Department of Clinical Surgical Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
Neuroradiology. 2020 Nov;62(11):1441-1449. doi: 10.1007/s00234-020-02476-y. Epub 2020 Jun 24.
Meningiomas are mainly benign tumors, though a considerable proportion shows aggressive behaviors histologically consistent with atypia/anaplasia. Histopathological grading is usually assessed through invasive procedures, which is not always feasible due to the inaccessibility of the lesion or to treatment contraindications. Therefore, we propose a multi-parametric MRI assessment as a predictor of meningioma histopathological grading.
Seventy-three patients with 74 histologically proven and previously treated meningiomas were retrospectively enrolled (42 WHO I, 24 WHO II, 8 WHO III) and studied with MRI including T2 TSE, FLAIR, Gradient Echo, DWI, and pre- and post-contrast T1 sequences. Lesion masks were segmented on post-contrast T1 sequences and rigidly registered to ADC maps to extract quantitative parameters from conventional DWI and intravoxel incoherent motion model assessing tumor perfusion. Two expert neuroradiologists assessed morphological features of meningiomas with semi-quantitative scores.
Univariate analysis showed different distributions (p < 0.05) of quantitative diffusion parameters (Wilcoxon rank-sum test) and morphological features (Pearson's chi-square; Fisher's exact test) among meningiomas grouped in low-grade (WHO I) and higher grade forms (WHO II/III); the only exception consisted of the tumor-brain interface. A multivariate logistic regression, combining all parameters showing statistical significance in the univariate analysis, allowed discrimination between the groups of meningiomas with high sensitivity (0.968) and specificity (0.925). Heterogeneous contrast enhancement and low ADC were the best independent predictors of atypia and anaplasia.
Our multi-parametric MRI assessment showed high sensitivity and specificity in predicting histological grading of meningiomas. Such an assessment may be clinically useful in characterizing lesions without histological diagnosis. Key points • When surgery and biopsy are not feasible, parameters obtained from both conventional and diffusion-weighted MRI can predict atypia and anaplasia in meningiomas with high sensitivity and specificity. • Low ADC values and heterogeneous contrast enhancement are the best predictors of higher grade meningioma.
脑膜瘤主要为良性肿瘤，但相当一部分组织学表现为具有不典型/间变特征的侵袭性行为。组织病理学分级通常通过有创性操作进行评估，但由于病变难以接近或治疗禁忌，并非总是可行。因此，我们提出一种多参数 MRI 评估方法，作为脑膜瘤组织病理学分级的预测因子。
回顾性纳入 73 例经组织学证实且已治疗的脑膜瘤患者（42 例 WHO I 级，24 例 WHO II 级，8 例 WHO III 级），并进行 MRI 检查，包括 T2 TSE、FLAIR、梯度回波、DWI 和对比前、后的 T1 序列。在对比后的 T1 序列上对病灶进行分割，并刚性配准到 ADC 图上，以从常规 DWI 和体素内不相干运动模型评估肿瘤灌注的定量参数中提取。两位经验丰富的神经放射科医生使用半定量评分评估脑膜瘤的形态特征。
单变量分析显示，在低级别（WHO I 级）和高级别（WHO II/III 级）脑膜瘤组之间，定量扩散参数（Wilcoxon 秩和检验）和形态特征（Pearson 卡方检验；Fisher 确切检验）的分布不同（p<0.05）；唯一的例外是肿瘤-脑界面。多变量逻辑回归，结合单变量分析中具有统计学意义的所有参数，可将脑膜瘤组进行高灵敏度（0.968）和高特异性（0.925）的区分。不均匀增强和低 ADC 值是不典型性和间变性的最佳独立预测因子。
我们的多参数 MRI 评估方法在预测脑膜瘤的组织学分级方面具有较高的灵敏度和特异性。在没有组织学诊断的情况下，这种评估可能对病变的特征具有临床意义。关键点：
在手术和活检不可行时，常规和弥散加权 MRI 获得的参数可高度敏感和特异性地预测脑膜瘤的不典型性和间变性。
低 ADC 值和不均匀增强是高级别脑膜瘤的最佳预测因子。
