OBJECTIVE 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. METHODS 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. RESULTS 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). CONCLUSIONS 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.

Background The management of asymptomatic intracranial meningiomas is controversial. Through the assessment of growth predictors, we aimed to create the basis for practicable clinical pathways for the management of these tumors. Material and Methods We volumetrically analyzed meningiomas radiologically diagnosed at our institution between 2003 and 2015. For this purpose, we used exclusively thin-layered MR images (i.e. ≤ 2mm slice thickness). The primary endpoint was tumor growth defined as a 14.35% increase in tumor volume. We identified predictive clinical and radiological characteristics and used the significant variables from a multivariable regression model to construct a decision tree based on the exhaustive Chi-squared Automatic Interaction Detection (exhaustive CHAID) algorithm. Results 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 (0.958-1.000), p=0.048) and presence of calcification (OR=0.442 (0.224-0.872), p=0.019) had a negative predictive value for tumor growth, while T2-signal iso-/hyperintensity (OR=4.415 (2.056-9.479), p&lt;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 with a proportion of growing tumors of 34.1% in the low risk group, 60.0% in the intermediate risk group and 80.2% in the high risk group. 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&lt;0.001). While 0% of meningiomas in the low and intermediate risk group had a Td of ≤12 months, 8.9% in the high risk group did so (p=0.021). Conclusion Most meningiomas demonstrated growth during follow-up. The presence or absence of calcifications and the signal intensity on T2-weighted imaging allow a practical and simple stratification of meningiomas into low, intermediate and high risk tumors. Small tumors in the low or intermediate risk categories can be monitored with longer follow-up intervals, whereas in the high risk category proactive management decisions can be justified.