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Predicting epidermal growth factor receptor gene amplification status in glioblastoma multiforme by quantitative enhancement and necrosis features deriving from conventional magnetic resonance imaging

机译:通过定量增强和常规磁共振成像产生的坏死特征预测多形性胶质母细胞瘤中表皮生长因子受体基因的扩增状态

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摘要

To study whether some of the quantitative enhancement and necrosis features in preoperative conventional MRI (cMRI) had a predictive value for epidermal growth factor receptor (EGFR) gene amplification status in glioblastoma multiforme (GBM).Fifty-five patients with pathologically determined GBMs who underwent cMRI were retrospectively reviewed. The following cMRI features were quantitatively measured and recorded: long and short diameters of the enhanced portion (LDE and SDE), maximum and minimum thickness of the enhanced portion (MaxTE and MinTE), and long and short diameters of the necrotic portion (LDN and SDN). Univariate analysis of each feature and a decision tree model fed with all the features were performed. Area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of features, and predictive accuracy was used to assess the performance of the model.For single feature, MinTE showed the best performance in differentiating EGFR gene amplification negative (wild-type) (nEGFR) GBM from EGFR gene amplification positive (pEGFR) GBM, and it got an AUC of 0.68 with a cut-off value of 2.6 mm. The decision tree model included 2 features MinTE and SDN, and got an accuracy of 0.83 in validation dataset.Our results suggest that quantitative measurement of the features MinTE and SDN in preoperative cMRI had a high accuracy for predicting EGFR gene amplification status in GBM.
机译:研究术前常规MRI(cMRI)的某些定量增强和坏死特征是否对多形胶质母细胞瘤(GBM)中的表皮生长因子受体(EGFR)基因扩增状态具有预测价值。回顾性回顾了cMRI。定量测量并记录了以下cMRI特征:增强部分的长和短直径(LDE和SDE),增强部分的最大和最小厚度(MaxTE和MinTE)以及坏死部分的长和短直径(LDN和SDN)。对每个特征进行单变量分析,并为决策树模型提供所有特征。接收者操作特征(ROC)曲线(AUC)下的面积用于评估特征的性能,预测准确性用于评估模型的性能。对于单个特征,MinTE在区分EGFR基因扩增阴性时表现出最佳性能。 EGFR基因扩增阳性(pEGFR)GBM产生的(野生型)(nEGFR)GBM,其AUC为0.68,截断值为2.6mm。决策树模型包含2个特征MinTE和SDN,在验证数据集中的准确度为0.83。我们的结果表明,术前cMRI中对特征MinTE和SDN的定量测量对GBM中EGFR基因扩增状态的预测具有较高的准确性。

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