Volume 48, Issue 4 pp. 916-926

Original Research

Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI

Xi Zhang MSc,

Xi Zhang MSc

orcid.org/0000-0003-0032-6636

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

The first two authors (Zhang X and Tian Q) contributed equally to this work.

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Qiang Tian MD,

Qiang Tian MD

Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

The first two authors (Zhang X and Tian Q) contributed equally to this work.

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Liang Wang MD,

Liang Wang MD

Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

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Yang Liu PhD,

Yang Liu PhD

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

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Baojuan Li PhD,

Baojuan Li PhD

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

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Zhengrong Liang PhD,

Zhengrong Liang PhD

Departments of Radiology, Computer Science and Biomedical Engineering, State University of New York, Stony Brook, New York, USA

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Peng Gao MSc,

Peng Gao MSc

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

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Kaizhong Zheng MSc,

Kaizhong Zheng MSc

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

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Bofeng Zhao MSc, MD,

Bofeng Zhao MSc, MD

Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

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Hongbing Lu PhD,

Corresponding Author

Hongbing Lu PhD

[email protected]

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

Address reprint request to: H.L., Fourth Military Medical University, 169 Changle West Road, Xi'an, Shanxi, P.R. China, 710032. E-mail: [email protected]Search for more papers by this author

Xi Zhang MSc,

Xi Zhang MSc

orcid.org/0000-0003-0032-6636

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

The first two authors (Zhang X and Tian Q) contributed equally to this work.

Search for more papers by this author

Qiang Tian MD,

Qiang Tian MD

Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

The first two authors (Zhang X and Tian Q) contributed equally to this work.

Search for more papers by this author

Liang Wang MD,

Liang Wang MD

Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

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Yang Liu PhD,

Yang Liu PhD

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

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Baojuan Li PhD,

Baojuan Li PhD

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

Search for more papers by this author

Zhengrong Liang PhD,

Zhengrong Liang PhD

Departments of Radiology, Computer Science and Biomedical Engineering, State University of New York, Stony Brook, New York, USA

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Peng Gao MSc,

Peng Gao MSc

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

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Kaizhong Zheng MSc,

Kaizhong Zheng MSc

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

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Bofeng Zhao MSc, MD,

Bofeng Zhao MSc, MD

Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

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Hongbing Lu PhD,

Corresponding Author

Hongbing Lu PhD

[email protected]

Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China

Address reprint request to: H.L., Fourth Military Medical University, 169 Changle West Road, Xi'an, Shanxi, P.R. China, 710032. E-mail: [email protected]Search for more papers by this author

First published: 02 February 2018

https://doi.org/10.1002/jmri.25960

Citations: 93

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Abstract

Background

Noninvasive detection of isocitrate dehydrogenase (IDH) and TP53 mutations are meaningful for molecular stratification of lower-grade gliomas (LrGG).

Purpose

To explore potential MRI features reflecting IDH and TP53 mutations of LrGG, and propose a radiomics strategy for detecting them.

Study Type

Retrospective, radiomics.

Population/Subjects

A total of 103 LrGG patients were separated into development (n = 73) and validation (n = 30) cohorts.

Field Strength/Sequence

T₁-weighted (before and after contrast-enhanced), T₂-weighted, and fluid-attenuation inversion recovery images from 1.5T (n = 37) or 3T (n = 66) scanners.

Assessment

After data preprocessing, high-throughput features were derived from patients' volumes of interests of different sequences. The support vector machine-based recursive feature elimination (SVM-RFE) was adopted to find the optimal features for IDH and TP53 mutation detection. SVM models were trained and tested on development and validation cohort. The commonly used metric was used for assessing the efficiency.

Statistical Tests

One-way analysis of variance (ANOVA), chi-square, or Fisher's exact test were applied on clinical characteristics to confirm whether significant differences exist between three molecular subtypes decided by IDH and TP53 status. Intraclass correlation coefficients were calculated to assess the robustness of the radiomics features.

Results

The constituent ratio of histopathologic subtypes was significantly different among three molecular subtypes (P = 0.017). SVM models for detecting IDH and TP53 mutation were established using 12 and 22 optimal features selected by SVM-RFE. The accuracies and area under the curves for IDH and TP53 mutations on the development cohort were 84.9%, 0.830, and 92.0%, 0.949, while on the validation cohort were 80.0%, 0.792, and 85.0%, 0.869, respectively. Furthermore, the stratified accuracies of three subtypes were 72.8%, 71.9%, and 70%, respectively.

Data Conclusion

Using a radiomics approach integrating SVM model and multimodal MRI features, molecular subtype stratification of LGG patients was implemented through detecting IDH and TP53 mutations. The results suggested that the proposed approach has promising detecting efficiency and T₂-weighted image features are more important than features from other images.

Level of Evidence: 3

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2018;48:916–926.

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Citing Literature

Volume48, Issue4

October 2018

Pages 916-926

Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI

Abstract

Background

Purpose

Study Type

Population/Subjects

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

References

Citing Literature

References

Information

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Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI

Abstract

Background

Purpose

Study Type

Population/Subjects

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

References

Citing Literature

References

Related

Information