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Grading meningiomas with diffusion metrics: a comparison between diffusion kurtosis, mean apparent propagator, neurite orientation dispersion and density, and diffusion tensor imaging

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Abstract

Objectives

To compare the histogram features of multiple diffusion metrics in predicting the grade and cellular proliferation of meningiomas.

Methods

Diffusion spectrum imaging was performed in 122 meningiomas (30 males, 13–84 years), which were divided into 31 high-grade meningiomas (HGMs, grades 2 and 3) and 91 low-grade meningiomas (LGMs, grade 1). The histogram features of multiple diffusion metrics obtained from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the solid tumours were analysed. All values between the two groups were compared with the Man-Whitney U test. Logistic regression analysis was applied to predict meningioma grade. The correlation between diffusion metrics and Ki-67 index was analysed.

Results

The DKI_AK (axial kurtosis) maximum, DKI_AK range, MAP_RTPP (return-to-plane probability) maximum, MAP_RTPP range, NODDI_ICVF (intracellular volume fraction) range, and NODDI_ICVF maximum values were lower (p < 0.0001), whilst the DTI_MD (mean diffusivity) minimum values were higher in LGMs than those in HGMs (p < 0.001). Amongst the DTI, DKI, MAP, NODDI, and combined diffusion models, no significant differences were found in areas under the receiver operating characteristic curves (AUCs) for grading meningiomas (AUCs, 0.75, 0.75, 0.80, 0.79, and 0.86, respectively; all corrected p > 0.05, Bonferroni correction). Significant but weak positive correlations were found between the Ki-67 index and DKI, MAP, and NODDI metrics (r = 0.26–0.34, all p < 0.05).

Conclusions

Whole tumour histogram analyses of the multiple diffusion metrics from four diffusion models are promising methods in grading meningiomas. The DTI model has similar diagnostic performance compared with advanced diffusion models.

Key Points

• Whole tumour histogram analyses of multiple diffusion models are feasible for grading meningiomas.

• The DKI, MAP, and NODDI metrics are weakly associated with the Ki-67 proliferation status.

• DTI has similar diagnostic performance compared with DKI, MAP, and NODDI in grading meningiomas.

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Abbreviations

AD:

Axial diffusivity

AK:

Axial kurtosis

AUCs:

Areas under the curves

DKI:

Diffusional kurtosis imaging

DSI:

Diffusion spectrum imaging

DTI:

Diffusion tensor imaging

FA:

Fractional anisotropy

FOV:

Field of view

HGMs:

High-grade meningiomas

ICVF:

Intracellular volume fraction

ISOVF:

Volume fraction of the isotropic compartment

LGMs:

Low-grade meningiomas

MAP:

Mean apparent propagator

MD:

Mean diffusivity

MK:

Mean kurtosis

MKT:

Mean kurtosis tensor

MSD:

Mean squared diffusion

NODDI:

Neurite orientation dispersion and density imaging

ODI:

Orientation dispersion index

QIV:

Q-space inverse variance

RD:

Radial diffusivity

RK:

Radial kurtosis

ROC:

Receiver operating characteristic curve

RTAP:

Return-to-axis probability

RTOP:

Return-to-origin probability

RTPP:

Return-to-plane probability

T1WI:

T1-weighted image

T2WI:

T2-weighted image

VOI:

Volume of interest

WHO:

World Health Organization Classification

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Acknowledgements

The authors thank Xueyong Liu, MD, and Henggui Chen, PhD, at the First Affiliated Hospital of Fujian Medical University, Fuzhou, China, for pathologic support and statistical support, respectively.

Funding

This study has received funding from the Fujian Provincial Health Technology Project (No. 2021GGA025), the National Natural Science Foundation of China (No. 82071869), and the Joint Funds of the Innovation of Science and Technology of Fujian Province (No. 2019Y9115).

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Authors and Affiliations

  1. Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People’s Republic of China

    Dejun She, Hao Huang, Wei Guo, Dongmei Jiang & Dairong Cao

  2. Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, 350005, People’s Republic of China

    Dejun She & Dairong Cao

  3. Philips, Healthineers Ltd., Beijing, 100000, People’s Republic of China

    Xiance Zhao & Yun Kang

  4. Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, 350005, People’s Republic of China

    Dairong Cao

Authors
  1. Dejun She
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Correspondence to Dairong Cao.

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Guarantor

The scientific guarantor of this publication is Dairong Cao.

Conflict of interest

The authors of this manuscript declare relationships with the following companies:

Xiance Zhao and Yun Kang are employees of Philips Healthcare. Both of them provided technical support in this study under Philips collaboration regulation without any payment and personal concern regarding this study. The remaining authors 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.

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Written informed consent was not required for this study because this is a retrospective study, and patient data are anonymised.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study

• performed at one institution

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Dejun She and Hao Huang contributed equally to this study and should be considered co-first authors.

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She, D., Huang, H., Guo, W. et al. Grading meningiomas with diffusion metrics: a comparison between diffusion kurtosis, mean apparent propagator, neurite orientation dispersion and density, and diffusion tensor imaging. Eur Radiol 33, 3671–3681 (2023). https://doi.org/10.1007/s00330-023-09505-3

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