Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Department of Radiology, Guangdong 999 Brain Hospital, Guangzhou, China.
J Magn Reson Imaging. 2021 Jul;54(1):227-236. doi: 10.1002/jmri.27514. Epub 2021 Feb 16.
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.
To investigate the role of iVASO-CBVa histogram features in determining MGMT promoter methylation status of grade II-IV gliomas.
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, T -weighted image (T WI), T -weighted image, T -weighted fluid attenuated inversion recovery image images, and enhanced T WI.
Sixteen structural imaging features were visually evaluated on structural MRI and 14 CBVa histogram features were extracted from iVASO-CBVa maps.
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.
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.
4 TECHNICAL EFFICACY: Stage 2.
O(6)-甲基鸟嘌呤-DNA 甲基转移酶（MGMT）启动子甲基化是胶质瘤的一个重要预后因素，与肿瘤血管生成有关。基于流入的血管空间占据（iVASO）磁共振成像（MRI）获得的动脉脑血容量（CBVa）被认为是肿瘤微血管的指标。其术前对 MGMT 启动子甲基化的预测能力尚不清楚。
探讨 iVASO-CBVa 直方图特征在确定 II-IV 级胶质瘤 MGMT 启动子甲基化状态中的作用。
回顾性
46 例患者，包括 20 例 MGMT 甲基化和 26 例非甲基化胶质瘤。
磁场强度/序列：3.0T 磁共振成像，包含 iVASO MRI、T1 加权图像（T1WI）、T2 加权图像、T2 加权液体衰减反转恢复图像和增强 T1WI。
在结构 MRI 上对 16 个结构成像特征进行了视觉评估，并从 iVASO-CBVa 图中提取了 14 个 CBVa 直方图特征。
使用 Fisher 精确检验、Mann-Whitney U 检验和随机森林算法对成像特征进行了筛选和排名。选择具有更高重要性的特征来开发逻辑回归模型，以确定 MGMT 甲基化状态。使用接收者操作特征（ROC）曲线及其曲线下面积（AUC）和留一法交叉验证（LOOCV）来评估有效性和稳定性。
前两个 CBVa 直方图特征是均方根（RMS）和方差。前两个结构成像特征是肿瘤的对比增强成分（CET）位置和肿瘤位置。RMS 和方差的 CBVa 模型（ROC，AUC=0.867；LOOCV，AUC=0.819）和结构特征模型（ROC，AUC=0.882；LOOCV，AUC=0.802）都能准确识别 MGMT 甲基化。RMS 和 CET 位置的 CBVa 融合模型提高了诊断性能（ROC，AUC=0.931；LOOCV，AUC=0.906）。
iVASO-CBVa 具有评估 II-IV 级胶质瘤 MGMT 甲基化状态的潜力。
4
2 级
