MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set
MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set
复制标题
DOI：
10.1148/radiol.13120118
复制
发表时间：
2013-05-01
期刊：
影响因子：
19.7
通讯作者：
Brat, Daniel J.
中科院分区：
文献类型：
--
作者：
Gutman, David A.;Cooper, Lee A. D.;Brat, Daniel J.
目的：对放射科医师对胶质母细胞瘤（GBM）肿瘤大小和组成的评估进行综合分析，使用社区开发的磁共振（MR）成像视觉特征的控制术语，因为它们与基因改变、基因表达类别和患者生存有关。材料和方法：由于所有研究患者之前都已通过癌症基因组图谱（TCGA）进行了鉴定，TCGA是一个公开可用的数据集，不包含与患者标识符的关联，并且符合HIPAA，因此不需要机构审查委员会的批准。三位神经放射科医生使用标准化特征集对75例具有TCGA门静脉遗传数据的GBM患者的术前MR图像进行了大小、位置和肿瘤形态的评分。采用Krippendorff alpha统计量和类内相关系数分析了组间一致性。生存率、肿瘤大小和形态学之间的关系通过多变量Cox回归模型确定；影像特征与基因组学之间的关系通过Fisher精确检验进行了研究。结果：内部分析显示在造影剂增强、非增强、坏死、水肿和大小变量方面有显著的一致性。即使在校正Karnofsky评分（P = 0.0208）后，对比增强的肿瘤体积和肿瘤最长轴长度与生存不良密切相关（风险比分别为8.84,P = 0.0253和1.02,P = 0.00973）。前叶型GBM的对比增强水平明显低于其他亚型（P = 0.02），而间叶型GBM的非增强水平明显低于其他亚型（P < 0.01）。结论：该分析证明了一种能够重复表征脑肿瘤的一致图像特征注释方法；本研究表明，放射科医生对宏观影像学特征的估计可以与遗传改变和基因表达亚型相结合，从而更深入地了解GBM亚群的潜在生物学特性。(c) rsna, 2013
Purpose: To conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) imaging visual features as they relate to genetic alterations, gene expression class, and patient survival.Materials and Methods: Because all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff alpha statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test.Results: Interrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P < .01).Conclusion: This analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets. (C) RSNA, 2013