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Preoperative prediction of brain invasion in meningiomas: a comparison of diffusion kurtosis imaging and diffusion
Tianjin Zhong1, Xiaodan Chen1,2,3, Ye Li1,3
1Department of Radiology, Fujian Medical University Union Hospital, No.29, Xinquan Road, Fuzhou, 350001, China.
Objective:
Identifying brain invasion particularly important for meningioma patients, but there is still a lack of valuable early biomarkers. The authors aimed to investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in predicting the brain invasion of meningioma.
Methods:
A total of 132 meningioma patients were prospectively included and underwent magnetic resonance diffusion imaging. The whole-tumour histogram parameters were extracted from diffusion maps including Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD). The Mann-Whitney U test was used to compare the parameters of brain invasive and non-invasive meningiomas. The receiver operating characteristic (ROC) curve and multiple logistic regression analyes were performed to identify the diagnostic performance. Spearman's partial correlation was used to evaluate correlations between histogram parameters and the Ki-67 index.
Results:
DKI-MK (10th, 50th, 90th percentile, maximum, mean and kurtosis), DKI-FA (minimum, maximum), DKI-MD (minimum, maximum, kurtosis and skewness), DTI-FA (maximum), DTI-MD (10th percentile, kurtosis and skewness) showed statistically significant differences between brain invasive and non-invasive meningiomas (p < 0.05). For all histogram parameters, the highest individual predictor was DKI-MK 90th percentile with an AUC of 0.837 and an accuracy of 75.0%. The DKI combined model can further improve the diagnostic efficiency, with an AUC of 0.914 and an accuracy of 85.6%. Significant correlations were found between various diffusion histogram parameters and the Ki-67 index (rho = -0.244-0.504, p < 0.05).
Conclusions:
The whole-tumour DKI and DTI histogram analysis is a promising approach for predicting brain invasion in meningiomas, and the multi-parameter combined model can further improve diagnostic efficiency.
