OBJECTIVE:Spinal meningiomas (SMs) are common primary spinal tumors for which surgery is considered the first-line treatment when safe and feasible. The ability to extrapolate the tumor grade from preoperative imaging may significantly inform early patient expectation-setting regarding recurrence. Building on radiomics studies in cranial meningiomas, the authors aimed to construct a benchmark radiomics model to preoperatively identify the histological grade of SMs. METHODS:Institutional surgical records from May 2012 to November 2025 were queried for pathology-confirmed meningiomas below the foramen magnum, with preoperative contrast-enhanced imaging available for segmentation. SMs were classified as low-grade (WHO grade 1) and high-grade (WHO grade 2 tumors and grade 1 tumors with atypia). Tumors were manually segmented, and features were extracted using the PyRadiomics software package. An ensemble model of k-nearest neighbors, random forest, and support vector machine classifiers was trained using nested cross-validation on a subset of 10 features to differentiate tumor grades. Clinical data for the cohort were also extracted, and disease control in an adjunctive clinical series was assessed. RESULTS:Seventy-four patients were included in radiomics analysis, with an area under the receiver operating characteristic curve of 0.879 and a mean F1 score of 0.748. The model's top 5 features were all texture features that differed significantly (p < 0.05) across low- and high-grade SMs. These included measures of tumor textural and contrast-enhancement heterogeneity, with overlap with features reported in radiomics models for histological grading of intracranial meningiomas. Fifty-five patients with a median radiographic follow-up of 22.2 (range 1.9-86.4) months remained for clinical analysis after exclusion of patients with less than 1 month of follow-up and syndromic meningiomas. Four recurrences occurred at a median of 20.8 (range 1.8-41.8) months. High-grade tumor pathology did not significantly impact progression-free survival (p = 0.682, log-rank test; Cox regression high vs low grade hazard ratio [HR] 0.62, 95% CI 0.06-6.11, p = 0.685). Subtotal resection was associated with poorer progression-free survival than gross-total resection (p = 0.004, log-rank test; Cox regression subtotal vs gross-total resection HR 10.62, 95% CI 1.46-77.05, p = 0.019). These findings remain contextualized within a relatively limited follow-up window and small recurrence event count, suggesting a need to characterize the interplay between tumor grade and extent of resection as drivers of local disease control in SMs. CONCLUSIONS:A preoperative radiomics model can stratify high-grade SMs using open-source tools applied to single-institution data.
OBJECTIVE:The objective was to evaluate the Superior Sagittal Sinus Involvement Grading (SSIG) system as an imaging-based surgical stratification framework for parasagittal meningiomas adjacent to the superior sagittal sinus (SSS) and to assess its relationship with established sinus invasion grading, venous sinus patency, and operative strategy. METHODS:In this single-center retrospective cohort study, the authors included 62 consecutive parasagittal meningioma resections performed by a single surgeon. SSIG grade was assigned primarily on contrast-enhanced coronal MRI, with CT/MR venography used when available to evaluate sinus patency and collateral venous drainage. Operative variables, resection strategy, and clinicopathological factors were compared across SSIG and Sindou grades, and postoperative complications were compared between low- and high-involvement SSIG groups. RESULTS:SSIG correlated significantly with Sindou grade (rs = 0.790, τb = 0.702, both p < 0.001), and among patients with available venous imaging, it also correlated with the venous sinus involvement grade (rs = 0.742, τb = 0.665, both p < 0.001). With increasing SSIG grade, operative time, intraoperative blood loss, and intraoperative fluid administration increased (p = 0.012, p = 0.008, and p = 0.007, respectively). Compared with the low-involvement group (SSIG grades 1, 2, and 4a), the high-involvement group (SSIG grades 3, 4b, and 5) was less likely to achieve Simpson grade I resection and more likely to adopt Simpson grades II-III strategies (66.7% vs 13.6%, p < 0.001; OR 12.667). Surgery-related complication rates did not differ significantly between groups. The mean follow-up was 13.3 ± 7.9 months, with no radiographic recurrence or progression at last follow-up. CONCLUSIONS:SSIG characterizes parasagittal meningiomas by integrating sinus invasion, venous patency, falcine extension, and parasagittal convexity involvement on preoperative imaging. This surgically oriented framework may help anticipate operative exposure, sinus handling, and resection strategy. Its predictive value for complications and long-term oncological outcomes requires validation in larger cohorts with longer follow-up.
Meningioma risk stratification has historically been defined by the Simpson grade of resection and the histological grade of tumor tissue. Recent scientific advances have elucidated the molecular architecture of meningioma, and multiple groups have developed molecular classification systems that refine meningioma risk stratification. Reported meningioma molecular classification systems use DNA methylation or gene expression profiling, copy number alterations, DNA mutations, or multiple molecular and histological variables combined into individual models. While the details of meningioma molecular classification systems may vary, concordance is observed across unsupervised systems that are not trained on clinical outcomes, suggesting conserved tumor biology. Moreover, some molecular classification systems outperform the WHO grade in risk stratification and can identify meningiomas that may benefit from radiotherapy or targeted therapies. According to this emerging paradigm, patients with molecular high-risk meningioma may be guided toward postoperative radiotherapy or targeted therapies. In contrast, patients with molecular low-risk meningioma may be followed with serial postoperative imaging in the right clinical context. Practical and logistical barriers must be overcome to implement meningioma molecular classification into clinical practice, and prospective randomized trials are needed to validate emerging molecular risk stratification systems and define the subsets of patients who might benefit the most from their use. In the interim, clinicians are increasingly recognizing the value of meningioma molecular classification and are already implementing these approaches into the postoperative management of patients with meningioma.
OBJECTIVE:Hyperostosing spheno-orbital meningiomas (SOMs) infiltrate bone and cause bone overgrowth leading to progressive visual and cosmetic deformities. DOTA0-Tyr3-octreotate (DOTATATE) is a somatostatin receptor-targeted PET ligand that enables highly sensitive identification of meningiomas. This ligand is useful in defining the extent of bone infiltration by the meningioma, which can be difficult to detect on standard CT and MRI. In this study, the authors aimed to provide a pragmatic technical framework for integrating molecular imaging, navigation, and patient-specific reconstruction in the management of complex SOMs by reporting their experience designing custom spheno-orbital implants based on DOTATATE-PET for skull and orbital reconstruction, as well as to report patient outcomes. METHODS:Four patients with SOMs who underwent cranio-orbital resection and rigid orbital reconstruction with custom polyetheretherketone patient-specific implants (PSIs) at a single center between 2023 and 2025 were retrospectively reviewed. Three patients had DOTATATE-PET/CT data available, and all had undergone CT and MRI to delineate tumor extent and guide implant design. All surgical procedures involved a multidisciplinary neurosurgery-oculoplastics team. The exophthalmos index was measured pre- and postoperatively. RESULTS:All patients underwent tumor resection with a single PSI for skull and orbital reconstruction. Gross-total resection was achieved in 1 patient. Radiotracer-defined transosseous tumor involvement extended beyond the structural hyperostosis seen on CT and MRI and directly informed both the planned bony resection and PSI design. Visual outcomes stabilized or improved in all patients; 1 pediatric patient with neurofibromatosis type 2 (NF2) had persistent poor vision despite decompression due to significant tumor involvement at the orbital apex. The exophthalmos index improved in all patients, with a return to normal globe position in 3 patients and persistent proptosis due to intraorbital residual tumor in 1 patient. One patient developed transient diplopia and a pseudomeningocele, which resolved with conservative management. There were no enophthalmos or implant-related complications. CONCLUSIONS:Integrating DOTATATE-PET with custom cranio-orbital PSIs allows single implant reconstruction of combined skull and orbital defects after resection of hyperostosing SOMs. DOTATATE-PET is useful for defining transosseous tumor extent and informing implant design. By enlarging the orbital volume with the implant compared with the contralateral normal side, excellent correction of proptosis can be achieved.
OBJECTIVE:The authors aimed to systematically investigate the expression and subcellular localization of estrogen receptors ERα and GPER in meningioma and to evaluate the functional impact of estradiol on cell viability in common meningioma cell lines and primary patient-derived cultures. METHODS:Tissue microarrays (TMAs) representing 120 patients (82 female, median age at diagnosis 57 years) with meningiomas (564 regions of interest) were analyzed using quantitative digital image analysis (QuPath) to evaluate nuclear and cytoplasmic receptor density. Subcellular localization was validated via immunofluorescence in meningeal and meningioma cell lines. Functional responsiveness to 17β-estradiol (E2) was assessed using in vitro cell viability assays with common meningioma cell lines Ben-Men-1, CH157-MN, IOMM-Lee, human meningeal cells, and primary cultured dura. RESULTS:GPER was ubiquitously expressed across the cohort, exhibiting both cytoplasmic and nuclear localization, whereas ERα detection was comparatively limited. Multivariable linear regression analysis identified the anatomical location as a significant independent predictor of GPER expression. Non-skull base tumors demonstrated higher GPER staining intensity compared with that of skull base tumors (nuclear, p = 0.013; cytoplasmic, p = 0.011). GPER staining intensity also varied across genomic alteration subgroups, with lower intensity observed in the TRAF7 ± other subgroup compared with NF2/22q-loss tumors. In vitro experiments revealed a biphasic response to E2 in which low-dose exposure (1-10 pM) was frequently associated with reduced viability, whereas higher concentrations (10-100 nM) increased cell viability. These effects varied across cell lines, with the NF2-loss CH157-MN meningioma cell line demonstrating the most pronounced increase in viability at 100 nM (adjusted p = 0.0002). CONCLUSIONS:These findings provide a systematic protein-level characterization of estrogen receptor expression in meningioma, identify GPER as a predominant receptor with expression patterns associated with tumor location and genomic subgroup, and lay the foundation for future mechanistic and translational studies.
