Grading Meningiomas Utilizing Multiparametric MRI with Inclusion of Susceptibility Weighted Imaging and Quantitative Susceptibility Mapping
2019
Abstract Background and purpose The ability to predict
high-grade
meningiomapreoperatively is important for clinical
surgical planning. The purpose of this study is to evaluate the performance of comprehensive
multiparametric MRI, including
susceptibility weighted imaging(SWI) and
quantitative susceptibility mapping(QSM) in predicting
high-grade
meningiomaboth qualitatively and quantitatively. Methods Ninety-two low-grade and 37 higher grade
meningiomasin 129 patients were included in this study. Morphological characteristics, quantitative histogram analysis of QSM and ADC images, and tumor size were evaluated to predict
high-grade
meningiomausing univariate and multivariate analyses. Receiver operating characteristic (ROC) analyses were performed on the morphological characteristics. Associations between Ki-67
proliferative index(PI) and quantitative parameters were calculated using Pearson correlation analyses. Results For predicting
high-grade
meningiomas, the best predictive model in multivariate logistic regression analyses included calcification (β = 0.874, P = 0.110), peritumoral edema (β = 0.554, P = 0.042), tumor border (β = 0.862, P = 0.024), tumor location (β = 0.545, P = 0.039) for morphological characteristics, and tumor size (β = 4 × 10 −5 , P = 0.004), QSM
kurtosis(β = − 5 × 10 −3 , P = 0.058), QSM entropy (β = − 0.067, P = 0.054), maximum ADC (β = − 1.6 × 10 −3 , P = 0.003), ADC
kurtosis(β = − 0.013, P = 0.014) for quantitative characteristics. ROC analyses on morphological characteristics resulted in an area under the curve (AUC) of 0.71 (0.61–0.81) for a combination of them. There were significant correlations between Ki-67 PI and mean ADC (r = − 0.277, P = 0.031), 25 th percentile of ADC (r = − 0.275, P = 0.032), and 50 th percentile of ADC (r = − 0.268, P = 0.037). Conclusions Although SWI and QSM did not improve differentiation between low and
high-grade
meningiomas, combining morphological characteristics and quantitative metrics can help predict
high-grade
meningioma.
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