Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI

  • Shi-Ting Feng
  • Yingmei Jia
  • Bing Liao
  • Bingsheng Huang
  • Qian Zhou
  • Xin Li
  • Kaikai Wei
  • Lili Chen
  • Bin Li
  • Wei Wang
  • Shuling Chen
  • Xiaofang He
  • Haibo Wang
  • Sui Peng
  • Ze-Bin Chen
  • Mimi Tang
  • Zhihang Chen
  • Yang Hou
  • Zhenwei PengEmail author
  • Ming KuangEmail author
Magnetic Resonance



Preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular cancer (HCC) is important for surgery strategy making. We aimed to develop and validate a combined intratumoural and peritumoural radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in primary HCC patients.


This study included a training cohort of 110 HCC patients and a validating cohort of 50 HCC patients. All the patients underwent preoperative Gd-EOB-DTPA-enhanced MRI examination and curative hepatectomy. The volumes of interest (VOIs) around the hepatic lesions including intratumoural and peritumoural regions were manually delineated in the hepatobiliary phase of MRI images, from which quantitative features were extracted and analysed. In the training cohort, machine-learning method was applied for dimensionality reduction and selection of the extracted features.


The proportion of MVI-positive patients was 38.2% and 40.0% in the training and validation cohort, respectively. Supervised machine learning selected ten features to establish a predictive model for MVI. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity of the combined intratumoural and peritumoural radiomics model in the training and validation cohort were 0.85 (95% confidence interval (CI), 0.77–0.93), 88.2%, 76.2%, and 0.83 (95% CI, 0.71–0.95), 90.0%, 75.0%, respectively.


We evaluate quantitative Gd-EOB-DTPA-enhanced MRI image features of both intratumoural and peritumoural regions and provide an effective radiomics-based model for the prediction of MVI in HCC patients, and may therefore help clinicians make precise decisions regarding treatment before the surgery.

Key Points

An effective radiomics model for prediction of microvascular invasion in HCC patients is established.

The radiomics model is superior to the radiologist in prediction of MVI.

The radiomics model can help clinicians in pretreatment decision making.


Hepatocellular cancer Radiomics Magnetic resonance imaging Gd-EOB-DTPA 



Three-dimensional volume interpolated breath-hold test




Akaike information criterion


Alanine aminotransferase


Aspartate aminotransferase


Area under receiver operating characteristic curve


Confidence interval


Computed tomography


Fast low angle shot


Fat suppression






Half-Fourier single-shot turbo spin-echo


Hepatobiliary phase


Hepatocellular cancer


Intra-class correlation coefficient




Least absolute shrinkage and selection operator


Magnetic resonance imaging


Microvascular invasion


Negative predictive value


Positive predictive value

Rad score

Radiomics score


Recurrence-free survival


Region of interest


Turbo spin-echo


Volume of interest



This study was funded by the National Natural Science Foundation of China (81571750), National Natural Science Foundation of China (81771908), National Natural Science Foundation of China (81770608), Natural Science Foundation of Guangdong Province (2015A030311039), Guangzhou Science and Technology Program key projects (201704020215), and the Kelin Outstanding Young Scientist of the First Affiliated Hospital of Sun Yet-sen University (2017).

Compliance with ethical standards


The scientific guarantor of this publication is Ming Kuang.

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

Two of the authors (Qian Zhou and Bin Li) have significant statistical expertise.

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_2018_5935_MOESM1_ESM.docx (3.4 mb)
ESM 1 (DOCX 3520 kb)


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Shi-Ting Feng
    • 1
  • Yingmei Jia
    • 1
  • Bing Liao
    • 2
  • Bingsheng Huang
    • 3
  • Qian Zhou
    • 4
  • Xin Li
    • 5
  • Kaikai Wei
    • 1
  • Lili Chen
    • 2
  • Bin Li
    • 4
  • Wei Wang
    • 6
  • Shuling Chen
    • 6
  • Xiaofang He
    • 7
  • Haibo Wang
    • 4
  • Sui Peng
    • 4
    • 8
  • Ze-Bin Chen
    • 9
  • Mimi Tang
    • 8
  • Zhihang Chen
    • 9
  • Yang Hou
    • 10
  • Zhenwei Peng
    • 11
    Email author
  • Ming Kuang
    • 6
    • 9
    Email author
  1. 1.Department of RadiologyThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  2. 2.Department of PathologyThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  3. 3.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science CenterShenzhen UniversityShenzhenChina
  4. 4.Clinical Trials UnitThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  5. 5.GE HealthcareShanghaiChina
  6. 6.Department of Medical Ultrasonics, Division of Interventional UltrasoundThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  7. 7.Department of Radiation OncologyThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  8. 8.Department of Gastroenterology and HepatologyThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  9. 9.Department of Liver SurgeryThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  10. 10.Jinan UniversityGuangzhouChina
  11. 11.Department of OncologyThe First Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina

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