Predictive performance of diffusion kurtosis imaging combined with magnetic resonance imaging morphological features for MGMT promoter status in glioma
Year: 2026
Type: article
Abstract: Background: -methylguanine-DNA methyltransferase promoter (MGMTp) status in gliomas. Methods: Sixty-one patients with glioma who underwent biopsy or surgery were included, comprising 39 cases in the MGMTp-methylated group and 22 cases in the MGMTp-unmethylated group. All patients underwent conventional MRI and DKI sequences preoperatively. This study quantified DKI metrics in the tumor region and in contralateral normal-appearing white matter (NAWM). Subsequently, relative DKI parameters were calculated for both the tumor parenchyma and the peritumoral edema region. These included relative mean kurtosis (rMK), relative radial kurtosis (rKr), relative axial kurtosis (rKa), relative mean diffusivity (rMD), and relative fractional anisotropy (rFA). Differences in clinical data, morphological features, and DKI-derived parameters were compared between the two groups. Receiver operating characteristic (ROC) curves were utilized to determine the diagnostic performance, including the area under the curve (AUC), sensitivity, and specificity. Results: 0.009) remained independent predictors after adjusting for World Health Organization (WHO) grade and IDH status. The fusion model combining rMK and tumor margin achieved the highest AUC of 0.924 (sensitivity 87.8%, specificity 81.8%). Normalization significantly and critically enhances the predictive performance for MGMTp methylation status. Conclusions: DKI parameters, particularly rMK and rMD, combined with MRI morphological features (blurred tumor margin), provide excellent non-invasive prediction of MGMTp methylation status in gliomas, independent of tumor grade and IDH mutation status. This integrative approach may facilitate preoperative treatment planning and individualized chemotherapy strategies.
Source: Translational Cancer Research
Authors: Yin Gao, Zhenyu Song, Ping Wang, Huan Huang
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7223259268