Status in Brain Gliomas Can Be Predicted by the Spherical Mean MRI Technique.

BACKGROUND AND PURPOSE

Diffuse gliomas, a heterogeneous group of primary brain tumors, have traditionally been stratified by histology, but recent insights into their molecular features, especially the mutation status, have fundamentally changed their classification and prognosis. Current diagnostic methods, still predominantly relying on invasive biopsy, necessitate the exploration of noninvasive imaging alternatives for glioma characterization.

MATERIALS AND METHODS

In this prospective study, we investigated the utility of the spherical mean technique (SMT) in predicting the status and histologic grade of adult-type diffuse gliomas. Patients with histologically confirmed adult-type diffuse glioma underwent a multiparametric MRI examination using a 3T system, which included a multishell diffusion sequence. Advanced diffusion parameters were obtained using SMT, diffusional kurtosis imaging, and ADC modeling. The diagnostic performance of studied parameters was evaluated by plotting receiver operating characteristic curves with associated area under curve, specificity, and sensitivity values.

RESULTS

A total of 80 patients with a mean age of 48 (SD, 16) years were included in the study. SMT metrics, particularly microscopic fractional anisotropy (μFA), intraneurite voxel fraction, and μFA to the third power (μFA), demonstrated strong diagnostic performance (all AUC = 0.905, 95% CI, 0.835-0.976; < .001) in determining status and compared favorably with diffusional kurtosis imaging and ADC models. These parameters also showed a strong predictive capability for tumor grade, with intraneurite voxel fraction and μFA achieving the highest diagnostic accuracy (AUC = 0.937, 95% CI, 0.880-0.993; < .001). Control analyses on normal-appearing brain tissue confirmed the specificity of these metrics for tumor tissue.

CONCLUSIONS

Our study highlights the potential of SMT for noninvasive characterization of adult-type diffuse gliomas, with a potential to predict status and tumor grade more accurately than traditional ADC metrics. SMT offers a promising addition to the current diagnostic toolkit, enabling more precise preoperative assessments and contributing to personalized treatment planning.