Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features
Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features
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DOI：
10.1148/radiol.14131731
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发表时间：
2014-10-01
期刊：
影响因子：
19.7
通讯作者：
Plevritis, Sylvia K.
中科院分区：
文献类型：
--
作者：
Gevaert, Olivier;Mitchell, Lex A.;Plevritis, Sylvia K.
目的：从表征多形性胶质母细胞瘤（GBM）病变的放射学表型的磁共振（MR）图像中导出定量图像特征，并创建将这些特征与各种分子数据相关联的放射基因组图谱。临床，分子，在当地伦理委员会和机构评估后，从癌症基因组图谱和癌症成像档案中获得了55例患者的GBM和MR成像数据。审查委员会的批准。绘制与肿瘤坏死和瘤周水肿增强相对应的感兴趣区域（ROI），并从这些ROI中获得定量图像特征。鲁棒的定量图像功能的基础上定义的组内相关系数为0.6的数字算法修改和重测分析。通过使用分层聚类可视化稳健特征，并通过使用考克斯比例风险模型与生存相关。接下来，通过使用非参数统计检验，将这些稳健的图像特征与来自视觉上可识别的伦勃朗图像（VASARI）特征集和GBM分子亚组的放射科医师手动注释相关联。使用生物信息学算法来创建基因表达模块，其被定义为一组共表达基因连同预测模块表达模式的癌症驱动基因的多变量模型。模块与强大的图像功能，通过使用斯皮尔曼相关性检验创建放射基因组图谱，并链接强大的图像功能与molecular pathways.Results：18个图像功能通过了鲁棒性分析，并进一步分析了三种类型的ROI，共54个图像功能。三个增强特征与生存率显著相关，77个显著相关性在稳健的定量特征和VASARI特征集之间，7个图像特征与分子亚组相关（所有P <0.05）。建立了一个放射基因组学地图链接的图像功能与基因表达模块，并允许链接的56%（30 54）的图像功能与生物processes.Conclusion：放射基因组学方法在GBM有潜力预测肿瘤的临床和分子特征的非侵入性。
Purpose: To derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create radiogenomic maps associating these features with various molecular data.Materials and Methods: Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and quantitative image features were derived from these ROIs. Robust quantitative image features were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest analysis. The robust features were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust image features were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create radiogenomic maps and to link robust image features with molecular pathways.Results: Eighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of 54 image features. Three enhancement features were significantly correlated with survival, 77 significant correlations were found between robust quantitative features and the VASARI feature set, and seven image features were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to link image features with gene expression modules and allowed linkage of 56% (30 of 54) of the image features with biologic processes.Conclusion: Radiogenomic approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively.