Correlation between CT based radiomics features and gene expression data in non-small cell lung cancer

Article type: Research Article

Authors: Wang, Ting^a | Gong, Jing^a | Duan, Hui-Hong^a | Wang, Li-Jia^a | Ye, Xiao-Dan^{b; *} | Nie, Sheng-Dong^{a; *}

Affiliations: [a] School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China | [b] Department of Radiology, Shanghai Jiao Tong University Affiliated Chest Hospital, Shanghai, China

Correspondence: [*] Corresponding author: Xiao-Dan Ye, Department of Radiology, Shanghai Jiao Tong University Affiliated Chest Hospital, 241 West Huaihai Road, Shanghai, 200030 China. Tel.: +86 861 22200000 1305; E-mail: [email protected]. and Sheng-Dong Nie, School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, 516 Jun Gong Road, Shanghai, 200093 China. Tel.: +86 861 02155271116; E-mail: [email protected].

Abstract: OBJECTIVE:Radiogenomics investigates radiographic imaging phenotypes associated with gene expression patterns. This study aims to explore relationships between CT imaging radiomics features and gene expression data in non-small cell lung cancer (NSCLC). METHODS:Eighty-nine NSCLC patients are included in the study. Radiomics features are extracted and selected to quantify the phenotype of tumors on CT-scans. Co-expressed genes are also clustered and the first principal component of the cluster is represented, which is defined as a metagene. Then, statistical analysis was performed to assess association of CT radiomics features with metagenes. In addition, predictive models are built and metagene enrichment are conducted to further evaluate performance of NSCLC radiogenomics statistically and biologically. RESULTS:There are 187 significant pairwise correlations between a CT radiomics feature and a metagene of NSCLC, where eighteen metagenes are annotated with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. Metagenes are predicted in terms of radiomics features with an accuracy of 41.89% –89.93%. CONCLUSIONS:This study reveals the associations between CT imaging radiomics features and NSCLC co-expressed gene sets. The findings suggest that CT radiomics features can reflect important biological information of NSCLC patients, which may have a significant clinical impact as CT is routinely used in clinical practice, assisting in improving medical decision-support at low cost.

Keywords: Radiogenomics, radiomics features, computed tomography, non-small cell lung cancer

DOI: 10.3233/XST-190526

Journal: Journal of X-Ray Science and Technology, vol. 27, no. 5, pp. 773-803, 2019