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 IDH 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 IDH 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 (μFA3), demonstrated strong diagnostic performance (all AUC = 0.905, 95% CI, 0.835-0.976; P < .001) in determining IDH 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; P < .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 IDH 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.

Department of Biomedical Engineering and Assistive Technology Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Prague Czech Republic

Department of Diagnostic Medical Physics Karolinska University Hospital Solna Stockholm Sweden

Department of Neuroimaging Centre for Neuroimaging Science Institute of Psychiatry Psychology and Neuroscience King's College London London UK

Department of Neurosurgery and Neurooncology 1st Faculty of Medicine Charles University and Military University Hospital Prague Czech Republic

Department of Neurosurgery and Spine Surgery Arberlandklinik Viechtach Germany

Division of Clinical Geriatrics Department of Neurobiology Care Sciences and Society Center for Alzheimer Research Karolinska Institute Stockholm Sweden

From the Department of Radiology Military University Hospital Prague Czech Republic

Radiodiagnostic Department Proton Therapy Center Czech Ltd Prague Czech Republic

AJNR Am J Neuroradiol. 2025 Mar 4;46(3):644. doi: 10.3174/ajnr.A8666. PubMed

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