非小细胞肺癌影像学特征与基因表达间相关性的探索性研究

影像基因组学通过挖掘肿瘤的影像和基因组学间的关联性,将两者的优势互补,以此指导不同患者个体化治疗方案的制定、预后评估、疗效检测等。针对非小细胞肺癌(NSCLC),建立其CT影像定量特征与基因表达之间的映射。首先,将对应的CT影像中肿瘤区域进行分割和特征提取,选用66种三维定量特征作为肿瘤区域影像特征集;然后,利用基因组学数据分析流程,在原始基因数据经过预处理、聚类后,获取其第一主成分作为具有相似表达谱基因聚类结果的代表;最后,运用基因芯片显著性分析算法探寻两者之间的相关性,并进行基因集的富集分析和预测模型的建立。对癌症图像归档数据库(TCIA)中的26例NSCLC影像数据和基因表达综合数据库(GEO)中相对应的基因数据进行分析,共得到126组成对的显著关联(q<0.05)。将所得结果中的29组元基因建立预测模型,并通过TCIA中更新的211组数据,对其中10组预测准确率大于70%且预测的元基因有生物学意义的预测模型进行验证,最终预测准确率为35.48%～80.85%,10个预测模型中有6个的准确率在70%以上。实验结果表明,特定的影像特征或其组合可以作为基因表达的影像标记物。

Study on the Correlation between Imaging Features and Gene Expression in Non-small Cell Lung Cancer

Radiogenomics combines the complementary advantages of radiomics and genomics by mining the association of them to guide the development of individualized treatment regimens, prognosis evaluation and efficacy detection for different patients. This paper established the mapping between quantitative characteristics of CT images and gene expression for non-small cell lung cancer (NSCLC). Firstly, the tumor regions in the corresponding CT images were segmented and features were extracted. We selected 66 kinds of three-dimensional quantitative features as the imaging feature set of the tumor area. Secondly, the first principal component was obtained as the representative of the clustering results with similar expression profiles after preprocessing and clustering the original genetic data by using genomics data analysis process. Finally, the algorithm about significance analysis of microarray was used to explore the correlation between imaging features and gene expression. We also carried out the enrichment analysis of gene sets and established the prediction models. The 26 cases of NSCLC image data from this study were selected from the Cancer Imaging Archive (TCIA) and the corresponding genetic data were derived from the Gene Expression Omnibus (GEO). Analysis of these data revealed a significant association of 126 pairs (q<0.05). Prediction models were established for 29 sets of genes in the obtained results. In addition, the updated 211 sets of data from TCIA were used to verify the prediction model with the predicted metagenomic significance in 10 of the 29 groups, whose prediction accuracy was more than 70%. In addition, 10 predictive models with prediction accuracy of more than 70% and biological significance were verified by 211 groups of updated data in TCIA. The final prediction accuracy was 35.48～80.85% and the accuracy of six of the 10 prediction models was above 70%. These experimental results showed that the specific image features or their combination could be used as image markers of gene expression.