Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients.
Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients.
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DOI：
10.1016/j.neurad.2014.02.006
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发表时间：
2015-07
期刊：
影响因子：
--
通讯作者：
TCGA Glioma Phenotype Research Group
中科院分区：
文献类型：
--
作者：
Nicolasjilwan M;Hu Y;Yan C;Meerzaman D;Holder CA;Gutman D;Jain R;Colen R;Rubin DL;Zinn PO;Hwang SN;Raghavan P;Hammoud DA;Scarpace LM;Mikkelsen T;Chen J;Gevaert O;Buetow K;Freymann J;Kirby J;Flanders AE;Wintermark M;TCGA Glioma Phenotype Research Group
本研究的目的是评估结合临床因素、MR成像特征和基因组学的模型是否能比任何一种单独的数据类型更好地预测胶质母细胞瘤（GBM）患者的总生存期。这项研究是利用美国国立卫生研究院支持的癌症基因组图谱（TCGA）进行的。6名神经放射科医生使用VASARI评分系统审查了来自癌症成像档案（http：//www.example.com）的102名GBM患者的MRI图像。cancerimagingarchive.net患者的临床和遗传数据从TCGA网站（http：//www.cancergenome.nih.gov/）获得。患者结局以总生存时间衡量。使用考克斯分析评估不同类别的生物标志物与生存率之间的关联。与生存率显著相关的特征是：1）临床因素：化疗; 2）成像：MRI上肿瘤对比增强的比例; 3）基因组学：HRAS拷贝数变异。这三种生物标志物的组合导致生存预测强度的递增，其中包括临床、成像和遗传变量的模型具有最高的预测准确性（曲线下面积0.679 ± 0.068，Akaike信息标准566.7，p < 0.001）。临床因素、影像学特征和HRAS拷贝数变异的组合最能预测GBM患者的生存。
The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. The study was conducted leveraging the Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive (http://cancerimagingarchive.net) of 102 GBM patients using the VASARI scoring system. The patients’ clinical and genetic data were obtained from the TCGA website (http://www.cancergenome.nih.gov/). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. The features that were significantly associated with survival were: 1) clinical factors: chemotherapy; 2) imaging: proportion of tumor contrast enhancement on MRI, and 3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679 ± 0.068, Akaike’s information criterion 566.7, p < 0.001). A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.
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影响因子：
2.7
作者：
Serão NV;Delfino KR;Southey BR;Beever JE;Rodriguez-Zas SL
通讯作者：
Rodriguez-Zas SL
影响因子：
50.3
作者：
Phillips, HS;Kharbanda, S;Aldape, K
通讯作者：
Aldape, K
影响因子：
19.7
作者：
Murakami, Ryuji;Sugahara, Takeshi;Yamashita, Yasuyuki
通讯作者：
Yamashita, Yasuyuki
影响因子：
3.5
作者：
Zolal, Amir;Hejcl, Ales;Sames, Martin
通讯作者：
Sames, Martin
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y