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

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusions

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.

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Abbreviations

3D VIBE:

Three-dimensional volume interpolated breath-hold test

AFP:

Alpha-fetoprotein

AIC:

Akaike information criterion

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

AUC:

Area under receiver operating characteristic curve

CI:

Confidence interval

CT:

Computed tomography

FLASH:

Fast low angle shot

FS:

Fat suppression

Gd-EOB-DTPA:

Gadolinium-ethoxybenzyl-diethylenetriamine

GGT:

Gamma-glutamyltransferase

HASTE:

Half-Fourier single-shot turbo spin-echo

HBP:

Hepatobiliary phase

HCC:

Hepatocellular cancer

ICC:

Intra-class correlation coefficient

KW:

Kruskal-Wallis

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

MVI:

Microvascular invasion

NPV:

Negative predictive value

PPV:

Positive predictive value

Rad score:

Radiomics score

RFS:

Recurrence-free survival

ROI:

Region of interest

TSE:

Turbo spin-echo

VOI:

Volume of interest

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Funding

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).

Author information

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Shi-Ting Feng, Yingmei Jia & Kaikai Wei

  2. Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Bing Liao & Lili Chen

  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 Center, Shenzhen University, Shenzhen, China

    Bingsheng Huang

  4. Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Qian Zhou, Bin Li, Haibo Wang & Sui Peng

  5. GE Healthcare, Shanghai, China

    Xin Li

  6. Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Wei Wang, Shuling Chen & Ming Kuang

  7. Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Xiaofang He

  8. Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Sui Peng & Mimi Tang

  9. Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Ze-Bin Chen, Zhihang Chen & Ming Kuang

  10. Jinan University, Guangzhou, China

    Yang Hou

  11. Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Zhenwei Peng

Authors
  1. Shi-Ting Feng
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  2. Yingmei Jia
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  3. Bing Liao
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  5. Qian Zhou
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  6. Xin Li
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  7. Kaikai Wei
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  8. Lili Chen
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  9. Bin Li
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  10. Wei Wang
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  11. Shuling Chen
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  12. Xiaofang He
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  13. Haibo Wang
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  14. Sui Peng
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  15. Ze-Bin Chen
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  16. Mimi Tang
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  17. Zhihang Chen
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  18. Yang Hou
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  19. Zhenwei Peng
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  20. Ming Kuang
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Corresponding authors

Correspondence to Zhenwei Peng or Ming Kuang.

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Guarantor

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.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Shi-Ting Feng and Yingmei Jia are co-first authors.

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Feng, ST., Jia, Y., Liao, B. et al. Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI. Eur Radiol 29, 4648–4659 (2019). https://doi.org/10.1007/s00330-018-5935-8

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  • DOI: https://doi.org/10.1007/s00330-018-5935-8

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