Chen Xiaodan, Lin Lin, Wu Jie, Yang Guang, Zhong Tianjin, Du Xiaoqiang, Chen Zhiyong, Xu Ganggang, Song Yang, Xue Yunjing, Duan Qing
Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, PR China.
Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, PR China.
Acta Radiol. 2020 Sep;61(9):1228-1239. doi: 10.1177/0284185119898656. Epub 2020 Jan 27.
Presurgical grading is particularly important for selecting the best therapeutic strategy for meningioma patients.
To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the differentiation of grades and histological subtypes of meningiomas.
A total of 172 patients with histopathologically proven meningiomas underwent preoperative magnetic resonance imaging (MRI) and were classified into low-grade and high-grade groups. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) histograms were generated based on solid components of the whole tumor. The following parameters of each histogram were obtained: 10th, 25th, 75th, and 90th percentiles, mean, median, maximum, minimum, and kurtosis, skewness, and variance. Comparisons of different grades and subtypes were made by Mann-Whitney U test, Kruskal-Wallis test, ROC curves analysis, and multiple logistic regression. Pearson correlation was used to evaluate correlations between histogram parameters and the Ki-67 labeling index.
Significantly higher maximum, skewness, and variance of MD, mean, median, maximum, variance, 10th, 25th, 75th, and 90th percentiles of MK were found in high-grade than low-grade meningiomas (all < 0.05). DKI histogram parameters differentiated 7/10 pairs of subtype pairs. The 90th percentile of MK yielded the highest AUC of 0.870 and was the only independent indicator for grading meningiomas. Various DKI histogram parameters were correlated with the Ki-67 labeling index ( < 0.05).
The histogram analysis of DKI is useful for differentiating meningioma grades and subtypes. The 90th percentile of MK may serve as an optimal parameter for predicting the grade of meningiomas.
术前分级对于为脑膜瘤患者选择最佳治疗策略尤为重要。
探讨扩散峰度成像（DKI）图的直方图分析在脑膜瘤分级及组织学亚型鉴别中的价值。
172例经组织病理学证实的脑膜瘤患者术前行磁共振成像（MRI）检查，并分为低级别和高级别组。基于整个肿瘤的实性成分生成平均峰度（MK）、各向异性分数（FA）和平均扩散率（MD）直方图。获取每个直方图的以下参数：第10、25、75和90百分位数、均值、中位数、最大值、最小值以及峰度、偏度和方差。采用Mann-Whitney U检验、Kruskal-Wallis检验、ROC曲线分析和多元逻辑回归对不同级别和亚型进行比较。采用Pearson相关性分析评估直方图参数与Ki-67标记指数之间的相关性。
高级别脑膜瘤的MD的最大值、偏度和方差、MK的均值、中位数、最大值、方差、第10、25、75和90百分位数均显著高于低级别脑膜瘤（均P<0.05）。DKI直方图参数可鉴别7/10对亚型。MK的第90百分位数的AUC最高，为0.870，是脑膜瘤分级的唯一独立指标。各种DKI直方图参数与Ki-67标记指数相关（P<0.05）。
DKI的直方图分析有助于鉴别脑膜瘤的级别和亚型。MK的第90百分位数可作为预测脑膜瘤级别的最佳参数。
