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29 October 2018 Association of CT-based imaging features and genomic data in non-small cell lung cancer
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Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108360P (2018) https://doi.org/10.1117/12.2514625
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China
Abstract
Radiogenomics is a recent promising field in cancer research focusing on associating genomic data with radiographic imaging phenotypes. This study is initiated to establish the mapping between quantitative characteristics of CT images and gene expression data, based on publically available dataset that includes 26 non-small cell lung cancer (NSCLC) patients. On one hand, a set of 66 features are extracted to quantify the phenotype of tumors after segmentation. On the other hand, co-expressed genes are clustered and are biologically annotated that are represented by metagenes, namely the first principal component of clusters. Finally, statistical analysis is performed to assess relationship between CT imaging features and metagenes. Furthermore, a predictive model is built to evaluate NSCLC radiogenomics performance. Experiment show that there are 126 significant and reliable pairwise correlations which suggest that CTbased features are minable and can reflect important biological information of NSCLC patients.
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Ting Wang, Jing Gong, Hui-hong Duan, Li-jia Wang, and Sheng-dong Nie "Association of CT-based imaging features and genomic data in non-small cell lung cancer", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360P (29 October 2018); https://doi.org/10.1117/12.2514625
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