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J Neurosurg. 2017 Nov;127(5):971-980. doi: 10.3171/2016.9.JNS161669. Epub 2017 Jan 13.

A novel weighted scoring system for estimating the risk of rapid growth in untreated intracranial meningiomas.

Author information

1
Department of Neurological Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul; and.
2
Department of Neurosurgery, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea.

Abstract

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 cm3/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 cm3 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.

KEYWORDS:

AGR = absolute growth rate; AIMSS = Asan Intracranial Meningioma Scoring System; AUC = area under the receiver operating characteristic curve; CT = computed tomography; IM = intracranial meningioma; RGR = relative growth rate; SI = signal intensity; T2W-MRI = T2-weighted MRI; factor analysis; incidental finding; meningioma; natural history; oncology; risk assessment

PMID:
28084908
DOI:
10.3171/2016.9.JNS161669

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