复发性脑胶质瘤：基于动态对比增强 T1 加权灌注 MR 成像的最优曲线下面积法。
Recurrent glioblastoma: optimum area under the curve method derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging.
机构信息
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 6 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-736, Korea.
出版信息
Radiology. 2013 Nov;269(2):561-8. doi: 10.1148/radiol.13130016. Epub 2013 Jul 22.
PURPOSE
To determine whether the ratio of the initial area under the time-signal intensity curve (AUC) (IAUC) to the final AUC--or AUCR--derived from dynamic contrast material-enhanced magnetic resonance (MR) imaging can be an imaging biomarker for distinguishing recurrent glioblastoma multiforme (GBM) from radiation necrosis and to compare the diagnostic accuracy of the AUCR with commonly used model-free dynamic contrast-enhanced MR imaging parameters.
MATERIALS AND METHODS
The institutional review board approved this retrospective study and waived the informed consent requirement. Fifty-seven consecutive patients with pathologically confirmed recurrent GBM (n = 32) or radiation necrosis (n = 25) underwent dynamic contrast-enhanced MR imaging. Histogram parameters of the IAUC at 30, 60, and 120 seconds and the AUCR, which included the mean value at the higher curve of the bimodal histogram (mAUCR(H)), as well as 90th percentile cumulative histogram cutoffs, were calculated and were correlated with final pathologic findings. The best predictor for differentiating recurrent GBM from radiation necrosis was determined by means of receiver operating characteristic (ROC) curve analysis.
RESULTS
The demographic data were not significantly different between the two patient groups. There were statistically significant differences in all of the IAUC and AUCR parameters between the recurrent GBM and the radiation necrosis patient groups (P < .05 for each). ROC curve analyses showed mAUCR(H) to be the best single predictor of recurrent GBM (mAUCR(H) for recurrent GBM = 0.35 ± 0.11 [standard deviation], vs 0.19 ± 0.17 for radiation necrosis; P < .0001; optimum cutoff, 0.23), with a sensitivity of 93.8% and a specificity of 88.0%.
CONCLUSION
A bimodal histogram analysis of AUCR derived from dynamic contrast-enhanced MR imaging can be a potential noninvasive imaging biomarker for differentiating recurrent GBM from radiation necrosis.
SUPPLEMENTAL MATERIAL
http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13130016/-/DC1.
目的
旨在确定初始时间信号强度曲线（AUC）下面积（IAUC）与动态对比增强磁共振成像（MR）中获得的最终 AUC（IAUC）的比值（AUCR）是否可以作为鉴别复发性多形性胶质母细胞瘤（GBM）与放射性坏死的影像学生物标志物，并比较 AUCR 与常用无模型动态对比增强磁共振成像参数的诊断准确性。
材料与方法
本回顾性研究经机构审查委员会批准，并豁免了知情同意书的要求。连续 57 例经病理证实的复发性 GBM（n = 32）或放射性坏死（n = 25）患者接受了动态对比增强 MR 成像检查。计算了 30、60 和 120 秒时 IAUC 的直方图参数和 AUCR，包括双模态直方图较高曲线的平均值（mAUCR(H)）以及 90 百分位累积直方图截止值，并与最终病理结果相关联。通过受试者工作特征（ROC）曲线分析确定鉴别复发性 GBM 与放射性坏死的最佳预测指标。
结果
两组患者的人口统计学数据无显著差异。复发性 GBM 组与放射性坏死组之间的所有 IAUC 和 AUCR 参数均存在统计学差异（P <.05）。ROC 曲线分析显示 mAUCR(H) 是鉴别复发性 GBM 的最佳单一预测指标（复发性 GBM 的 mAUCR(H)为 0.35 ± 0.11 [标准差]，放射性坏死为 0.19 ± 0.17；P <.0001；最佳截止值为 0.23），其敏感性为 93.8%，特异性为 88.0%。
结论
从动态对比增强 MR 成像中获得的 AUCR 的双模态直方图分析可能是鉴别复发性 GBM 与放射性坏死的潜在无创影像学生物标志物。
补充材料
http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13130016/-/DC1.
Zotero 插件