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

• Published in: Cancer science Vol. 112; no. 7; pp. 2835 - 2844
• Main Authors: Huang, Wei‐yuan, Wen, Ling‐hua, Wu, Gang, Pang, Pei‐pei, Ogbuji, Richard, Zhang, Chao‐cai, Chen, Feng, Zhao, Jian‐nong
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.07.2021; John Wiley and Sons Inc
• Subjects: Biomarkers; Brain cancer; Chemotherapy; DNA methylation; Edema; Glioblastoma; Glioma; Magnetic resonance imaging; Methylguanine; methylguanine methyltransferase; Nervous system; O6-methylguanine-DNA methyltransferase; Original; Polymerase chain reaction; Radiomics; texture analysis; methylguanine methyltransferase; glioma; magnetic resonance imaging; texture analysis
• ISSN: 1347-9032; 1349-7006
• DOI: 10.1111/cas.14918

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. 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 methylguanine methyltransferase (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.