Preoperative assessment of meningioma consistency using a combination of magnetic resonance elastography and diffusion tensor imaging

ABSTRACT

BACKGROUND AND PURPOSE:

Preoperative assessment of meningioma consistency is beneficial for optimizing surgical strategy and prognosis of patients. We aim to develop a non-invasive prediction model for meningioma consistency utilizing magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI).

MATERIALS AND METHODS:

Ninety-four patients (52yr ± 22, 69 females, 25 males) diagnosed with meningioma were recruited in the study. Each patient underwent preoperative T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), DTI, and MRE. Combined MRE-DTI model was developed based on multiple logistic regression. Intraoperative tumor descriptions served as clinical criteria for evaluating meningioma consistency. The diagnostic efficacy in determining meningioma consistency was evaluated using receiver operating characteristic (ROC) curve. Further validation was conducted in twenty-seven stereotactic biopsies using indentation tests and underlying mechanism was investigated by histologic analysis.

RESULTS:

Among all the imaging modalities, MRE demonstrated the highest efficacy with the shear modulus magnitude (|G*|) achieving an area under the curve (AUC) of 0.81 (95% CI: 0.70-0.93). When combined with DTI, the diagnostic accuracy further increased (AUC: 0.88, 95% CI: 0.78-0.97), surpassing any modality alone. Indentation measurement based on stereotactic biopsies further demonstrated that the MRE-DTI model was suitable for predicting intra-tumor consistency. Histological analysis suggested that meningioma consistency may be correlated with tumor cell density and fibrous content.

CONCLUSIONS:

The MRE-DTI combined model is effective in noninvasive prediction of meningioma consistency. ABBREVIATIONS: MRE = magnetic resonance elastography; FA = fractional anisotropy; ROC = receiver operating characteristic; AUC = area under curve.