Original Research

MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma

Dongsheng Gu PhD

Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Yongsheng Xie MD

Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China

Dongsheng Gu, Yongsheng Xie, Jingwei Wei and Wencui Li contributed equally to this work.Search for more papers by this author

Jingwei Wei PhD,

Jingwei Wei PhD

Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

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Wencui Li MD,

Wencui Li MD

Dongsheng Gu, Yongsheng Xie, Jingwei Wei and Wencui Li contributed equally to this work.Search for more papers by this author

Zhaoxiang Ye PhD,

Zhaoxiang Ye PhD

Search for more papers by this author

Zhongyuan Zhu MD,

Zhongyuan Zhu MD

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Jie Tian PhD,

Corresponding Author

Jie Tian PhD

[email protected]

orcid.org/0000-0003-0498-0432

Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

University of Chinese Academy of Sciences, Beijing, China

Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China

Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China

Address reprint requests to: X.L., Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China E-mail: [email protected] or J.T., Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing,China E-mail: [email protected]Search for more papers by this author

Xubin Li PhD,

Corresponding Author

Xubin Li PhD

[email protected]

Dongsheng Gu PhD,

Dongsheng Gu PhD

Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Search for more papers by this author

Yongsheng Xie MD,

Yongsheng Xie MD

Dongsheng Gu, Yongsheng Xie, Jingwei Wei and Wencui Li contributed equally to this work.Search for more papers by this author

Jingwei Wei PhD,

Jingwei Wei PhD

Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Search for more papers by this author

Wencui Li MD,

Wencui Li MD

Dongsheng Gu, Yongsheng Xie, Jingwei Wei and Wencui Li contributed equally to this work.Search for more papers by this author

Zhaoxiang Ye PhD,

Zhaoxiang Ye PhD

Search for more papers by this author

Zhongyuan Zhu MD,

Zhongyuan Zhu MD

Search for more papers by this author

Jie Tian PhD,

Corresponding Author

Jie Tian PhD

[email protected]

orcid.org/0000-0003-0498-0432

Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

University of Chinese Academy of Sciences, Beijing, China

Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China

Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China

Xubin Li PhD,

Corresponding Author

Xubin Li PhD

[email protected]

First published: 03 June 2020

https://doi.org/10.1002/jmri.27199

Citations: 46

Contract grant sponsor: National Natural Science Foundation of China; Contract grant numbers: 81227901, 81527805; Contract grant sponsor: Ministry of Science and Technology of China; Contract grant numbers: 2017YFC1308701, 2017YFC1309100, 2016YFC0102600, 2016YFA0100902, 2016YFC0103803, 2016YFA0201401, 2016YFC0103702, 2014CB748600, 2016YFC0103001; Contract grant sponsor: Chinese Academy of Sciences; Contract grant numbers: GJJSTD20170004, QYZDJ-SSW-JSC005; Contract grant sponsor: Beijing Municipal Science & Technology Commission; Contract grant numbers: Z161100002616022，Z171100000117023; Contract grant sponsor: Strategic Priority Research Program of Chinese Academy of Science; Contract grant number: XDBS01000000.

Level of Evidence: 3 Technical Efficacy Stage: 2

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Abstract

Background

Glypican 3 (GPC3) expression has proved to be a critical risk factor related to prognosis in hepatocellular carcinoma (HCC) patients.

Purpose

To investigate the performance of MRI-based radiomics signature in identifying GPC3-positive HCC.

Study Type

Retrospective.

Population

An initial cohort of 293 patients with pathologically confirmed HCC was involved in this study, and patients were randomly divided into training (195) and validation (98) cohorts.

Field Strength/Sequences

Contrast-enhanced T₁-weight MRI was performed with a 1.5T scanner.

Assessment

A total of 853 radiomic features were extracted from the volume imaging. Univariate analysis and Fisher scoring were utilized for feature reduction. Subsequently, forward stepwise feature selection and radiomics signature building were performed based on a support vector machine (SVM). Incorporating independent risk factors, a combined nomogram was developed by multivariable logistic regression modeling.

Statistical Tests

The predictive performance of the nomogram was calculated using the area under the receive operating characteristic curve (AUC). Decision curve analysis (DCA) was applied to estimate the clinical usefulness.

Results

The radiomics signature consisting of 10 selected features achieved good prediction efficacy (training cohort: AUC = 0.879, validation cohort: AUC = 0.871). Additionally, the combined nomogram integrating independent clinical risk factor α-fetoprotein (AFP) and radiomics signature showed improved calibration and prominent predictive performance with AUCs of 0.926 and 0.914 in the training and validation cohorts, respectively.

Data Conclusion

The proposed MR-based radiomics signature is strongly related to GPC3-positive. The combined nomogram incorporating AFP and radiomics signature may provide an effective tool for noninvasive and individualized prediction of GPC3-positive in patients with HCC. J. MAGN. RESON. IMAGING 2020;52:1679–1687.

Supporting Information

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Citing Literature

Volume52, Issue6

December 2020

Pages 1679-1687

MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma

Abstract

Background

Purpose

Study Type

Population

Field Strength/Sequences

Assessment

Statistical Tests

Results

Data Conclusion

Supporting Information

REFERENCES

Citing Literature

References

Information

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MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma

Abstract

Background

Purpose

Study Type

Population

Field Strength/Sequences

Assessment

Statistical Tests

Results

Data Conclusion

Supporting Information

REFERENCES

Citing Literature

References

Related

Information