Department of Radiology, The First Affiliated Hospital of Xin Jiang Medical University, Urumqi, China.
School of Information Engineering, Wuhan University of Technology, Wuhan, China.
Br J Radiol. 2021 Mar 1;94(1119):20200699. doi: 10.1259/bjr.20200699. Epub 2020 Dec 17.
To evaluate the effect of artery input function (AIF) derived from different arteries for pharmacokinetic modeling on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in the grading of gliomas.
49 patients with pathologically confirmed gliomas were recruited and underwent DCE-MRI. A modified Tofts model with different AIFs derived from anterior cerebral artery (ACA), ipsilateral and contralateral middle cerebral artery (MCA) and posterior cerebral artery (PCA) was used to estimate quantitative parameters such as K (volume transfer constant) and V (fractional extracellular-extravascular space volume) for distinguishing the low grade glioma from high grade glioma. The K and V were compared between different arteries using Two Related Samples Tests (TRST) ( Wilcoxon Signed Ranks Test). In addition, these parameters were compared between the low and high grades as well as between the grade II and III using the Mann-Whitney U-test. A -value of less than 0.05 was regarded as statistically significant.
All the patients completed the DCE-MRI successfully. Sharp wash-in and wash-out phases were observed in all AIFs derived from the different arteries. The quantitative parameters (K and V) calculated from PCA were significant higher than those from ACA and MCA for low and high grades, respectively ( < 0.05). Despite the differences of quantitative parameters derived from ACA, MCA and PCA, the K and V from any AIFs could distinguish between low and high grade, however, only K from any AIFs could distinguish grades II and III. There was no significant correlation between parameters and the distance from the artery, which the AIF was extracted, to the tumor.
Both quantitative parameters K and V calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas, however, only K can distinguish grades II and III.
We sought to assess the effect of AIF on DCE-MRI for determining grades of gliomas. Both quantitative parameters K and V calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas.
评估不同动脉源的动脉输入函数（AIF）在基于动态对比增强磁共振成像（DCE-MRI）的胶质瘤分级中对药代动力学模型参数的影响。
共纳入 49 例经病理证实的脑胶质瘤患者，行 DCE-MRI 检查。使用改良的 Tofts 模型，以大脑前动脉（ACA）、患侧和对侧大脑中动脉（MCA）、大脑后动脉（PCA）的 AIF 分别计算定量参数，如 K（容积转移常数）和 V（细胞外-细胞外间隙容积分数），以区分低级别胶质瘤和高级别胶质瘤。采用两相关样本检验（TRST）（Wilcoxon 符号秩检验）比较不同动脉的 K 和 V 值。此外，采用 Mann-Whitney U 检验比较低级别和高级别、II 级和 III 级之间的这些参数。p 值小于 0.05 为差异有统计学意义。
所有患者均成功完成 DCE-MRI 检查。所有不同动脉源的 AIF 均呈现明显的快速上升和快速下降期。PCA 计算的定量参数（K 和 V）在低级别和高级别胶质瘤中均显著高于 ACA 和 MCA（p 值均小于 0.05）。尽管 ACA、MCA 和 PCA 的 AIF 定量参数存在差异，但任何 AIF 的 K 和 V 都可以区分低级别和高级别胶质瘤，而只有任何 AIF 的 K 可以区分 II 级和 III 级。参数与 AIF 提取的动脉与肿瘤之间的距离之间无显著相关性。
ACA、MCA 和 PCA 的任何动脉源的 AIF 计算的定量参数 K 和 V 都可用于区分低级别和高级别胶质瘤，但只有 K 可以区分 II 级和 III 级。
我们旨在评估 AIF 对 DCE-MRI 确定胶质瘤分级的影响。ACA、MCA 和 PCA 的任何动脉源的 AIF 计算的定量参数 K 和 V 都可用于区分低级别和高级别胶质瘤。
