European Radiology

, Volume 27, Issue 12, pp 5056–5063 | Cite as

The diagnostic value of high-frequency power-based diffusion-weighted imaging in prediction of neuroepithelial tumour grading

  • Zhiye Chen
  • Peng Zhou
  • Bin Lv
  • Mengqi Liu
  • Yan Wang
  • Yulin Wang
  • Xin Lou
  • Qiuping Gui
  • Huiguang He
  • Lin Ma



To retrospectively evaluate the diagnostic value of high-frequency power (HFP) compared with the minimum apparent diffusion coefficient (MinADC) in the prediction of neuroepithelial tumour grading.


Diffusion-weighted imaging (DWI) data were acquired on 115 patients by a 3.0-T MRI system, which included b0 images and b1000 images over the whole brain in each patient. The HFP values and MinADC values were calculated by an in-house script written on the MATLAB platform.


There was a significant difference among each group excluding grade I (G1) vs. grade II (G2) (P = 0.309) for HFP and among each group for MinADC. ROC analysis showed a higher discriminative accuracy between low-grade glioma (LGG) and high-grade glioma (HGG) for HFP with area under the curve (AUC) value 1 compared with that for MinADC with AUC 0.83 ± 0.04 and also demonstrated a higher discriminative ability among the G1-grade IV (G4) group for HFP compared with that for MinADC except G1 vs. G2.


HFP could provide a simple and effective optimal tool for the prediction of neuroepithelial tumour grading based on diffusion-weighted images in routine clinical practice.

Key Points

HFP shows positive correlation with neuroepithelial tumour grading.

HFP presents a good diagnostic efficacy for LGG and HGG.

HFP is helpful in the selection of brain tumour boundary.


Diffusion-weighted imaging High-frequency power Minimum apparent diffusion coefficient Neuroepithelial tumour grading Magnetic resonance imaging 



Area under the curve


Diffusion-weighted imaging


Grade I


Grade II


Grade III


Grade IV


High-frequency power


High-grade glioma


Low-grade glioma


Minimum apparent diffusion coefficient


Receiver-operating characteristics curve


Compliance with ethical standards


The scientific guarantor of this publication is Lin Ma.

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.


This study has received funding from a Special Financial Grant from the China Postdoctoral Science Foundation (2014 T70960), the Foundation for Medical and Health Science and Technology Innovation Project of Sanya (2016YW37), National Natural Science Foundation of China (91520202) and Youth Innovation Promotion Association CAS.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was not required for this study because this is a retrospective study, and MRI sequences and diffusion-weighted imaging are routinely performed in clinical practice at our hospital.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2017_4899_MOESM1_ESM.doc (114 kb)
ESM 1 (DOC 114 kb)


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Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  • Zhiye Chen
    • 1
    • 2
  • Peng Zhou
    • 3
    • 4
  • Bin Lv
    • 5
  • Mengqi Liu
    • 1
    • 2
  • Yan Wang
    • 1
  • Yulin Wang
    • 1
  • Xin Lou
    • 1
  • Qiuping Gui
    • 6
  • Huiguang He
    • 3
    • 4
    • 7
  • Lin Ma
    • 1
  1. 1.Department of RadiologyChinese PLA General HospitalBeijingChina
  2. 2.Department of RadiologyHainan Branch of Chinese PLA General HospitalSanyaChina
  3. 3.Research Center for Brain-inspired Intelligence, Institute of AutomationChinese Academy of SciencesBeijingChina
  4. 4.University of Chinese Academy of Sciences BeijingChina
  5. 5.Academy of Telecommunication Research of MIITBeijingChina
  6. 6.Department of PathologyChinese PLA General HospitalBeijingChina
  7. 7.Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesBeijingChina

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