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Grading meningiomas with diffusion metrics: a comparison between diffusion kurtosis, mean apparent propagator,
Dejun She1,2, Hao Huang1, Wei Guo1
1Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China.
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.
Insights
Histogram analysis of diffusion metrics aids in meningioma grading. Diffusion tensor imaging (DTI) shows comparable diagnostic performance to advanced diffusion models for predicting tumor grade and proliferation.
Area of Science:
- Neuroimaging
- Radiology
- Oncology
Background:
- Meningiomas are the most common primary brain tumors.
- Accurate grading is crucial for treatment planning.
- Advanced diffusion MRI techniques offer potential for non-invasive tumor characterization.
Purpose of the Study:
- To compare histogram features of multiple diffusion metrics in predicting meningioma grade.
- To assess the correlation between diffusion metrics and cellular proliferation (Ki-67 index).
Main Methods:
- Diffusion spectrum imaging (DSI) was performed on 122 meningiomas.
- Histogram features from Diffusion Tensor Imaging (DTI), Diffusion Kurtosis Imaging (DKI), Mean Apparent Propagator (MAP), and Neurite Orientation Dispersion and Density Imaging (NODDI) were analyzed.
- Statistical analyses included Man-Whitney U test, logistic regression, and correlation analysis with Ki-67 index.
Main Results:
- Specific histogram features of DKI, MAP, and NODDI were lower in low-grade meningiomas (LGMs) compared to high-grade meningiomas (HGMs).
- Diffusion Tensor Imaging (DTI) mean diffusivity minimum values were higher in LGMs than HGMs.
- No significant differences in Area Under the Curve (AUC) were found between DTI, DKI, MAP, and NODDI models for grading meningiomas.
- Weak positive correlations were observed between Ki-67 index and DKI, MAP, and NODDI metrics.
Conclusions:
- Whole-tumor histogram analysis of multiple diffusion models is a promising method for meningioma grading.
- Diffusion Tensor Imaging (DTI) demonstrates comparable diagnostic performance to advanced diffusion models (DKI, MAP, NODDI) in grading meningiomas.
- Diffusion metrics show a weak association with meningioma proliferation status.
