Predicting Glioblastoma Recurrence by Early Changes in the Apparent Diffusion Coefficient Value and Signal Intensity on FLAIR Images
Predicting Glioblastoma Recurrence by Early Changes in the Apparent Diffusion Coefficient Value and Signal Intensity on FLAIR Images
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DOI：
10.2214/ajr.16.16234
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发表时间：
2017-01-01
影响因子：
5
通讯作者：
Lignelli, Angela
中科院分区：
文献类型：
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作者：
Chang, Peter D.;Chow, Daniel S.;Lignelli, Angela
OBJECTIVE.多形性胶质母细胞瘤（GBM）的复发是由于显微镜下的肿瘤浸润尚未破坏血脑屏障。我们假设这些显微镜下的浸润灶会引起表观扩散系数（ADC）和FLAIR信号的细微变化，这些变化可以通过使用计算大数据建模来检测。26例原发性GBM患者在接受大体肿瘤全切除术后立即进行了研究。在瘤周区域，通过随访MRI检查的共同登记来确定未来GBM复发的区域。根据ADC图和FLAIR图像上的信号强度评估每个个体素的肿瘤复发可能性。建立了单因素和多因素Logistic回归模型。共分析了419，473个数据体素（105，477个肿瘤复发数据体素和313，996个肿瘤周围水肿数据体素）。与周围瘤周水肿相比，未来复发区域的ADC值降低9.5%（p < 0.001），FLAIR图像上的信号强度降低9.2%（p < 0.001）。Logistic回归显示ADC图和FLAIR图像上的信号丢失量与肿瘤复发的可能性相关。多参数Logistic回归模型联合应用对肿瘤复发的预测比单因素模型更准确。在出现异常强化的数月前，ADC图和FLAIR图像上，未来GBM复发区域的信号强度差异较小，但具有高度统计学意义。根据这些变化校准的多参数logistic模型可用于估计显微镜下非增强肿瘤的负担并预测复发疾病的位置。在体素级别执行的计算大数据建模是一种强大的技术，能够发现成像数据中重要但微妙的模式。
OBJECTIVE. Recurrence of glioblastoma multiforme (GBM) arises from areas of microscopic tumor infiltration that have yet to disrupt the blood-brain barrier. We hypothesize that these microscopic foci of invasion cause subtle variations in the apparent diffusion coefficient (ADC) and FLAIR signal detectable with the use of computational big-data modeling.MATERIALS AND METHODS. Twenty-six patients with native GBM were studied immediately after undergoing gross total tumor resection. Within the peritumoral region, areas of future GBM recurrence were identified through coregistration of follow-up MRI examinations. The likelihood of tumor recurrence at each individual voxel was assessed as a function of signal intensity on ADC maps and FLAIR images. Both single and combined multivariable logistic regression models were created.RESULTS. A total of 419,473 voxels of data (105,477 voxels of data within tumor recurrence and 313,996 voxels of data on surrounding peritumoral edema) were analyzed. For future areas of recurrence, a 9.5% decrease in the ADC value (p < 0.001) and a 9.2% decrease in signal intensity on FLAIR images (p < 0.001) were shown, compared with findings for the surrounding peritumoral edema. Logistic regression revealed that the amount of signal loss on both ADC maps and FLAIR images correlated with the likelihood of tumor recurrence. A combined multiparametric logistic regression model was more specific in the prediction of tumor recurrence than was either single-variable model alone.CONCLUSION. Areas of future GBM recurrence exhibit small but highly statistically significant differences in signal intensity on ADC maps and FLAIR images months before the development of abnormal enhancement occurs. A multiparametric logistic model calibrated to these changes can be used to estimate the burden of microscopic nonenhancing tumor and predict the location of recurrent disease. Computational big-data modeling performed at the voxel level is a powerful technique capable of discovering important but subtle patterns in imaging data.