Diagnostic performance of amide proton transfer-weighted imaging and four advanced diffusion-weighted MRI models for meningioma grading and subtyping.

OBJECTIVE

To compare the diagnostic performance of amide proton transfer-weighted (APTw), diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) in meningioma grading and subtyping.

METHODS

APTw and diffusion spectrum imaging were performed in 110 meningiomas (26 males, 26–84 years), which were classified into 81 low-grade meningiomas (LGMs, grade 1) and 29 high-grade meningiomas (HGMs, grades 2 and 3). Among LGMs, 30 fibrous, 19 meningothelial, and 22 transitional meningiomas were further analyzed. The whole-tumor median values were obtained from APTw and diffusion images. The Mann-Whitney U test and Kruskal-Wallis test were applied for meningioma grading and subtyping, respectively. The receiver operating characteristic (ROC) curve and logistic regression analysis were used to evaluate the diagnostic performance.

RESULTS

The APTw_MTRasym (magnetization transfer-ratio asymmetry), DKI_AK (axial kurtosis), DKI_MK (mean kurtosis), DKI_MKT (mean kurtosis tensor), MAP_RTPP (return-to-plane probability), and NODDI_ODI (orientation dispersion index) were significantly higher, while the DTI_AD (axial diffusivity) and DTI_MD (mean diffusivity) were significantly lower in HGMs than those of LGMs (all p < 0.05). Compared with APTw, DTI, DKI, MAP, and NODDI models, the combined model integrating APTw and four diffusion models showed superior diagnostic performance (AUC [area under the curve] = 0.881, all corrected p < 0.05) in meningioma grading, with improved IDI (integrated discrimination improvement, 0.320–0.360). The combined model integrating APTw_MTRasym and DTI_FA (fractional anisotropy) achieved the highest AUC of 0.981 in differentiating fibrous from meningothelial meningiomas, with increased IDI (0.153–0.494).

CONCLUSION

Integrating APTw-based molecular information with diffusion-model-derived microstructural data represents a promising approach to meningioma grading and subtyping.