Radiological model based on the standard magnetic resonance sequences for detecting methylguanine methyltransferase methylation in glioma using texture analysis.

This study aims to build a radiological model based on standard MR sequences for detecting methylguanine methyltransferase (MGMT) methylation in gliomas using texture analysis. A retrospective cross-sectional study was undertaken in a cohort of 53 glioma patients who underwent standard preoperative magnetic resonance (MR) imaging. Conventional visual radiographic features and clinical factors were compared between MGMT promoter methylated and unmethylated groups. Texture analysis extracted the top five most powerful texture features of MR images in each sequence quantitatively for detecting the MGMT promoter methylation status. The radiomic signature (Radscore) was generated by a linear combination of the five features and estimates in each sequence. The combined model based on each Radscore was established using multivariate logistic regression analysis. A receiver operating characteristic (ROC) curve, nomogram, calibration, and decision curve analysis (DCA) were used to evaluate the performance of the model. No significant differences were observed in any of the visual radiographic features or clinical factors between different MGMT methylated statuses. The top five most powerful features were selected from a total of 396 texture features of T1, contrast-enhanced T1, T2, and T2 FLAIR. Each sequence's Radscore can distinguish MGMT methylated status. A combined model based on Radscores showed differentiation between methylated MGMT and unmethylated MGMT both in the glioblastoma (GBM) dataset as well as the dataset for all other gliomas. The area under the ROC curve values for the combined model was 0.818, with 90.5% sensitivity and 72.7% specificity, in the GBM dataset, and 0.833, with 70.2% sensitivity and 90.6% specificity, in the overall gliomas dataset. Nomogram, calibration, and DCA also validated the performance of the combined model. The combined model based on texture features could be considered as a noninvasive imaging marker for detecting MGMT methylation status in glioma.

Comparison of Radiomics Analyses Based on Different Magnetic Resonance Imaging Sequences in Grading and Molecular Genomic Typing of Glioma.

OBJECTIVE: To investigate the value of radiomics analyses based on different magnetic resonance (MR) sequences in the noninvasive evaluation of glioma characteristics for the differentiation of low-grade glioma versus high-grade glioma, isocitrate dehydrogenase (IDH)1 mutation versus IDH1 wild-type, and mutation status and 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation (+) versus MGMT promoter methylation (-) glioma. METHODS: Fifty-nine patients with untreated glioma who underwent a standard 3T-MR tumor protocol were included in the study. A total of 396 radiomics features were extracted from the MR images, with the manually delineated tumor as the volume of interest. Clinical imaging diagnostic features (tumor location, necrosis/cyst change, crossing midline, and the degree of enhancement or peritumoral edema) were analyzed by univariate logistic regression to select independent clinical factors. Radiomics and combined clinical-radiomics models were established for grading and molecular genomic typing of glioma by multiple logistic regression and cross-validation. The performance of the models based on different sequences was evaluated by using receiver operating characteristic curves, nomograms, and decision curves. RESULTS: The radiomics model based on T1-CE performed better than models based on other sequences in predicting the tumor grade and the IDH1 status of the glioma. The radiomics model based on T2 performed better than models based on other sequences in predicting the MGMT methylation status of glioma. Only the T1 combined clinical-radiomics model showed improved prediction performance in predicting tumor grade and the IDH1 status. CONCLUSIONS: The results demonstrate that state-of-the-art radiomics analysis methods based on multiparametric MR image data and radiomics features can significantly contribute to pretreatment glioma grading and molecular subtype classification.