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A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status

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Abstract

Objectives

The purpose of the study was to explore the performance of a three-component diffusion model in evaluating the degree of malignancy and isocitrate dehydrogenase 1 (IDH-1) gene type of gliomas.

Methods

Overall, 60 patients with gliomas were enrolled. The intermediate and perfusion-related diffusion coefficients (Dint and Dp) and fractions of strictly limited, intermediate, and perfusion-related diffusion (Fvery-slow, Fint, and Fp) were obtained with a three-component diffusion model. Parameters were also obtained from a diffusion kurtosis model and mono- and biexponential models. All parameters were compared between different tumor grades and IDH-1 gene types. Diagnostic performance and logistic regression analyses were performed.

Results

High-grade gliomas (HGGs) had significantly higher Fint, Fvery-slow, and Dp values but significantly lower Fp and Dint values than low-grade gliomas (LGGs), and Fint and Fp differed significantly among grade II, III, and IV gliomas (p < 0.05 for all). Fint achieved the highest AUC of 0.872 in differentiating between LGGs and HGGs. Logistic regression analysis revealed that in each model, Fint, diffusion coefficient (D), apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) were associated with glioma grading. After multiple regression analysis, Fint remained the only differentiator. Additionally, Fint and Fp showed significant differences between IDH-1 mutated and IDH-1 wild-type gliomas (p = 0.007 and 0.01, respectively).

Conclusions

The three-component DWI model served as a useful biomarker for detecting microstructural features in gliomas with different grades and IDH-1 mutation statuses.

Key Points

• The three-component model enables the estimation of an intermediate diffusion component.

• The three-component model performed better than the other models in glioma grading and genotyping.

• Fint was useful in evaluating the grade and genotype of gliomas.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

DWI:

Diffusion-weighted imaging

EES:

Extravascular extracellular space

HGG:

High-grade glioma

IDH-1:

Isocitrate dehydrogenase 1

IVIM:

Intravoxel incoherent motion

LGG:

Low-grade glioma

MRI:

Magnetic resonance imaging

NNLS:

Non-negative least squares

ROC:

Receiver operating characteristic

ROI:

Region of interest

WHO:

World Health Organization

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Funding

This study was funded by the National Natural Science Foundation of China (No. 82171885, 81971583); Shanghai Science and Technology Commission Explorer Program (21TS1400700); Shanghai Natural Science Foundation (20ZR1433200); and the Medical Engineering Cross Research Foundation of Shanghai Jiao Tong University (No. YG2022QN035).

Author information

Author notes

  1. Mengqiu Cao and Xiaoqing Wang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd., Shanghai, 200127, China

    Mengqiu Cao, Xiaoqing Wang, Fang Liu & Yan Zhou

  2. MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China

    Ke Xue & Yongming Dai

Authors
  1. Mengqiu Cao
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  2. Xiaoqing Wang
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  3. Fang Liu
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  4. Ke Xue
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  5. Yongming Dai
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  6. Yan Zhou
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Corresponding author

Correspondence to Yan Zhou.

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Guarantor

The scientific guarantor of this publication is Yan Zhou.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

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

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Institutional review board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Cao, M., Wang, X., Liu, F. et al. A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status. Eur Radiol 33, 2871–2880 (2023). https://doi.org/10.1007/s00330-022-09212-5

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  • DOI: https://doi.org/10.1007/s00330-022-09212-5

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