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Jennifer Moliterno, William P. Cope, Emma D. Vartanian, Anne S. Reiner, Roselyn Kellen, Shahiba Q. Ogilvie, Jason T. Huse and Philip H. Gutin

OBJECT

While most meningiomas are benign, 1%–3% display anaplastic features, with little current understanding regarding the molecular mechanisms underlying their formation. In a large single-center cohort, the authors tested the hypothesis that two distinct subtypes of anaplastic meningiomas, those that arise de novo and those that progress from lower grade tumors, exist and exhibit different clinical behavior.

METHODS

Pathology reports and clinical data of 37 patients treated between 1999 and 2012 for anaplastic meningioma at Memorial Sloan–Kettering Cancer Center (MSKCC) were retrospectively reviewed. Patients were divided into those whose tumors arose de novo and those whose tumors progressed from previously documented benign or atypical meningiomas.

RESULTS

Overall, the median age at diagnosis was 59 years and 57% of patients were female. Most patients (38%) underwent 2 craniotomies (range 1–5 surgeries) aimed at gross-total resection (GTR; 59%), which afforded better survival when compared with subtotal resection according to Kaplan-Meier estimates (median overall survival [OS] 3.2 vs 1.3 years, respectively; p = 0.04, log-rank test). Twenty-three patients (62%) presented with apparently de novo anaplastic meningiomas. Compared with patients whose tumors had progressed from a lower grade, those patients with de novo tumors were significantly more likely to be female (70% vs 36%, respectively; p = 0.04), experience better survival (median OS 3.0 vs 2.4 years, respectively; p = 0.03, log-rank test), and harbor cerebral hemispheric as opposed to skull base tumors (91% vs 43%, respectively; p = 0.002).

CONCLUSIONS

Based on this single-center experience at MSKCC, anaplastic meningiomas, similar to glial tumors, can arise de novo or progress from lower grade tumors. These tumor groups appear to have distinct clinical behavior. De novo tumors may well be molecularly distinct, which is under further investigation. Aggressive GTR appears to confer an OS advantage in patients with anaplastic meningioma, and this is likely independent of tumor progression status. Similarly, those patients with de novo tumors experience a survival advantage likely independent of extent of resection.

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H. Isaac Chen, Mark G. Burnett, Jason T. Huse, Robert A. Lustig, Linda J. Bagley and Eric L. Zager

✓Late cerebral radiation necrosis usually occurs within 3 years of stereotactic radiosurgery. The authors report on a case of recurrent radiation necrosis with rapid clinical deterioration and imaging findings resembling those of a malignant glioma. This 68-year-old man, who had a history of a left posterior temporal and thalamic arteriovenous malformation (AVM) treated with linear accelerator radiosurgery 13 years before presentation and complicated by radiation necrosis 11 years before presentation, exhibited new-onset mixed aphasia, right hemiparesis, and right hemineglect. Imaging studies demonstrated hemorrhage and an enlarging, heterogeneously enhancing mass in the region of the previously treated AVM. The patient was treated medically with corticosteroid agents, and stabilized temporarily. Unfortunately, his condition worsened precipitously soon thereafter, requiring the placement of a shunt for relief of obstructive hydrocephalus. Further surgical intervention was offered, but the patient’s family opted for hospice care instead. The patient died 10 weeks after initially presenting to the authors’ institution, and the results of an autopsy demonstrated radiation necrosis.

Symptomatic radiation necrosis can occur more than a decade after stereotactic radiosurgery, necessitating patient follow up during a longer period of time than currently practiced. Furthermore, there is a need for more careful reporting on the natural history of such cases to clarify the pathogenesis of very late and recurrent radiation necrosis after radiosurgery and to define patient groups with a higher risk for these entities.

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Robert G. Whitmore, Jaroslaw Krejza, Gurpreet S. Kapoor, Jason Huse, John H. Woo, Stephanie Bloom, Joanna Lopinto, Ronald L. Wolf, Kevin Judy, Myrna R. Rosenfeld, Jaclyn A. Biegel, Elias R. Melhem and Donald M. O'rourke

Object

Treatment of patients with oligodendrogliomas relies on histopathological grade and characteristic cytogenetic deletions of 1p and 19q, shown to predict radio- and chemosensitivity and prolonged survival. Perfusion weighted magnetic resonance (MR) imaging allows for noninvasive determination of relative tumor blood volume (rTBV) and has been used to predict the grade of astrocytic neoplasms. The aim of this study was to use perfusion weighted MR imaging to predict tumor grade and cytogenetic profile in oligodendroglial neoplasms.

Methods

Thirty patients with oligodendroglial neoplasms who underwent preoperative perfusion MR imaging were retrospectively identified. Tumors were classified by histopathological grade and stratified into two cytogenetic groups: 1p or 1p and 19q loss of heterozygosity (LOH) (Group 1), and 19q LOH only on intact alleles (Group 2). Tumor blood volume was calculated in relation to contralateral white matter. Multivariate logistic regression analysis was used to develop predictive models of cytogenetic profile and tumor grade.

Results

In World Health Organization Grade II neoplasms, the rTBV was significantly greater (p < 0.05) in Group 1 (mean 2.44, range 0.96–3.28; seven patients) compared with Group 2 (mean 1.69, range 1.27–2.08; seven patients). In Grade III neoplasms, the differences between Group 1 (mean 3.38, range 1.59–6.26; four patients) and Group 2 (mean 2.83, range 1.81–3.76; 12 patients) were not significant. The rTBV was significantly greater (p < 0.05) in Grade III neoplasms (mean 2.97, range 1.59–6.26; 16 patients) compared with Grade II neoplasms (mean 2.07, range 0.96–3.28; 14 patients). The models integrating rTBV with cytogenetic profile and grade showed prediction accuracies of 68 and 73%, respectively.

Conclusions

Oligodendroglial classification models derived from advanced imaging will improve the accuracy of tumor grading, provide prognostic information, and have potential to influence treatment decisions.