3 increments) showed the highest association with patients’ survival.


vADC and vVE have the potential to accurately predict HCC differentiation. Additionally, some imaging features in combination with patients’ clinical characteristics can predict patient survival.

Key Points

• Volumetric functional MRI metrics can be considered as non-invasive measures for determining tumor histopathology in HCC.

• Estimating patient survival based on clinical and imaging parameters can be used for modifying management approach and preventing unnecessary adverse events.

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Apparent diffusion coefficient


Area under the curve


Digital Imaging and Communications in Medicine


Diffusion-weighted imaging


Hepatocellular carcinoma


Health Insurance Portability and Accountability Act


Intraclass correlation coefficient


Magnetic resonance imaging


Portal venous phase


Receiver operating characteristic


Region of interest


Echo time


Repetition time


Volumetric apparent diffusion coefficient


Volumetric venous enhancement


  1. 1.

    Jonas S, Bechstein WO, Steinmüller T et al (2001) Vascular invasion and histopathologic grading determine outcome after liver transplantation for hepatocellular carcinoma in cirrhosis. Hepatology 33(5):1080–1086

    CAS  Article  Google Scholar 

  2. 2.

    Tung-Ping Poon R, Fan ST, Wong J (2000) Risk factors, prevention, and management of postoperative recurrence after resection of hepatocellular carcinoma. Ann Surg 232:10–24

    CAS  Article  Google Scholar 

  3. 3.

    Edmondson HA, Steiner PE (1954) Primary carcinoma of the liver. A study of 100 cases among 48,900 necropsies. Cancer 7(3):462–503

    CAS  Article  Google Scholar 

  4. 4.

    Kim BK, Han KH, Park YN et al (2008) Prediction of microvascular invasion before curative resection of hepatocellular carcinoma. J Surg Oncol 97:246–252

    Article  Google Scholar 

  5. 5.

    Pérez-Saborido B, de los Galanes SJ, Menéu-Díaz JC et al (2007) Tumor recurrence after liver transplantation for hepatocellular carcinoma: recurrence pathway and prognostic factors. Transplant Proc 39(7):2304–2307

    Article  Google Scholar 

  6. 6.

    Mori Y, Tamai H, Shingaki N et al (2016) Hypointense hepatocellular carcinomas on apparent diffusion coefficient mapping: pathological features and metastatic recurrence after hepatectomy. Hepatol Res 46(7):634–641

    CAS  Article  Google Scholar 

  7. 7.

    Nasu K, Kuroki Y, Tsukamoto T, Nakajima H, Mori K, Minami M (2009) Diffusion-weighted imaging of surgically resected hepatocellular carcinoma: imaging characteristics and relationship among signal intensity, apparent diffusion coefficient, and histopathologic grade. AJR Am J Roentgenol 193(2):438–444

  8. 8.

    Muhi A, Ichikawa T, Motosugi U et al (2009) High-b-value diffusion-weighted MR imaging of hepatocellular lesions: estimation of grade of malignancy of hepatocellular carcinoma. J Magn Reson Imaging 30(5):1005–1011

    Article  Google Scholar 

  9. 9.

    Nakanishi M, Chuma M, Hige S et al (2012) Relationship between diffusion-weighted magnetic resonance imaging and histological tumor grading of hepatocellular carcinoma. Ann Surg Oncol 19(4):1302–1309

    Article  Google Scholar 

  10. 10.

    Woo S, Lee JM, Yoon JH Joo I, Han JK, Choi BI (2014) Intravoxel incoherent motion diffusion-weighted MR imaging of hepatocellular carcinoma: correlation with enhancement degree and histologic grade. Radiology 270(3):758–767

  11. 11.

    Corona-Villalobos CP, Halappa VG, Geschwind JF et al (2015) Volumetric assessment of tumour response using functional MR imaging in patients with hepatocellular carcinoma treated with a combination of doxorubicin-eluting beads and sorafenib. Eur Radiol 25(2):380–390

    Article  Google Scholar 

  12. 12.

    Saito K, Kotake F, Ito N et al (2005) Gd-EOB-DTPA enhanced MRI for hepatocellular carcinoma: quantitative evaluation of tumor enhancement in hepatobiliary phase. Magn Reson Med Sci 4(1):1–9

    Article  Google Scholar 

  13. 13.

    Kitao A, Zen Y, Matsui O et al (2010) Hepatocellular carcinoma: signal intensity at gadoxetic acid-enhanced MR imaging-correlation with molecular transporters and histopathologic features. Radiology 256(3):817–826

    Article  Google Scholar 

  14. 14.

    Kwon HJ, Byun JH, Kim JY et al (2015) Differentiation of small (≤2 cm) hepatocellular carcinomas from small benign nodules in cirrhotic liver on gadoxetic acid-enhanced and diffusion-weighted magnetic resonance images. Abdom Imaging 40(1):64–75

    Article  Google Scholar 

  15. 15.

    Chang WC, Chen RC, Te Chou C et al (2014) Histological grade of hepatocellular carcinoma correlates with arterial enhancement on gadoxetic acid-enhanced and diffusion-weighted MR images. Abdom Imaging 39(6):1202–1212

    Article  Google Scholar 

  16. 16.

    Choi JY, Kim MJ, Park YN et al (2011) Gadoxetate disodium-enhanced hepatobiliary phase MRI of hepatocellular carcinoma: correlation with histological characteristics. AJR Am J Roentgenol 197(2):399–405

    Article  Google Scholar 

  17. 17.

    Corona-Villalobos CP, Halappa VG, Bonekamp S et al (2015) Functional magnetic resonance imaging response of targeted tumor burden and its impact on survival in patients with hepatocellular carcinoma. Investig Radiol 50(4):283–289

    Article  Google Scholar 

  18. 18.

    Pandey A, Pandey P, Ghasabeh MA et al (2018) Baseline volumetric multiparametric MRI: can it be used to predict survival in patients with unresectable intrahepatic cholangiocarcinoma undergoing transcatheter arterial chemoembolization? Radiology 289(3):843–853

    Article  Google Scholar 

  19. 19.

    Zarghampour M, Fouladi DF, Pandey A et al (2018) Utility of volumetric contrast-enhanced and diffusion-weighted MRI in differentiating between common primary hypervascular liver tumors. J Magn Reson Imaging 48(4):1080–1090

    Article  Google Scholar 

  20. 20.

    Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(11):1768–1783

    Article  Google Scholar 

  21. 21.

    Lewis HL, Ghasabeh MA, Khoshpouri P, Kamel IR, Pawlik TM (2017) Functional hepatic imaging as a biomarker of primary and secondary tumor response to loco-regional therapies. Surg Oncol 26(4):411–422

  22. 22.

    Chan JH, Tsui EY, Luk SH et al (2001) Diffusion-weighted MR imaging of the liver: distinguishing hepatic abscess from cystic or necrotic tumor. Abdom Imaging 26(2):161–165

    CAS  Article  Google Scholar 

  23. 23.

    Asayama Y, Yoshimitsu K, Nishihara Y et al (2008) Arterial blood supply of hepatocellular carcinoma and histologic grading: radiologic-pathologic correlation. AJR Am J Roentgenol 190(1):W28–W34

    Article  Google Scholar 

  24. 24.

    The International Consensus Group for Hepatocellular Neoplasia (2009) Pathologic diagnosis of early hepatocellular carcinoma: a report of the International Consensus Group for Hepatocellular Neoplasia. Hepatology 49(2):658–664

  25. 25.

    Poyraz AK, Onur MR, Kocakoç E, Oğur E (2012) Diffusion-weighted MRI of fatty liver. J Magn Reson Imaging 35(5):1108–1111

  26. 26.

    Xu H, Li X, Xie JX, Yang ZH, Wang B (2007) Diffusion-weighted magnetic resonance imaging of focal hepatic nodules in an experimental hepatocellular carcinoma rat model. Acad Radiol 14(3):279–286

  27. 27.

    Vandecaveye V, De Keyzer F, Verslype C et al (2009) Diffusion-weighted MRI provides additional value to conventional dynamic contrast-enhanced MRI for detection of hepatocellular carcinoma. Eur Radiol 19(10):2456–2466

    Article  Google Scholar 

  28. 28.

    Chen J, Wu M, Liu R, Li S, Gao R, Song B (2015) Preoperative evaluation of the histological grade of hepatocellular carcinoma with diffusion-weighted imaging: a meta-analysis. PLoS One 10(2):e0117661

  29. 29.

    Jiang T, Xu JH, Zou Y et al (2017) Diffusion-weighted imaging (DWI) of hepatocellular carcinomas: a retrospective analysis of the correlation between qualitative and quantitative DWI and tumour grade. Clin Radiol 72(6):465–472

    CAS  Article  Google Scholar 

  30. 30.

    Bonekamp D, Bonekamp S, Halappa VG et al (2014) Interobserver agreement of semi-automated and manual measurements of functional MRI metrics of treatment response in hepatocellular carcinoma. Eur J Radiol 83(3):487–496

    Article  Google Scholar 

  31. 31.

    Najmi Varzaneh F, Pandey A, Aliyari Ghasabeh M et al (2018) Prediction of post-TACE necrosis of hepatocellular carcinoma usingvolumetric enhancement on MRI and volumetric oil deposition on CT, with pathological correlation. Eur Radiol 28(7):3032–3040

    Article  Google Scholar 

  32. 32.

    Mitchell DG, Burk DL Jr, Vinitski S, Rifkin MD (1987) The biophysical basis of tissue contrast in extracranial MR imaging. AJR Am J Roentgenol 149(4):831–837

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Correspondence to Ihab R. Kamel.

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The scientific guarantor of this publication is Dr. Ihab R. Kamel.

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Ameli, S., Shaghaghi, M., Aliyari Ghasabeh, M. et al. Role of baseline volumetric functional MRI in predicting histopathologic grade and patients’ survival in hepatocellular carcinoma. Eur Radiol 30, 3748–3758 (2020).

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  • Hepatocellular carcinoma
  • Magnetic resonance imaging
  • Survival analysis