From the Department of Radiology (K.M.S., M.A.P., S.D.R.)
From the Department of Radiology (K.M.S., M.A.P., S.D.R.).
AJNR Am J Neuroradiol. 2018 Jun;39(6):1008-1016. doi: 10.3174/ajnr.A5675. Epub 2018 May 24.
Standard assessment criteria for brain tumors that only include anatomic imaging continue to be insufficient. While numerous studies have demonstrated the value of DSC-MR imaging perfusion metrics for this purpose, they have not been incorporated due to a lack of confidence in the consistency of DSC-MR imaging metrics across sites and platforms. This study addresses this limitation with a comparison of multisite/multiplatform analyses of shared DSC-MR imaging datasets of patients with brain tumors.
DSC-MR imaging data were collected after a preload and during a bolus injection of gadolinium contrast agent using a gradient recalled-echo-EPI sequence (TE/TR = 30/1200 ms; flip angle = 72°). Forty-nine low-grade ( = 13) and high-grade ( = 36) glioma datasets were uploaded to The Cancer Imaging Archive. Datasets included a predetermined arterial input function, enhancing tumor ROIs, and ROIs necessary to create normalized relative CBV and CBF maps. Seven sites computed 20 different perfusion metrics. Pair-wise agreement among sites was assessed with the Lin concordance correlation coefficient. Distinction of low- from high-grade tumors was evaluated with the Wilcoxon rank sum test followed by receiver operating characteristic analysis to identify the optimal thresholds based on sensitivity and specificity.
For normalized relative CBV and normalized CBF, 93% and 94% of entries showed good or excellent cross-site agreement (0.8 ≤ Lin concordance correlation coefficient ≤ 1.0). All metrics could distinguish low- from high-grade tumors. Optimum thresholds were determined for pooled data (normalized relative CBV = 1.4, sensitivity/specificity = 90%:77%; normalized CBF = 1.58, sensitivity/specificity = 86%:77%).
By means of DSC-MR imaging data obtained after a preload of contrast agent, substantial consistency resulted across sites for brain tumor perfusion metrics with a common threshold discoverable for distinguishing low- from high-grade tumors.
仅包含解剖成像的脑肿瘤标准评估标准仍然不够。虽然许多研究已经证明 DSC-MR 成像灌注指标在这方面具有价值，但由于缺乏对 DSC-MR 成像指标在不同站点和平台上的一致性的信心，因此尚未将其纳入。本研究通过对脑肿瘤患者的共享 DSC-MR 成像数据集进行多站点/多平台分析来解决这一局限性。
使用梯度回波-EPI 序列（TE/TR = 30/1200 ms；翻转角= 72°）在预加载后和钆对比剂的团注期间采集 DSC-MR 成像数据。49 例低级别（n = 13）和高级别（n = 36）胶质瘤数据集上传至癌症影像档案。数据集包括预定的动脉输入功能、增强肿瘤 ROI 和创建归一化相对 CBV 和 CBF 图所需的 ROI。7 个站点计算了 20 个不同的灌注指标。使用 Lin 一致性相关系数评估站点之间的两两一致性。使用 Wilcoxon 秩和检验评估低级别和高级别肿瘤之间的区别，然后进行接收器操作特征分析，以根据敏感性和特异性确定最佳阈值。
对于归一化相对 CBV 和归一化 CBF，93%和 94%的条目显示出良好或优秀的跨站点一致性（0.8 ≤ Lin 一致性相关系数≤ 1.0）。所有指标均可区分低级别和高级别肿瘤。根据敏感性和特异性确定了合并数据的最佳阈值（归一化相对 CBV = 1.4，敏感性/特异性= 90%：77%；归一化 CBF = 1.58，敏感性/特异性= 86%：77%）。
通过对比剂预加载后获得的 DSC-MR 成像数据，站点之间对于脑肿瘤灌注指标具有相当大的一致性，并发现了一个可用于区分低级别和高级别肿瘤的通用阈值。
