Recurrent Glioblastoma: Optimum Area under the Curve Method Derived from Dynamic Contrast-enhanced T1-weighted Perfusion MR Imaging
Recurrent Glioblastoma: Optimum Area under the Curve Method Derived from Dynamic Contrast-enhanced T1-weighted Perfusion MR Imaging
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DOI：
10.1148/radiol.13130016
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发表时间：
2013-11-01
期刊：
影响因子：
19.7
通讯作者：
Kim, Sang Joon
中科院分区：
文献类型：
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作者：
Chung, Won Jung;Kim, Ho Sung;Kim, Sang Joon
目的：确定时间-信号强度曲线下面积（AUC）（IAUC）与动态对比材料增强磁共振（MR）成像的最终AUC或AUCR的比值是否可作为区分复发性多形性胶质母细胞瘤（GBM）与放射性坏死的成像生物标志物，并比较AUCR与常用无模型动态对比的诊断准确性。材料和方法：机构审查委员会批准了这项回顾性研究，并免除了知情同意要求。57例经病理证实的复发性GBM（n = 32）或放射性坏死（n = 25）的连续患者接受了动态对比增强MR成像。计算30、60和120秒时的IAUC和AUCR的直方图参数，包括双峰直方图较高曲线处的平均值（mAUCR（H））以及第90百分位数累积直方图截止值，并与最终病理学结果相关。通过受试者工作特征（ROC）曲线分析确定了区分复发性GBM和放射性坏死的最佳预测因子。复发性GBM和放射性坏死患者组之间的所有IAUC和AUCR参数均存在统计学显著差异（P <0.05）。ROC曲线分析显示，mAUCR（H）是复发性GBM的最佳单一预测因子（复发性GBM的mAUCR（H）= 0.35 +/- 0.11 [标准差]，放射性坏死的mAUCR（H）= 0.19 +/- 0.17; P <0.0001;最佳临界值0.23），灵敏度为93.8%，特异性为88.0%。动态对比增强MR成像的AUCR双峰直方图分析可能是区分复发性GBM与放射性坏死的潜在非侵入性成像生物标志物。（C）RSNA，2013年
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. (C)RSNA, 2013