MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set

<h4>PURPOSE:</h4>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.<h4>MATERIALS AND METHODS:</h4>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 α 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.<h4>RESULTS:</h4>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).<h4>CONCLUSION:</h4>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.

{'PURPOSE': '旨在利用社区开发的磁共振成像视觉特征受控术语进行全面的综合分析，以评估放射科医生对胶质母细胞瘤（GBM）肿瘤大小和组成的判断，并探究其与遗传变异、基因表达类型及患者生存率之间的关系。', 'MATERIALS_AND_METHODS': '鉴于所有研究患者均已通过癌症基因组图谱（TCGA）进行去标识化处理，TCGA是一个公开的数据集，其中不包含患者标识符，且符合健康保险携带和责任法案（HIPAA）的要求，因此无需获得机构审查委员会的批准。75名具有遗传数据的GBM患者的术前磁共振图像由三位神经放射科医生使用标准化的特征集进行评估，包括肿瘤大小、位置和形态。通过Krippendorff α统计量和类内相关系数分析了评分者间的一致性。使用多变量Cox回归模型确定生存率、肿瘤大小和形态之间的关联；通过Fisher精确检验研究成像特征与基因组学之间的关联。', 'RESULTS': '评分者间分析显示，在对比剂增强、无增强、坏死、水肿和大小变量方面存在显著的一致性。对比增强的肿瘤体积和肿瘤最长轴长度与不良生存率显著相关（分别对应的风险比：8.84，P = .0253，和风险比：1.02，P = .00973），即使在调整了Karnofsky功能评分后（P = .0208）。神经向性亚型的GBM与其它亚型相比，对比增强水平显著降低（P = .02），而间质亚型的GBM显示出非增强肿瘤水平较低（P < .01）。', 'CONCLUSION': '本研究展示了具有一致性图像特征注释的方法，能够可靠地表征脑肿瘤；研究结果表明，放射科医生对宏观成像特征的估计可以与遗传变异和基因表达亚型相结合，以提供对GBM亚群潜在生物特性的更深入见解。'}