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Radiogenomic Profiling for Survival Analysis in Gastric Cancer: Integrating CT Imaging, Gene Expression, and Clinical
Anju R Nath1, Kiruthika Thenmozhi2, Jeyakumar Natarajan3
1Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, 641 046, India.
Molecular Imaging and Biology
|May 15, 2025
Summary
Integrating computed tomography (CT) radiomics, gene expression, and clinical data improves gastric cancer survival prediction. This study identified novel radiogenomic biomarkers for enhanced prognostic accuracy.
Area of Science:
- Radiomics and Genomics
- Oncology
- Medical Imaging
Background:
- Gastric cancer (GC) prognosis relies on integrating diverse data types.
- Computed Tomography (CT) radiomics offers quantitative imaging biomarkers.
- Gene expression profiles provide molecular insights into cancer biology.
Purpose of the Study:
- To integrate CT radiomic features, gene expression, and clinical data for gastric cancer.
- To identify novel radiogenomic biomarkers for improved overall survival prediction.
- To explore the prognostic value of integrated data in GC patients.
Main Methods:
- Quantitative radiomic analysis of 37 GC CT images.
- Gene expression and clinical data integration.
- Gene Set Enrichment Analysis (GSEA) and regression modeling for biomarker discovery and survival prediction.
Main Results:
- Selected 46 radiomic features, 1,032 genes, and age as significant predictors.
- Identified 29 KEGG pathways (immune, signal transduction, catabolism) linking radiomics and genomics.
- Support Vector Machine (SVM) model identified age, CSF1R, CXCL12, and specific image features as independent predictors of survival.
Conclusions:
- Integration of imaging, genomics, and clinical data holds significant prognostic potential for GC.
- Identified genes (CSF1R, CXCL12) represent potential novel radiogenomic biomarker candidates.
- Further evaluation of these biomarkers could enhance personalized treatment strategies for gastric cancer.
