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Evaluation of diffuse glioma grade and proliferation activity by different diffusion-weighted-imaging models including diffusion kurtosis imaging (DKI) and mean apparent propagator (MAP) MRI

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

To evaluate two advanced diffusion models, diffusion kurtosis imaging and the newly proposed mean apparent propagation factor-magnetic resonance imaging, in the grading of gliomas and the assessing of their proliferative activity.

Methods

Fifty-nine patients with clinically diagnosed and pathologically proven gliomas were enrolled in this retrospective study. All patients underwent DKI and MAP-MRI scans. Manually outline the ROI of the tumour parenchyma. After delineation, the imaging parameters were extracted using only the data from within the ROI including mean diffusion kurtosis (MK), return-to-origin probability (RTOP), Q-space inverse variance (QIV) and non-Gaussian index (NG), and the differences in each parameter in the classification of glioma were compared. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of these parameters.

Results

MK, NG, RTOP and QIV were significantly different amongst the different grades of glioma. MK, NG and RTOP had excellent diagnostic value in differentiating high-grade from low-grade glioma, with largest areas under the curve (AUCs; 0.929, 0.933 and 0.819, respectively; P < 0.01). MK and NG had the largest AUCs (0.912 and 0.904) when differentiating grade II tumours from III tumours (P < 0.01) and large AUCs (0.791 and 0.786) when differentiating grade III from grade IV tumours. Correlation analysis of tumour proliferation activity showed that MK, NG and QIV were strongly correlated with the Ki-67 LI (P < 0.001).

Conclusion

MK, RTOP and NG can effectively represent the microstructure of these altered tumours. Multimodal diffusion-weighted imaging is valuable for the preoperative evaluation of glioma grade and tumour proliferative activity.

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Data availability

All data generated or analysed during this study are included in this published article.

Code availability

DIPY (Diffusion Imaging in Python, http://nipy.org/dipy).

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Funding

This study has received funding by Inner Mongolia Autonomous Region Science and Technology Plan Project: Application value of multimodal functional magnetic resonance imaging in accurate evaluation of glioma (No. 2019GG047).

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Author notes

  1. SHX and RL contributed equally to this work.

Authors and Affiliations

  1. Graduate School of Tianjin Medical University, Tianjin, China

    Sheng-hui Xie

  2. Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China

    Sheng-hui Xie, Bo Li, He Zhao, Peng Wang, Jin-long He, Xue-ying Ma, Qiong Wu & Yang Gao

  3. Ultrasound Diagnostic Department, Inner Mongolia People’s Hospital, Hohhot, Inner Mongolia, China

    Rui Lang

  4. MR Scientific Marketing, Siemens Healthcare, Shanghai, China

    Shao-yu Wang & Hua-peng Zhang

  5. Department of Radiology, Zhongshan Hospital Affiliated to Dalian University, Dalian, Liaoning Province, China

    Jian-lin Wu

Authors
  1. Sheng-hui Xie
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Contributions

Yang Gao and Qiong Wu contributed to the conception of the study; Peng Wang and He Zhao performed the experiment; Sheng-hui Xie and Rui Lang contributed significantly to analysis and manuscript preparation; Bo Li, Xue-ying Ma and Jin-long He performed the data analyses and wrote the manuscript; Hua-peng Zhang and Shao-yu Wang helped perform the analysis with constructive discussions.

Corresponding author

Correspondence to Jian-lin Wu.

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Conflict of interest

The two authors in this article (Huapeng Zhang and Shaoyu Wang) are employees of Siemens Healthcare. The remaining authors state that their products or services may not be related to any company related to the subject of the article.

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Institutional Review Board approval was obtained.

Informed Consent

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

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Written informed consent was obtained from the patient for publication of this case report and any accompanying images.

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Key Points

1.Non-Gaussian diffusion model (DKI and MAP-MRI) can better reflect the pathological state of glioma than Gaussian diffusion model.

2.High-order diffusion weighted imaging can better reflect the proliferation activity of glioma.

3.DKI and MAP-MRI are important for neurosurgeons to assess the grade of glioma before operation.

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Xie, Sh., Lang, R., Li, B. et al. Evaluation of diffuse glioma grade and proliferation activity by different diffusion-weighted-imaging models including diffusion kurtosis imaging (DKI) and mean apparent propagator (MAP) MRI. Neuroradiology 65, 55–64 (2023). https://doi.org/10.1007/s00234-022-03000-0

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  • DOI: https://doi.org/10.1007/s00234-022-03000-0

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