Okita Yoshiko, Takano Koji, Tateishi Soichiro, Hayashi Motohisa, Sakai Mio, Kinoshita Manabu, Kishima Haruhiko, Nakanishi Katsuyuki
Department of Neurosurgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan; Department of Neurosurgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.
Department of Neurosurgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan.
Magn Reson Imaging. 2023 Jul;100:18-25. doi: 10.1016/j.mri.2023.03.001. Epub 2023 Mar 15.
Glioblastomas are highly infiltrative tumors, and differentiating between non-enhancing tumors (NETs) and vasogenic edema (Edemas) occurring in the non-enhancing T2-weighted hyperintense area is challenging. Here, we differentiated between NETs and Edemas in glioblastomas using neurite orientation dispersion and density imaging (NODDI) and diffusion tensor imaging (DTI).
Data were collected retrospectively from 21 patients with primary glioblastomas, three with metastasis, and two with meningioma as controls. MRI data included T2 weighted images and contrast enhanced T1 weighted images, NODDI, and DTI. Three neurosurgeons manually assigned volumes of interest (VOIs) to the NETs and Edemas. The DTI and NODDI-derived parameters calculated for each VOI were fractional anisotropy (FA), apparent diffusion coefficient (ADC), intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion index.
Sixteen and 14 VOIs were placed on NETs and Edemas, respectively. The ICVF, ISOVF, FA, and ADC values of NETs and Edemas differed significantly (p < 0.01). Receiver operating characteristic curve analysis revealed that using all parameters allowed for improved differentiation of NETs from Edemas (area under the curve = 0.918) from the use of NODDI parameters (0.910) or DTI parameters (0.899). Multiple logistic regression was performed with all parameters, and a predictive formula to differentiate between NETs and Edemas could be created and applied to the edematous regions of the negative control-group images; the tumor prediction degree was well below 0.5, confirming differentiation as edema.
Using NODDI and DTI may prove useful in differentiating NETs from Edemas in the non-contrast T2 hyperintensity region of glioblastomas.
胶质母细胞瘤是具有高度浸润性的肿瘤，区分非增强肿瘤（NETs）和非增强T2加权高信号区域出现的血管源性水肿（Edemas）具有挑战性。在此，我们使用神经突方向离散度与密度成像（NODDI）和扩散张量成像（DTI）区分胶质母细胞瘤中的NETs和Edemas。
回顾性收集21例原发性胶质母细胞瘤患者、3例转移瘤患者和2例脑膜瘤患者作为对照的数据。MRI数据包括T2加权图像、对比增强T1加权图像、NODDI和DTI。三名神经外科医生手动将感兴趣区（VOIs）划定到NETs和Edemas区域。为每个VOI计算的DTI和NODDI衍生参数包括分数各向异性（FA）、表观扩散系数（ADC）、细胞内体积分数（ICVF）、各向同性体积分数（ISOVF）和方向离散度指数。
分别在NETs和Edemas区域放置了16个和14个VOIs。NETs和Edemas的ICVF、ISOVF、FA和ADC值有显著差异（p < 0.01）。受试者工作特征曲线分析显示，使用所有参数比单独使用NODDI参数（曲线下面积 = 0.910）或DTI参数（0.899）能更好地区分NETs和Edemas（曲线下面积 = 0.918）。对所有参数进行多元逻辑回归，可创建一个区分NETs和Edemas的预测公式，并应用于阴性对照组图像的水肿区域；肿瘤预测度远低于0.5，证实为水肿。
使用NODDI和DTI可能有助于在胶质母细胞瘤的非增强T2高信号区域区分NETs和Edemas。
