European Radiology

, Volume 29, Issue 4, pp 1986–1996 | Cite as

Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour

  • Changliang Su
  • Jingjing Jiang
  • Shun Zhang
  • Jingjing Shi
  • Kaibin Xu
  • Nanxi Shen
  • Jiaxuan Zhang
  • Li Li
  • Lingyun Zhao
  • Ju Zhang
  • Yuanyuan Qin
  • Yong LiuEmail author
  • Wenzhen ZhuEmail author



To explore the feasibility and diagnostic performance of radiomics based on anatomical, diffusion and perfusion MRI in differentiating among glioma subtypes and predicting tumour proliferation.


220 pathology-confirmed gliomas and ten contrasts were included in the retrospective analysis. After being registered to T2FLAIR images and resampling to 1 mm3 isotropically, 431 radiomics features were extracted from each contrast map within a semi-automatic defined tumour volume. For single-contrast and the combination of all contrasts, correlations between the radiomics features and pathological biomarkers were revealed by partial correlation analysis, and multivariate models were built to identify the best predictive models with adjusted 0.632+ bootstrap AUC.


In univariate analysis, both non-wavelet and wavelet radiomics features were correlated significantly with tumour grade and the Ki-67 labelling index. The max R was 0.557 (p = 2.04E-14) in T1C for tumour grade and 0.395 (p = 2.33E-07) in ADC for Ki-67. In the multivariate analysis, the combination of all-contrast radiomics features had the highest AUCs in both differentiating among glioma subtypes and predicting proliferation compared with those in single-contrast images. For low-/high-grade gliomas, the best AUC was 0.911. In differentiating among glioma subtypes, the best AUC was 0.896 for grades II–III, 0.997 for grades II–IV, and 0.881 for grades III–IV. In predicting proliferation levels, multicontrast features led to an AUC of 0.936.


Multicontrast radiomics supplies complementary information on both geometric characters and molecular biological traits, which correlated significantly with tumour grade and proliferation. Combining all-contrast radiomics models might precisely predict glioma biological behaviour, which may be attributed to presurgical personal diagnosis.

Key Points

Multicontrast MRI radiomics features are significantly correlated with tumour grade and Ki-67 LI.

Multimodality MRI provides independent but supplemental information in assessing glioma pathological behaviour.

Combined multicontrast MRI radiomics can precisely predict glioma subtypes and proliferation levels.


Radiomics Glioma Neoplasm grading Cell proliferation Magnetic resonance imaging 



Apparent diffusion coefficient


Arterial spin labelling imaging


Cerebral blood flow


Diffusion-weighted imaging


Exponential apparent diffusion coefficient


Fluid-attenuated inversion recovery


Contrast-enhanced T1-weighted images


T2-weighted fast-echo images


Volume of interest



We acknowledge Dr. M Vallières for his patient responses regarding the radiomics data post-processing. We also acknowledge Dr. Yang Fan from General Electric Company (China) for his generous contributions to manuscript revision. We also thank Dr. Xiaowei Chen (10 years’ experience) for his generous work in checking the VOIs.


This study has received funding by the National Program of the Ministry of Science and Technology of China during the “12th Five-Year Plan” (ID: 2011BAI08B10) and the National Natural Science Foundation of China (No. 81171308, No. 81570462, and No. 81730049)

Compliance with ethical standards


The scientific guarantor of this publication is Wenzhen Zhu.

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

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.


• Retrospective

• Observational

• Performed at one institution

Supplementary material

330_2018_5704_MOESM1_ESM.docx (425 kb)
ESM 1 (DOCX 425 kb)


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Changliang Su
    • 1
  • Jingjing Jiang
    • 1
  • Shun Zhang
    • 1
  • Jingjing Shi
    • 1
  • Kaibin Xu
    • 2
    • 3
  • Nanxi Shen
    • 1
  • Jiaxuan Zhang
    • 1
  • Li Li
    • 1
  • Lingyun Zhao
    • 1
  • Ju Zhang
    • 1
  • Yuanyuan Qin
    • 1
  • Yong Liu
    • 2
    • 3
    • 4
    Email author
  • Wenzhen Zhu
    • 1
    Email author
  1. 1.Department of Radiology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
  2. 2.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
  4. 4.Center for Excellence in Brain Science and Intelligence Technology, Institute of AutomationChinese Academy of SciencesBeijingChina

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