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Magnetic resonance elastography characterization of meningioma mechanical properties for improved neurosurgical planning and resection: a prospective cohort study

Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Accurate characterization of tumor stiffness and adhesion is crucial when surgically resecting meningiomas. Currently, such determinations are difficult to ascertain through standard preoperative imaging diagnostics and clinical experience. Herein, we explored magnetic resonance elastography (MRE) as a means of assessing these parameters and thereby improving neurosurgical resections. METHODS: This prospective study of 193 patients with meningiomas involved three distinct patient subsets (cohort 1, 88 [2017-2021]; cohort 2, 55 [2022-2023]; cohort 3, 50 [2023-2024]. We analyzed preoperatively obtained MRE data on meningioma stiffness (cohorts 1 and 2) and adhesion (cohort 2) for accuracy, while matching subjects with or without MRE studies (cohort 3, 1:1 ratio) to define the role of preoperative MRE in improving operative outcomes. RESULTS: Areas under the curve (AUCs) for MRE in gauging meningioma stiffness and adhesion were 0.87 and 0.89, respectively. Both parameters significantly surpassed empiric predictions of stiffness and adhesion rendered by surgeons and radiologists, based on routine magnetic resonance imaging (MRI). MRE-determined stiffness measures independently correlated with prolonged resection duration, whereas MRE-assessed adhesion showed independent associations with increased blood loss, longer duration of tumor resection, and higher risk of pial injury. Ultimately, results based on cohort 3 demonstrated that MRE has great potential in this setting to improve neurosurgical procedures by significantly reducing intraoperative bleeding, resection duration, and risks of pial injury. CONCLUSION: MRE is a promising, noninvasive tool for accurately assessing meningioma stiffness and adhesion, standing to improve the precision and safety of needed surgical intervention.
Language: English
Subfield: Epidemiology
Field: Medicine
Open Access status: hybrid
Estimated APC paid: $3,450
OpenAlex ID: W4417226404
FWCI: 0.61
Cites: 30
Cited by: 1
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