2026 Proffered Presentations
S150: DEVELOPMENT OF A PREDICTIVE MODEL OF MENINGIOMA CONSISTENCY USING MAGNETIC RESONANCE IMAGING (MRI).
Célina Maguemoun1; Samira Ravanbakhsh, Ing, Phd1; Malek El-Fekih1; Théophraste Lescot, Ing, MSc1; Martin Côté, MD2; Marc-André Fortin, Ing, PhD1; Pierre-Olivier Champagne, MD, PhD2; 1Laboratoire de Biomatériaux pour l'Imagerie Médicale, Axe Médecine Régénératrice, Centre de Recherche du Centre Hospitalier Universitaire de Québec - Université Laval, Québec, QC, Canada; 2Département de Neurochirurgie, Université Laval, Québec, QC, Canada
Meningiomas are the most common intracranial tumors, representing nearly one-third of primary central nervous system neoplasms. Surgical resection remains the only curative treatment, but operative complexity and morbidity largely depend on tumor consistency, defined by its viscoelastic properties. Soft tumors can be aspirated easily, whereas fibrous collagen-rich tumors, often firm and encasing neurovascular structures, increase operative time, bleeding risk, and morbidity. Currently, intraoperative tactile impression is the standard for assessing consistency, but it is subjective and only available during surgery. This creates a need for reliable preoperative predictors.
Magnetic resonance imaging (MRI) is the most widely used modality for meningioma evaluation. Qualitative T2-weighted imaging (T2WI) has shown correlations with firmness, with hyperintense tumors generally soft and hypointense tumors more fibrous. Quantitative T2 mapping improves on this approach by providing absolute relaxation times through multi-echo acquisitions and exponential fitting. Lower T2 values typically indicate firm meningiomas, while higher values correspond to soft tumors, also capturing intratumoral heterogeneity.
Histopathological data support these observations: collagen-rich tumors usually display short T2 values, whereas edematous or necrotic regions appear hyperintense. Most prior studies, however, relied on subjective intraoperative impressions, limiting reproducibility. Semi-quantitative methods, such as tumor-to-reference intensity ratios, have been proposed but remain indirect. Quantitative T2 mapping offers a more objective alternative. Mechanical validation with rheometers has confirmed the link between collagen and T2 hypointensity, but bulk measurements fail to capture intratumoral heterogeneity. Radiomics and deep learning applied to multiparametric MRI have achieved accuracies above 80%, correctly classifying tumor consistency in many cases. Diffusion imaging has also shown potential, but T2-based methods remain the most accessible and reproducible. Overall, quantitative T2 mapping is a strong candidate for preoperative prediction, though no standardized method currently exists.
To address this gap, we developed a predictive framework integrating preoperative MRI and microscopic validation. Multi-echo T2 datasets were collected from 20 patients undergoing meningioma resection. Voxel-wise T2 maps were generated, and regions with distinct values were selected as regions of interest. Corresponding tumor samples were cryosectioned, lyophilized, and imaged with a high-resolution digital microscope (Keyence VHX-7000) to produce three-dimensional topographical maps. At each region, residual material height was measured, while grayscale intensity was extracted from microscopic images using standardized analysis software (GIMP). These metrics were correlated with MRI-derived T2 relaxation times. Automated Python mapping generates voxel-wise T2 values, providing a reproducible basis for linking MRI data with microscopic measurements.
Preliminary analyses show that regions with low T2 values (60-100 ms) correspond to areas with greater residual material height (30-40 µm) and higher optical density on microscopy (≈0.43). Conversely, regions with higher T2 values (200-300 ms) correspond to reduced residual material (5-20 µm) and lower optical density (≈0.28). Selecting regions with distinct T2 values and analyzing them microscopically provides direct validation of MRI-based predictions.
By combining quantitative MRI analysis, microscopic topography, signal quantification, and automated voxel-wise classification, this project introduces an innovative approach to predicting meningioma consistency. Such integration could improve preoperative planning, reduce complications, and optimize surgical outcomes.
Keywords: meningioma, tumor consistency, T2 mapping, ROI analysis, microscopic topography, surgical planning