Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.
BMC Med Imaging. 2020 Nov 23;20(1):124. doi: 10.1186/s12880-020-00524-w.
To compare the diagnostic performance of neurite orientation dispersion and density imaging (NODDI), mean apparent propagator magnetic resonance imaging (MAP-MRI), diffusion kurtosis imaging (DKI), diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs).
Patients with previously untreated, histopathologically confirmed HGGs (n = 20) or SBMs (n = 21) appearing as a solitary and contrast-enhancing lesion on structural MRI were prospectively recruited to undergo diffusion-weighted MRI. DWI data were obtained using a q-space Cartesian grid sampling procedure and were processed to generate parametric maps by fitting the NODDI, MAP-MRI, DKI, DTI and DWI models. The diffusion metrics of the contrast-enhancing tumor and peritumoral edema were measured. Differences in the diffusion metrics were compared between HGGs and SBMs, followed by receiver operating characteristic (ROC) analysis and the Hanley and McNeill test to determine their diagnostic performances.
NODDI-based isotropic volume fraction (V) and orientation dispersion index (ODI); MAP-MRI-based mean-squared displacement (MSD) and q-space inverse variance (QIV); DKI-generated radial, mean diffusivity and fractional anisotropy (RD, MD and FA); and DTI-generated radial, mean diffusivity and fractional anisotropy (RD, MD and FA) of the contrast-enhancing tumor were significantly different between HGGs and SBMs (p < 0.05). The best single discriminative parameters of each model were V, MSD, RD and RD for NODDI, MAP-MRI, DKI and DTI, respectively. The AUC of V (0.871) was significantly higher than that of MSD (0.736), RD (0.760) and RD (0.733) (p < 0.05).
NODDI outperforms MAP-MRI, DKI, DTI and DWI in differentiating between HGGs and SBMs. NODDI-based V has the highest performance.
比较神经纤维各向异性分数弥散成像（NODDI）、平均表观扩散系数磁共振成像（MAP-MRI）、扩散峰度成像（DKI）、弥散张量成像（DTI）和扩散加权成像（DWI）在鉴别高级别胶质瘤（HGG）和单发脑转移瘤（SBM）中的诊断性能。
前瞻性纳入 20 例经组织病理学证实的初治 HGG 和 21 例单发强化占位的 SBM 患者，行弥散加权磁共振成像。采用 q 空间笛卡尔网格采样方法获取 DWI 数据，通过拟合 NODDI、MAP-MRI、DKI、DTI 和 DWI 模型生成参数图。测量增强肿瘤和瘤周水肿的弥散指标。比较 HGG 和 SBM 之间的弥散指标差异，然后进行受试者工作特征（ROC）分析和 Hanley 和 McNeill 检验，以确定其诊断性能。
NODDI 各向同性体积分数（V）和各向异性指数（ODI）、MAP-MRI 平均平方位移（MSD）和 q 空间倒数方差（QIV）、DKI 生成的径向弥散度、平均弥散度和各向异性分数（RD、MD 和 FA）、DTI 生成的径向弥散度、平均弥散度和各向异性分数（RD、MD 和 FA）在 HGG 和 SBM 之间差异均有统计学意义（p<0.05）。各模型最佳单分辨参数分别为 NODDI 的 V、MAP-MRI 的 MSD、DKI 的 RD 和 DTI 的 RD。V 的 AUC（0.871）显著高于 MSD（0.736）、RD（0.760）和 RD（0.733）（p<0.05）。
与 MAP-MRI、DKI、DTI 和 DWI 相比，NODDI 能更好地鉴别 HGG 和 SBM。基于 NODDI 的 V 具有最高的性能。
