Can IVIM help predict HCC recurrence after hepatectomy?

  • Yao Zhang
  • Sichi Kuang
  • Qungang Shan
  • Dailin Rong
  • Zhongping Zhang
  • Hao Yang
  • Jun Wu
  • Jingbiao Chen
  • Bingjun He
  • Ying Deng
  • Neil Roberts
  • Jun Shen
  • Sudhakar K. Venkatesh
  • Jin WangEmail author



To determine the diagnostic performance of intravoxel incoherent motion (IVIM) parameters to predict tumor recurrence after hepatectomy in patients with hepatitis B virus (HBV)–related hepatocellular carcinoma (HCC).

Materials and methods

One hundred and fifty-seven patients (mean age 52.54 ± 11.32 years, 87% male) with surgically and pathologically confirmed HCC were included. Regions of interests were drawn including the tumors by two independent radiologists. ADC and IVIM-derived parameters (true diffusion coefficient [D]; pseudodiffusion coefficient [D*]; pseudodiffusion fraction [f]) were obtained preoperatively. The Cox proportional hazards model was used to analyze the predictors associated with tumor recurrence after hepatectomy.


Forty-seven of 157 (29.9%) patients experienced tumor recurrence. The multivariate Cox proportional hazards model revealed that a D value < 0.985 × 10−3 mm2/s (hazard ratio (HR), 0.190; p = 0.023) was a risk factor for tumor recurrence. Additional risk factors included younger age (HR, 0.328; p = 0.034) and higher serum alpha-fetoprotein (AFP) level (HR, 2.079; p = 0.013). Further, receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of the obtained Cox regression model improved from 0.68 for the combination of AFP and age alone to 0.724 for the combination of D value, AFP, and age.


The D value derived from the IVIM model is a potential biomarker for the preoperative prediction of recurrence after hepatectomy in patients with HCC. When combined with age and AFP levels, D can improve the predictive performance for tumor recurrence.

Key Points

• The recurrence rate of HCC after hepatectomy was higher in patients with ADC, D, and f values that were lower than the optimal cutoff values.

• The optimal cutoff values of ADC, D, D*, and f for predicting recurrence in HBV associated HCC were 0.858 × 10−3 mm2/s, 0.985 × 10−3 mm2/s, 12.5 × 10−3 mm2/s, and 23.4%, respectively.

• The D value derived from IVIM diffusion-weighted imaging may be a useful biomarker for preoperative prediction of recurrence after hepatectomy in patients with HCC. When combined with age and AFP levels, D can improve the predictive performance for tumor recurrence.


Magnetic resonance imaging (MRI) Diffusion Hepatocellular carcinoma (HCC) Hepatitis B virus (HBV) Recurrence 



Apparent diffusion coefficient




Area under the curve


Confidence interval


True diffusion coefficient


Pseudodiffusion coefficient


Pseudodiffusion fraction


Hepatobiliary phase


Hepatitis B virus


Hepatocellular carcinoma


Hazard ratio


Intra-class correlation coefficient


Intravoxel incoherent motion


Receiver operating characteristic


T2-weighted imaging



The authors state that this study has received funding by National Natural Science Foundation of China grant 81271562 (JW) and Science and Technology Program of Guangzhou, China 201704020016 (JW).

Compliance with ethical standards


The scientific guarantor of this publication is Jin Wang.

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

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2019_6180_MOESM1_ESM.docx (24 kb)
ESM 1 (DOCX 24 kb)


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Yao Zhang
    • 1
  • Sichi Kuang
    • 1
  • Qungang Shan
    • 1
  • Dailin Rong
    • 1
  • Zhongping Zhang
    • 2
  • Hao Yang
    • 1
  • Jun Wu
    • 1
  • Jingbiao Chen
    • 1
  • Bingjun He
    • 1
  • Ying Deng
    • 1
  • Neil Roberts
    • 3
  • Jun Shen
    • 4
  • Sudhakar K. Venkatesh
    • 5
  • Jin Wang
    • 1
    Email author
  1. 1.Department of Radiology, the Third Affiliated HospitalSun Yat-sen University (SYSU)GuangzhouPeople’s Republic of China
  2. 2.Philips Intergrated Solution CenterGuangzhouPeople’s Republic of China
  3. 3.Edinburgh Imaging, School of Clinical SciencesUniversity of EdinburghEdinburghUK
  4. 4.Department of Radiology, Sun Yat-sen Memorial HospitalSun Yat-sen University (SYSU)GuangzhouPeople’s Republic of China
  5. 5.Department of Radiology, Mayo Clinic College of MedicineMayo ClinicRochesterUSA

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