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Proceedings Paper

Association of CT-based imaging features and genomic data in non-small cell lung cancer
Author(s): Ting Wang; Jing Gong; Hui-hong Duan; Li-jia Wang; Sheng-dong Nie
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Paper 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.

Paper Details

Date Published: 29 October 2018
PDF: 8 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360P (29 October 2018); doi: 10.1117/12.2514625
Show Author Affiliations
Ting Wang, Univ. of Shanghai for Science and Technology (China)
Jing Gong, Univ. of Shanghai for Science and Technology (China)
Hui-hong Duan, Univ. of Shanghai for Science and Technology (China)
Li-jia Wang, Univ. of Shanghai for Science and Technology (China)
Sheng-dong Nie, Univ. of Shanghai for Science and Technology (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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