Colorectal cancer liver metastasis (CRLM) was one of the cancers with high mortality. Clinically, the target point was determined by invasive detection, which increased the suffering of patients and the cost of treatment. If the target point was found through the relationship between early radiomic information and genetic information, it was expected to assist doctors in diagnosing disease, formulating treatment plans, and reducing the pain and burden of patients. In this study, gene coexpression analysis and hub gene mining were first performed on the gene data; secondly, quantitative radiomic features were extracted from CT-enhanced radiomic data to obtain features highly correlated with CRLM; and finally, we analyzed the relationship between gene features and radiomic feature correlations by establishing a link between early radiomic features and gene sequencing and finding highly correlated expressions. This experiment demonstrated that radiomic features could be used to mine gene attributes. Based on the four previously identified genes (NRAS, KRAS, BRAF, and PIK3CA), we identified two novel genes, MAPK1 and STAT1, highly associated with CRLM. There were specific correlations between these 6 genes and radiomic features (shape_elongation, glcm, glszm, firstorder_10percentile, gradient, exponent_firstorder_Range, and gradient_glszm_SmallAreaLowGrayLevel). Therefore, this paper established the correlation between radiomic features and genes, and through radiomic features, we could find the genes associated with them, which was expected to achieve noninvasive prediction of liver metastasis.