A new method for quantification and 3D visualization of brain tumor adhesion using slip interface imaging in patients with meningiomas.

OBJECTIVES

To develop an objective quantitative method to characterize and visualize meningioma-brain adhesion using MR elastography (MRE)-based slip interface imaging (SII).

METHODS

This retrospective study included 47 meningiomas (training dataset: n = 35; testing dataset: n = 12) with MRE/SII examinations. Normalized octahedral shear strain (NOSS) values were calculated from the acquired MRE displacement data. The change in NOSS at the tumor boundary (ΔNOSS) was computed, from which a 3D ΔNOSS map of the tumor surface was created and the probability distribution of ΔNOSS over the entire tumor surface was calculated. Statistical features were calculated from the probability histogram. After eliminating highly correlated features, the capability of the remaining feature for tumor adhesion classification was assessed using a one-way ANOVA and ROC analysis.

RESULTS

The magnitude and location of the tumor adhesion can be visualized by the reconstructed 3D ΔNOSS surface map. The entropy of the ΔNOSS histogram was significantly different between adherent tumors and partially/completely non-adherent tumors in both the training (AUC: 0.971) and testing datasets (AUC: 0.900). Based on the cutoff values obtained from the training set, the ΔNOSS entropy in the testing dataset yielded an accuracy of 0.83 for distinguishing adherent versus partially/non-adherent tumors, and 0.67 for distinguishing non-adherent versus completely/partially adherent tumors.

CONCLUSIONS

SII-derived ΔNOSS values are useful for quantification and classification of meningioma-brain adhesion. The reconstructed 3D ΔNOSS surface map presents the state and location of tumor adhesion in a "clinician-friendly" manner, and can identify meningiomas with a high risk of adhesion to adjacent brain parenchyma.

KEY POINTS

• MR elastography (MRE)-based slip interface imaging shows promise as an objective tool to preoperatively discriminate meningiomas with a high risk of intraoperative adhesion. • Measurement of the change of shear strain at meningioma boundaries can provide quantitative metrics depicting the state of adhesion at the tumor-brain interface. • The surface map of tumor adhesion shows promise in assisting precise adhesion localization, using a comprehensible, "clinician-friendly" 3D visualization.