Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor
Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor
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DOI：
10.1148/radiol.14131691
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发表时间：
2014-08-01
期刊：
影响因子：
19.7
通讯作者：
Flanders, Adam
中科院分区：
文献类型：
--
作者：
Jain, Rajan;Poisson, Laila M.;Flanders, Adam
目的：研究从胶质母细胞瘤(GBM)的非增强区(NER)获得的形态影像特征和血流动力学参数，以及临床和基因组标志物与患者生存率之间的关系。材料和方法：这项符合HIPAA标准的回顾研究获得了机构审查委员会的豁免。对45例GBM患者进行了基线成像、对比剂增强磁共振(MR)成像和动态磁化率增强T2*加权灌注成像。获得了生存的分子和临床预测因素。结果：总生存期(OS)和无进展生存期(PFS)的恶化与NER的相对脑血容量(rCBV(NER))增加有关，脑白质受累深度(t检验，P=.0482)和NER边缘清晰度差(t检验，P=.0147)与OS和PFS的恶化相关。NER超过中线是NER唯一与生存不良相关的形态特征(LOG-RANK检验，P=.0125)。术前Karnofsky功能评分(KPS)和手术切除范围(n=30)是有临床意义的OS预测因素(LOG-RANK检验，分别为P=0.0176和P=0.0038)。除了高rCBV(NER)和野生型表皮生长因子受体(EGFR)突变的患者存活率显著降低外，没有基因组改变与存活率相关(对数等级检验，P=.0306；受试者工作特征曲线下面积=0.62)。联合切除范围和rCBV(NER)可略微改善预后能力(排列，P=.084)。术前预测因子的随机森林模型显示rCBV(NER)是最重要的预测因子；KPS、确诊时年龄和NER跨越中线也很重要。包含rCBV(NER)、确诊年龄和KPS的多变量模型可用于将观察到的中位生存期相差超过1年(0.49-1.79年)的患者分组。结论：高rCBV(NER)和NER跨越中线的患者和高rCBV(NER)和野生型EGFR突变的患者生存率较低。然而，在多变量生存模型中，rCBV(NER)提供了独特的预后信息，超过了对所有NER成像特征以及临床和基因组特征的评估。(C)RSNA，2014年
Purpose: To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers.Materials and Methods: An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression- free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests.Results: Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBV(NER)), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBV(NER) and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBV(NER) marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBV(NER) as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBV(NER), age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years).Conclusion: Patients with high rCBV(NER) and NER crossing the midline and those with high rCBV(NER) and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBV(NER) provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features. (C) RSNA, 2014