Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data-Methods and Preliminary Results
Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data-Methods and Preliminary Results
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DOI：
10.1148/radiol.12111607
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发表时间：
2012-08-01
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;Xu, Jiajing;Plevritis, Sylvia K.
目的：为了确定预后的成像生物标志物在非小细胞肺癌（NSCLC）的放射基因组学策略，整合的基因表达和医学图像的患者的生存结果是不提供利用公共基因表达datasets.Materials和方法的生存数据：一个放射基因组学策略相关联的图像特征与集群的共表达基因（metagene）的定义。首先，创建放射基因组学相关图，用于图像特征和元基因之间的成对关联。其次，利用稀疏线性回归建立了基于图像特征的元基因预测模型。类似地，图像特征的预测模型是根据元基因构建的。最后，预测的图像特征的预后意义进行评估，在一个公共的基因表达数据集与生存结果。这种放射基因组学策略被应用到一个队列的26例NSCLC患者的基因表达和180个图像功能从计算机断层扫描（CT）和正电子发射断层扫描（PET）/CT是available.Results：有243个统计学显着的成对相关性图像功能和多基因的NSCLC。根据图像特征预测了元基因，准确率为59%-83%。180个CT图像特征和PET标准化摄取值中的114个被预测为多基因，准确率为65%-86%。当预测的图像特征被映射到一个公共的基因表达数据集与生存结局，肿瘤的大小，边缘形状，和清晰度排名最高的预后significant.Conclusion：这种放射基因组学策略识别成像生物标志物，可以使一个更快速的评价新的成像方式，从而加速其翻译个性化医疗。(c)RSNA，2012年
Purpose: To identify prognostic imaging biomarkers in non-small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets.Materials and Methods: A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available.Results: There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%-83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%-86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance.Conclusion: This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine. (c) RSNA, 2012