Natural history of meningiomas: a serial volumetric analysis of 240 tumors

Pascal ThomannDepartment of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Switzerland;

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Levin HäniDepartment of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Switzerland;

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Sonja VulcuDepartment of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Switzerland;

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Alessa SchützDepartment of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Switzerland;

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Maximilian FroschDepartment of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Switzerland;
Institute of Neuropathology, Medical Center—University of Freiburg, Germany;

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Christopher Marvin JesseDepartment of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Switzerland;

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Marwan El-KoussyDepartment of Radiology and Neuroradiology, Hospital of Emmental, Burgdorf, Switzerland; and

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Nicole SöllDepartment of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Switzerland;

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Arsany HakimInstitute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Switzerland

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Andreas RaabeDepartment of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Switzerland;

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Philippe SchuchtDepartment of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Switzerland;

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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.

ABBREVIATIONS

CHAID = Chi-Square Automatic Interaction Detection; PFS = progression-free survival; RGR = relative annual growth rate; Td = tumor volume doubling time.

Supplementary Materials

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Figure from Kim et al. (pp 1601–1609).

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