Noninvasive Assessment of O(6)‐Methylguanine‐DNA Methyltransferase Promoter Methylation Status in World Health Organization Grade II–IV Glioma Using Histogram Analysis of Inflow‐Based Vascular‐Space‐Occupancy Combined with Structural Magnetic Resonance Imaging. Issue 1 (16th February 2021) - He, Wenle; Li, Xiaodan; Hua, Jun; Liao, Shukun; Guo, Liuji; Xiao, Xiang; Liu, Xiaomin; Zhou, Jun; Wang, Wensheng; Xu, Yikai; Wu, Yuankui
Abstract: Background: O(6)‐methylguanine‐DNA methyltransferase (MGMT) promoter methylation is an important prognostic factor for gliomas and is associated with tumor angiogenesis. Arteriolar cerebral blood volume (CBVa) obtained from inflow‐based vascular‐space‐occupancy (iVASO) magnetic resonance imaging (MRI) is assumed to be an indicator of tumor microvasculature. Its preoperative predictive ability for MGMT promoter methylation remains unclear. Purpose: To investigate the role of iVASO‐CBVa histogram features in determining MGMT promoter methylation status of grade II–IV gliomas. Study Type: Retrospective Subjects: Forty‐six patients consisting of 20 MGMT methylated and 26 unmethylated gliomas. Field Strength/Sequence: 3.0 T magnetic resonance images containing iVASO MRI, T1 ‐weighted image (T1 WI), T2 ‐weighted image, T2 ‐weighted fluid attenuated inversion recovery image images, and enhanced T1 WI. Assessment: Sixteen structural imaging features were visually evaluated on structural MRI and 14 CBVa histogram features were extracted from iVASO‐CBVa maps. Statistical Tests: Imaging features were screened and ranked using Fisher's exact test, Mann–Whitney U ‐test, and randomforest algorithm. Features with higher importance were selected to develop logistic regression models to determine MGMT methylation status. Receiver operating characteristics (ROC) curve with the area under the curve (AUC) and leave‐one‐out cross‐validation (LOOCV) were used to assess effectiveness and stability. Results: The top two CBVa histogram features were root mean squared (RMS) and variance. The top two structural imaging features were contrast‐enhancing component of the tumor (CET) location and tumor location. Both the CBVa model of RMS and variance (ROC, AUC = 0.867; LOOCV, AUC = 0.819) and the model of structural features (ROC, AUC = 0.882; LOOCV, AUC = 0.802) accurately identified MGMT methylation. The fusion model of CBVa RMS and CET location improved diagnostic performance (ROC, AUC = 0.931; LOOCV, AUC =0.906). Data Conclusion: iVASO‐CBVa has potential in evaluating MGMT methylation status in grade II–IV gliomas. Level of Evidence: 4 Technical Efficacy: Stage 2