Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping.
Academic Article
Overview
abstract
BACKGROUND AND PURPOSE: The ability to predict high-grade meningioma preoperatively 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 meningioma both qualitatively and quantitatively. METHODS: Ninety-two low-grade and 37 higher grade meningiomas in 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 meningioma using 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), 25th percentile of ADC (r=-0.275, P=0.032), and 50th 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.
