Neurite orientation dispersion and density imaging and diffusion tensor imaging to facilitate distinction between infiltrating tumors and edemas in glioblastoma
Neurite orientation dispersion and density imaging and diffusion tensor imaging to facilitate distinction between infiltrating tumors and edemas in glioblastoma
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DOI：
10.1016/j.mri.2023.03.001
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发表时间：
2023-03-20
影响因子：
2.5
通讯作者：
Nakanishi, Katsuyuki
中科院分区：
文献类型：
--
作者：
Okita, Yoshiko;Takano, Koji;Nakanishi, Katsuyuki
背景：胶质母细胞瘤是高度浸润性肿瘤，区分非增强性肿瘤（NETs）和血管源性水肿（Edemas）发生在非增强的t2加权高信号区是具有挑战性的。在这里，我们使用神经突定向弥散和密度成像（NODDI）和扩散张量成像（DTI）来区分胶质母细胞瘤中的NETs和水肿。材料和方法：回顾性收集21例原发性胶质母细胞瘤患者的资料，3例转移，2例脑膜瘤作为对照。MRI数据包括T2加权图像和增强T1加权图像、NODDI和DTI。三名神经外科医生手动将感兴趣体积（VOIs）分配给NETs和水肿。计算每个VOI的DTI和noddi衍生参数为分数各向异性（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）和取向色散指数。结果：分别在NETs和水肿处放置16例和14例voi。NETs与水肿的ICVF、ISOVF、FA、ADC值差异有统计学意义（p < 0.01）。受试者工作特征曲线分析显示，与使用NODDI参数（0.910）或DTI参数（0.899）相比，使用所有参数可以更好地区分NETs与水肿（曲线下面积= 0.918）。对所有参数进行多元logistic回归，建立NETs与水肿的预测公式，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，确认分化为水肿。结论：在胶质母细胞瘤的非对比T2高强度区，使用NODDI和DTI可能有助于鉴别NETs和水肿。
Background: 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 chal-lenging. Here, we differentiated between NETs and Edemas in glioblastomas using neurite orientation dispersion and density imaging (NODDI) and diffusion tensor imaging (DTI).Materials and methods: 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.Results: 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.Conclusions: Using NODDI and DTI may prove useful in differentiating NETs from Edemas in the non-contrast T2 hyperintensity region of glioblastomas.