Department of Neurological Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul; and.
Department of Neurosurgery, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea.
J Neurosurg. 2017 Nov;127(5):971-980. doi: 10.3171/2016.9.JNS161669. Epub 2017 Jan 13.
OBJECTIVE Advances in neuroimaging techniques have led to the increased detection of asymptomatic intracranial meningiomas (IMs). Despite several studies on the natural history of IMs, a comprehensive evaluation method for estimating the growth potential of these tumors, based on the relative weight of each risk factor, has not been developed. The aim of this study was to develop a weighted scoring system that estimates the risk of rapid tumor growth to aid treatment decision making. METHODS The authors performed a retrospective analysis of 232 patients with presumed IM who had been prospectively followed up in the absence of treatment from 1997 to 2013. Tumor volume was measured by imaging at each follow-up visit, and the growth rate was determined by regression analysis. Predictors of rapid tumor growth (defined as ≥ 2 cm/year) were identified using a logistic regression model; each factor was awarded a score based on its own coefficient value. The probability (P) of rapid tumor growth was estimated using the following formula:[Formula: see text] RESULTS Fifty-nine tumors (25.4%) showed rapid growth. Tumor size (OR per cm 1.07, p = 0.000), absence of calcification (OR 3.87, p = 0.004), peritumoral edema (OR 2.74, p = 0.025), and hyperintense or isointense signal on T2-weighted MRI (OR 3.76, p = 0.049) were predictors of tumor growth rate. In the Asan Intracranial Meningioma Scoring System (AIMSS), tumor size was categorized into 3 groups of < 2.5 cm, ≥ 2.5 to < 4.0 cm, and ≥ 4.0 cm in diameter and awarded a score of 0, 3, and 6, respectively; the parameters of calcification and peritumoral edema were categorized into 2 groups based on their presence or absence and given a score of 0 or 2 and 1 or 0, respectively; and the signal on T2-weighted MRI was categorized into 2 groups of hypointense and hyperintense/isointense and given a score of 0 or 2, respectively. The risk of rapid tumor growth was estimated to be < 10% when the total score was 0-2, 10%-50% when the total score was 3-6, and ≥ 50% when the total score was 7-11 (Hosmer-Lemeshow goodness-of-fit test, p = 0.9958). The area under the receiver operating characteristic curve was 0.86. CONCLUSIONS The authors suggest a weighted scoring system (AIMSS) that predicts the specific probability of rapid tumor growth for patients with untreated IM. This scoring system will aid treatment decision making in clinical settings by screening out patients at high risk for rapid tumor growth.
目的 神经影像学技术的进步导致无症状颅内脑膜瘤（IM）的检出率增加。尽管有几项关于 IM 自然史的研究，但尚未建立一种基于每个危险因素相对权重的综合评估方法来估计这些肿瘤的生长潜力。本研究旨在建立一种加权评分系统，以估计肿瘤快速生长的风险，从而辅助治疗决策。
方法 作者对 1997 年至 2013 年期间未接受治疗的 232 例疑似 IM 患者进行了回顾性分析。在每次随访时通过影像学测量肿瘤体积，并通过回归分析确定生长速度。使用逻辑回归模型确定快速肿瘤生长（定义为≥2cm/年）的预测因素；根据自身系数值为每个因素分配一个分数。使用以下公式估计快速肿瘤生长的概率（P）：[公式：见正文]
结果 59 个肿瘤（25.4%）显示快速生长。肿瘤大小（每厘米 1.07 的 OR，p=0.000）、无钙化（OR 3.87，p=0.004）、瘤周水肿（OR 2.74，p=0.025）和 T2 加权 MRI 上的高信号或等信号（OR 3.76，p=0.049）是肿瘤生长速度的预测因素。在 Asan 颅内脑膜瘤评分系统（AIMSS）中，肿瘤大小分为<2.5cm、≥2.5cm 至<4.0cm 和≥4.0cm 3 组，分别给予 0、3 和 6 分；钙化和瘤周水肿的参数根据其存在或不存在分为 2 组，分别给予 0 或 2 分和 1 或 0 分；T2 加权 MRI 上的信号分为低信号和高信号/等信号 2 组，分别给予 0 或 2 分。当总分 0-2 时，快速肿瘤生长的风险估计<10%；当总分 3-6 时，快速肿瘤生长的风险估计为 10%-50%；当总分 7-11 时，快速肿瘤生长的风险估计≥50%（Hosmer-Lemeshow 拟合优度检验，p=0.9958）。受试者工作特征曲线下面积为 0.86。
结论 作者提出了一种加权评分系统（AIMSS），用于预测未经治疗的 IM 患者特定的快速肿瘤生长概率。该评分系统通过筛选出快速肿瘤生长风险高的患者，将有助于临床治疗决策。
