Ting Wang1, Jing Gong1, Hui-Hong Duan1, Li-Jia Wang1, Xiao-Dan Ye2, Sheng-Dong Nie1. 1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China. 2. Department of Radiology, Shanghai Jiao Tong University Affiliated Chest Hospital, Shanghai, China.
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.
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 NSCLCpatients 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 NSCLCpatients, which may have a significant clinical impact as CT is routinely used in clinical practice, assisting in improving medical decision-support at low cost.
Entities:
Keywords:
Radiogenomics; computed tomography; non-small cell lung cancer; radiomics features
Authors: Gopichandh Danala; Masoom Desai; Bappaditya Ray; Morteza Heidari; Sai Kiran R Maryada; Calin I Prodan; Bin Zheng Journal: Ann Biomed Eng Date: 2022-02-02 Impact factor: 3.934
Authors: Morteza Heidari; Sivaramakrishnan Lakshmivarahan; Seyedehnafiseh Mirniaharikandehei; Gopichandh Danala; Sai Kiran R Maryada; Hong Liu; Bin Zheng Journal: IEEE Trans Biomed Eng Date: 2021-08-19 Impact factor: 4.756