利用包含磁敏感加权成像和定量磁敏感图的多参数 MRI 对脑膜瘤进行分级。
Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping.
机构信息
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Radiology, Weill Cornell Medicine, 407, E61st, Suite 117, 10065 New York, NY, USA.
Department of Radiology, Weill Cornell Medicine, 407, E61st, Suite 117, 10065 New York, NY, USA.
出版信息
J Neuroradiol. 2020 Jun;47(4):272-277. doi: 10.1016/j.neurad.2019.05.002. Epub 2019 May 25.
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, P=0.004), QSM kurtosis (β=-5×10, P=0.058), QSM entropy (β=-0.067, P=0.054), maximum ADC (β=-1.6×10, 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 percentile of ADC (r=-0.275, P=0.032), and 50 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.
背景与目的
术前预测高级别脑膜瘤的能力对于临床手术规划很重要。本研究旨在评估综合多参数 MRI（包括磁敏感加权成像 [SWI] 和定量磁化率映射 [QSM]）在定性和定量方面预测高级别脑膜瘤的性能。
方法
本研究纳入了 129 名患者的 92 例低级别和 37 例高级别脑膜瘤。使用单变量和多变量分析评估形态特征、QSM 和 ADC 图像的定量直方图分析以及肿瘤大小，以预测高级别脑膜瘤。对形态特征进行接收者操作特征（ROC）分析。使用 Pearson 相关分析计算 Ki-67 增殖指数（PI）与定量参数之间的相关性。
结果
在多变量逻辑回归分析中，用于预测高级别脑膜瘤的最佳预测模型包括钙化（β=0.874，P=0.110）、瘤周水肿（β=0.554，P=0.042）、肿瘤边界（β=0.862，P=0.024）、肿瘤位置（β=0.545，P=0.039）等形态特征，以及肿瘤大小（β=4×10，P=0.004）、QSM 峰度（β=-5×10，P=0.058）、QSM 熵（β=-0.067，P=0.054）、最大 ADC（β=-1.6×10，P=0.003）、ADC 峰度（β=-0.013，P=0.014）等定量特征。形态特征的 ROC 分析得到的曲线下面积（AUC）为 0.71（0.61-0.81）。Ki-67 PI 与平均 ADC（r=-0.277，P=0.031）、ADC 第 25 百分位数（r=-0.275，P=0.032）和 ADC 第 50 百分位数（r=-0.268，P=0.037）之间存在显著相关性。
结论
虽然 SWI 和 QSM 并未改善低级别和高级别脑膜瘤之间的鉴别，但结合形态特征和定量指标有助于预测高级别脑膜瘤。
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