1Department of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Switzerland.
2Institute of Neuropathology, Medical Center-University of Freiburg, Germany.
J Neurosurg. 2022 Apr 29;137(6):1639-1649. doi: 10.3171/2022.3.JNS212626. Print 2022 Dec 1.
The management of asymptomatic intracranial meningiomas is controversial. Through the assessment of growth predictors, the authors aimed to create the basis for practicable clinical pathways for the management of these tumors.
The authors volumetrically analyzed meningiomas radiologically diagnosed at their institution between 2003 and 2015. The primary endpoint was growth of tumor volume. The authors used significant variables from the multivariable regression model to construct a decision tree based on the exhaustive Chi-Square Automatic Interaction Detection (CHAID) algorithm.
Of 240 meningiomas, 159 (66.3%) demonstrated growth during a mean observation period of 46.9 months. On multivariable logistic regression analysis, older age (OR 0.979 [95% CI 0.958-1.000], p = 0.048) and presence of calcification (OR 0.442 [95% CI 0.224-0.872], p = 0.019) had a negative predictive value for tumor growth, while T2-signal iso-/hyperintensity (OR 4.415 [95% CI 2.056-9.479], p < 0.001) had a positive predictive value. A decision tree model yielded three growth risk groups based on T2 signal intensity and presence of calcifications. The median tumor volume doubling time (Td) was 185.7 months in the low-risk, 100.1 months in the intermediate-risk, and 51.7 months in the high-risk group (p < 0.001). Whereas 0% of meningiomas in the low- and intermediate-risk groups had a Td of ≤ 12 months, the percentage was 8.9% in the high-risk group (p = 0.021).
Most meningiomas demonstrated growth during follow-up. The absence of calcifications and iso-/hyperintensity on T2-weighted imaging offer a practical way of stratifying meningiomas as low, intermediate, or high risk. Small tumors in the low- or intermediate-risk categories can be monitored with longer follow-up intervals.
无症状颅内脑膜瘤的治疗存在争议。通过评估生长预测因子，作者旨在为这些肿瘤的治疗建立可行的临床路径基础。
作者对 2003 年至 2015 年间在其机构诊断的脑膜瘤进行了容积分析。主要终点是肿瘤体积的生长。作者使用多变量回归模型中的显著变量，基于穷举 Chi-Square 自动交互检测（CHAID）算法构建决策树。
在 240 例脑膜瘤中，159 例（66.3%）在平均 46.9 个月的观察期内显示生长。多变量逻辑回归分析显示，年龄较大（比值比 0.979[95%置信区间 0.958-1.000]，p=0.048）和存在钙化（比值比 0.442[95%置信区间 0.224-0.872]，p=0.019）对肿瘤生长具有负预测值，而 T2 信号等/高信号（比值比 4.415[95%置信区间 2.056-9.479]，p<0.001）具有正预测值。决策树模型根据 T2 信号强度和钙化的存在生成了三个生长风险组。低风险组的中位肿瘤倍增时间（Td）为 185.7 个月，中风险组为 100.1 个月，高风险组为 51.7 个月（p<0.001）。低风险和中风险组中，没有肿瘤的 Td 为≤12 个月，而高风险组中，这一比例为 8.9%（p=0.021）。
大多数脑膜瘤在随访期间显示生长。T2 加权成像上无钙化和等/高信号提供了一种实用的方法，可将脑膜瘤分为低、中、高风险。低风险或中风险类别中的小肿瘤可以通过更长的随访间隔进行监测。
