Qi Jinbo, Wang Peipei, Zhao Guohua, Gao Eryuan, Zhao Kai, Gao Ankang, Bai Jie, Zhang Huiting, Yang Guang, Zhang Yong, Ma Xiaoyue, Cheng Jingliang
Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
MR Scientific Marketing, Siemens Healthineers Ltd, Wuhan, China.
J Magn Reson Imaging. 2023 May;57(5):1464-1474. doi: 10.1002/jmri.28419. Epub 2022 Sep 6.
Preoperative differentiation of glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) contributes to guide neurosurgical decision-making.
To explore the value of histogram analysis based on neurite orientation dispersion and density imaging (NODDI) in differentiating between GBM and SBM and comparison of the diagnostic performance of two region of interest (ROI) placements.
Retrospective.
In all, 109 patients with GBM (n = 57) or SBM (n = 52) were enrolled.
FIELD STRENGTH/SEQUENCE: A 3.0 T scanners. T -dark-fluid sequence, contrast-enhanced T magnetization-prepared rapid gradient echo sequence, and NODDI.
ROIs were placed on the peritumoral edema area (ROI1) and whole tumor area (ROI2, included the cystic, necrotic, and hemorrhagic areas). Histogram parameters of each isotropic volume fraction (ISOVF), intracellular volume fraction (ICVF), and orientation dispersion index (ODI) from NODDI images for two ROIs were calculated, respectively.
Mann-Whitney U test, independent t-test, chi-square test, multivariate logistic regression analysis, DeLong's test.
For the ROI1 and ROI2, the ICVF and ODI obtained the highest area under curve (AUC, AUC = 0.741 and 0.750, respectively) compared to other single parameters, and the AUC of the multivariate logistic regression model was 0.851 and 0.942, respectively. DeLong's test revealed significant difference in diagnostic performance between optimal single parameter and multivariate logistic regression model within the same ROI, and the multivariate logistic regression models between two different ROIs.
The performance of multivariate logistic regression model is superior to optimal single parameter in both ROIs based on NODDI histogram analysis to distinguish SBM from GBM, and the ROI placed on the whole tumor area exhibited better diagnostic performance.
4 TECHNICAL EFFICACY: Stage 2.
术前鉴别多形性胶质母细胞瘤（GBM）和孤立性脑转移瘤（SBM）有助于指导神经外科决策。
探讨基于神经突方向离散度与密度成像（NODDI）的直方图分析在鉴别GBM和SBM中的价值，以及比较两种感兴趣区（ROI）放置方式的诊断性能。
回顾性研究。
共纳入109例GBM患者（n = 57）或SBM患者（n = 52）。
场强/序列：3.0 T扫描仪。T2-黑血序列、对比增强T1加权快速梯度回波序列和NODDI。
ROI放置在瘤周水肿区（ROI1）和整个肿瘤区（ROI2，包括囊性、坏死和出血区）。分别计算两个ROI的NODDI图像中各向同性体积分数（ISOVF）、细胞内体积分数（ICVF）和方向离散指数（ODI）的直方图参数。
曼-惠特尼U检验、独立t检验、卡方检验、多因素逻辑回归分析、德龙检验。
对于ROI1和ROI2，与其他单一参数相比，ICVF和ODI获得了最高的曲线下面积（AUC，分别为AUC = 0.741和0.750），多因素逻辑回归模型的AUC分别为0.851和0.942。德龙检验显示，同一ROI内最佳单一参数与多因素逻辑回归模型之间以及两个不同ROI之间的多因素逻辑回归模型在诊断性能上存在显著差异。
基于NODDI直方图分析，在两个ROI中，多因素逻辑回归模型在区分SBM和GBM方面的性能均优于最佳单一参数，且放置在整个肿瘤区的ROI表现出更好的诊断性能。
4 技术效能：2级
