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Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature

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

Abstract

Purpose

This study was conducted in order to investigate the value of magnetic resonance imaging (MRI)-based radiomics signatures for the preoperative prediction of hepatocellular carcinoma (HCC) grade.

Methods

Data from 170 patients confirmed to have HCC by surgical pathology were divided into a training group (n = 125) and a test group (n = 45). The radiomics features of tumours based on both T1-weighted imaging (WI) and T2WI were extracted by using Matrix Laboratory (MATLAB), and radiomics signatures were generated using the least absolute shrinkage and selection operator (LASSO) logistic regression model. The predicted values of pathological HCC grades using radiomics signatures, clinical factors (including age, sex, tumour size, alpha fetoprotein (AFP) level, history of hepatitis B, hepatocirrhosis, portal vein tumour thrombosis, portal hypertension and pseudocapsule) and the combined models were assessed.

Results

Radiomics signatures could successfully categorise high-grade and low-grade HCC cases (p < 0.05) in both the training and test datasets. Regarding the performances of clinical factors, radiomics signatures and the combined clinical and radiomics signature (from the combined T1WI and T2WI images) models for HCC grading prediction, the areas under the curve (AUCs) were 0.600, 0.742 and 0.800 in the test datasets, respectively. Both the AFP level and radiomics signature were independent predictors of HCC grade (p < 0.05).

Conclusions

Radiomics signatures may be important for discriminating high-grade and low-grade HCC cases. The combination of the radiomics signatures with clinical factors may be helpful for the preoperative prediction of HCC grade.

Key Points

• The radiomics signature based on non-contrast-enhanced MR images was significantly associated with the pathological grade of HCC.

• The radiomics signatures based on T1WI or T2WI images performed similarly at predicting the pathological grade of HCC.

• Combining the radiomics signature and clinical factors (including age, sex, tumour size, AFP level, history of hepatitis B, hepatocirrhosis, portal vein tumour thrombosis, portal hypertension and pseudocapsule) may be helpful for the preoperative prediction of HCC grade.

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Abbreviations

3D:

Three-dimensional

AFP:

Alpha fetoprotein

AUC:

Area under the curve

CHB:

Chronic hepatitis B

CI:

Confidence interval

DWI:

Diffusion-weighted imaging

GLCM:

Grey-level co-occurrence matrix

GLN:

Grey-level run-length non-uniformity

GLRLM:

Grey-level run-length matrix

HCC:

Hepatocellular carcinoma

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

MVI:

Microvascular invasion

NSCLC:

Non-small cell lung cancer

OR:

Odds ratio

PACS:

Picture archiving and communication system

ROC:

Receiver operating characteristic

ROI:

Region of interest

T1WI:

T1-weighted imaging

T2WI:

T2-weighted imaging

TE:

Echo time

TR:

Repetition time

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Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Radiological Department of the People’s Hospital of Zhengzhou University and Henan Provincial People’s Hospital, No. 7 Weiwu Road, Zhengzhou, 450003, Henan, China

    Minghui Wu, Hongna Tan, Peigang Ning, Shaocheng Zhu, Meiyun Wang, Shewei Dou & Dapeng Shi

  2. National Digital Switching System Engineering and Technological R&D Center, Zhengzhou, 450002, Henan, China

    Fei Gao, Jinjin Hai & Jian Chen

Authors
  1. Minghui Wu
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  2. Hongna Tan
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  3. Fei Gao
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  4. Jinjin Hai
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  6. Jian Chen
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  7. Shaocheng Zhu
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  8. Meiyun Wang
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  10. Dapeng Shi
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Corresponding author

Correspondence to Dapeng Shi.

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Guarantor

The scientific guarantor of this publication is Dapeng Shi.

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 obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Wu, M., Tan, H., Gao, F. et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. Eur Radiol 29, 2802–2811 (2019). https://doi.org/10.1007/s00330-018-5787-2

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  • DOI: https://doi.org/10.1007/s00330-018-5787-2

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