Department of Radiology, Mayo Clinic, 200 First St SW, Opus 2-149, Rochester, MN, 55905, USA.
Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA.
Eur Radiol. 2021 Aug;31(8):5554-5564. doi: 10.1007/s00330-021-07918-6. Epub 2021 Apr 14.
To develop an objective quantitative method to characterize and visualize meningioma-brain adhesion using MR elastography (MRE)-based slip interface imaging (SII).
This retrospective study included 47 meningiomas (training dataset: n = 35; testing dataset: n = 12) with MRE/SII examinations. Normalized octahedral shear strain (NOSS) values were calculated from the acquired MRE displacement data. The change in NOSS at the tumor boundary (ΔNOSS) was computed, from which a 3D ΔNOSS map of the tumor surface was created and the probability distribution of ΔNOSS over the entire tumor surface was calculated. Statistical features were calculated from the probability histogram. After eliminating highly correlated features, the capability of the remaining feature for tumor adhesion classification was assessed using a one-way ANOVA and ROC analysis.
The magnitude and location of the tumor adhesion can be visualized by the reconstructed 3D ΔNOSS surface map. The entropy of the ΔNOSS histogram was significantly different between adherent tumors and partially/completely non-adherent tumors in both the training (AUC: 0.971) and testing datasets (AUC: 0.900). Based on the cutoff values obtained from the training set, the ΔNOSS entropy in the testing dataset yielded an accuracy of 0.83 for distinguishing adherent versus partially/non-adherent tumors, and 0.67 for distinguishing non-adherent versus completely/partially adherent tumors.
SII-derived ΔNOSS values are useful for quantification and classification of meningioma-brain adhesion. The reconstructed 3D ΔNOSS surface map presents the state and location of tumor adhesion in a "clinician-friendly" manner, and can identify meningiomas with a high risk of adhesion to adjacent brain parenchyma.
• MR elastography (MRE)-based slip interface imaging shows promise as an objective tool to preoperatively discriminate meningiomas with a high risk of intraoperative adhesion. • Measurement of the change of shear strain at meningioma boundaries can provide quantitative metrics depicting the state of adhesion at the tumor-brain interface. • The surface map of tumor adhesion shows promise in assisting precise adhesion localization, using a comprehensible, "clinician-friendly" 3D visualization.
利用基于磁共振弹性成像(MRE)的滑动界面成像(SII)开发一种客观定量的方法来描述和可视化脑膜瘤-脑组织粘连。
本回顾性研究纳入了 47 例脑膜瘤患者(训练数据集:n=35;测试数据集:n=12),均进行了 MRE/SII 检查。从获得的 MRE 位移数据中计算归一化八面体剪切应变(NOSS)值。计算肿瘤边界处 NOSS 的变化(ΔNOSS),并由此创建肿瘤表面的 3DΔNOSS 图,计算整个肿瘤表面的ΔNOSS 概率分布。从概率直方图中计算统计特征。在消除高度相关的特征后,使用单向方差分析和 ROC 分析评估剩余特征对肿瘤粘连分类的能力。
重建的 3DΔNOSS 表面图可可视化肿瘤粘连的程度和位置。在训练数据集(AUC:0.971)和测试数据集(AUC:0.900)中,ΔNOSS 直方图的熵在粘连性肿瘤和部分/完全非粘连性肿瘤之间存在显著差异。基于训练集获得的截断值,测试数据集的ΔNOSS 熵在区分粘连性与部分/非粘连性肿瘤时的准确率为 0.83,在区分非粘连性与完全/部分粘连性肿瘤时的准确率为 0.67。
SII 衍生的ΔNOSS 值可用于脑膜瘤-脑组织粘连的定量和分类。重建的 3DΔNOSS 表面图以一种“临床医生友好”的方式呈现肿瘤粘连的状态和位置,并可以识别出与邻近脑实质粘连风险较高的脑膜瘤。
MRE 滑动界面成像有望成为一种术前区分具有高术中粘连风险的脑膜瘤的客观工具。
测量脑膜瘤边界处剪切应变的变化可以提供定量指标,描述肿瘤-脑界面的粘连状态。
肿瘤粘连的表面图有望通过可理解的“临床医生友好”的 3D 可视化来辅助精确的粘连定位。