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J Clin Oncol. 2003 Sep 1;21(17):3285-95.

New classification scheme for the prognostic stratification of meningioma on the basis of chromosome 14 abnormalities, patient age, and tumor histopathology.

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Neurosurgery Service, Hospital Universitario de Salamanca, Salamanca, Spain.



Meningiomas are usually considered benign tumors. However, relapses occur in 10% to 20% of all patients, including both histopathologically aggressive and benign tumors. This study explored the value of numerical abnormalities for 10 different chromosomes in meningiomas for predicting relapse-free survival (RFS).


This study prospectively analyzed the frequency of numerical abnormalities of chromosomes 1, 9, 10, 11, 14, 15, 17, 22, X, and Y in 70 meningioma patients by fluorescence in situ hybridization and their relationship with disease characteristics at diagnosis and patients' outcome.


Results showed the presence of numerical abnormalities for one or more chromosomes in most patients (77%). Chromosome 22 in the whole series and chromosome Y in males were those more frequently altered, followed by chromosomes 1, 14, and X in females. Patients with abnormalities of chromosomes 1, 9, 10, 11, 14, 15, 17, the sex chromosomes, and gains of chromosome 22 were associated with adverse prognostic features, more frequent relapses, and shorter RFS. Multivariate analysis showed that tumor grade together with chromosome 14 status and age were the best combination of independent variables for predicting RFS. According to these variables, all patients with a score of two or more than two adverse prognostic factors had experienced relapse at 5 years, whereas none of those with a score of zero had experienced relapse 10 years after surgery.


In addition to age and histologic grade, abnormalities of chromosome 14 contribute to a better prognostic stratification of meningioma patients at diagnosis. Additional prospective studies in larger series of patients, also including larger numbers of patients who experienced relapse, are necessary to confirm the utility of the proposed predictive model.

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