Abstract 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.
Wang Ting,Gong Jing,Duan Huihong, et al. Study on the Correlation between Imaging Features and Gene Expression in Non-small Cell Lung Cancer[J]. Chinese Journal of Biomedical Engineering, 2020, 39(6): 667-675.
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