Preoperative Prediction of Meningioma Consistency, Adhesion, and Biomarker Status Using Whole-Tumor MR Elastography Histogram Analysis.

RATIONALE AND OBJECTIVES: To evaluate the use of whole-tumor histogram analysis of magnetic resonance elastography (MRE) for preoperative prediction of meningioma stiffness, adhesion, histopathological subtypes, expression of prognosis-related biomarkers, and surgical outcomes. MATERIALS AND METHODS: This prospective study enrolled 56 patients who underwent preoperative MRE to derive shear wave speed (c-map) and phase angle (φ-map). Whole-tumor histogram features were extracted. Intraoperative tumor stiffness and adhesion were graded according to the Zada and Yin classification systems, respectively. Expression of extracellular matrix (ECM) and prognostic biomarkers was evaluated. Correlation tests, receiver operating characteristic curves, and logistic regression were further performed. RESULTS: The maximum c-map value excellently predicted intraoperative tumor stiffness (area under the curve [AUC] = 0.956, specificity 100%) and was identified as an independent predictor on multivariate analysis (odds ratio 1.134, P = 0.007). The combined clinical-imaging model for predicting stiffness achieved 96.43% accuracy. For adhesion, the minimum φ-map value showed predictive utility (AUC = 0.722), which was significantly enhanced in a multimodal model (AUC = 0.901, P < 0.05). Furthermore, MRE parameters correlated with ECM biomarkers (collagen, α‑SMA, Piezo1; P < 0.05) and effectively differentiated histologic subtypes. The φ-map Perc. 10 serves as a prognostic parameter for Ki‑67 expression (AUC = 0.701), while the minimum φ‑map value from MRE predicts microsurgical time (AUC = 0.737) and blood loss (AUC = 0.763). CONCLUSION: Whole-tumor MRE histogram analysis serves as a comprehensive, noninvasive preoperative tool for meningiomas, effectively predicting stiffness, adhesion, histologic subtype, and biomarker expression to inform surgical risk stratification and planning.