RT Journal Article SR Electronic T1 IDH Status in Brain Gliomas Can Be Predicted by the Spherical Mean MRI Technique JF American Journal of Neuroradiology JO Am. J. Neuroradiol. FD American Society of Neuroradiology SP 121 OP 128 DO 10.3174/ajnr.A8432 VO 46 IS 1 A1 Sedlák, Vojtěch A1 Němý, Milan A1 Májovský, Martin A1 Bubeníková, Adéla A1 Nordin, Love Engstrom A1 Moravec, Tomáš A1 Engelová, Jana A1 Sila, Dalibor A1 Konečná, Dora A1 Belšan, Tomáš A1 Westman, Eric A1 Netuka, David YR 2025 UL http://www.ajnr.org/content/46/1/121.abstract AB 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.AKaxial kurtosisAUCarea under the curveCESTchemical exchange saturation transferDKIdiffusional kurtosis imagingINVFintraneurite voxel fractionμFAmicroscopic fractional anisotropyMKmean kurtosisMKTmean kurtosis tensorRKradial kurtosisROCreceiver operating characteristicSMTspherical mean techniqueTMDtransverse microscopic diffusivityWHOWorld Health Organization