Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related death worldwide, yet current prognostic stratification is hindered by tumor heterogeneity. Here, we developed a deep learning radiomics model (DLRM), optimized through systematic evaluation of ten machine learning algorithms across 117 combinations, using venous-phase computed tomography (CT) images of 1183 patients from four centers. The resulting risk stratification stratified patients into high- and low-risk groups with distinct survival outcomes, and integration with clinical factors further improved prediction. Integrative transcriptomic and metabolomic analyses revealed that high-risk tumors were enriched for extracellular matrix (ECM)-related pathways associated with tumor progression, whereas low-risk tumors exhibited immune-related signatures, including higher CD8⁺ T-cell infiltration. Both omics consistently identified butanoate metabolism and nitrogen metabolism as protective pathways, validated in an independent public cohort (n = 417). This integrative analytic framework provides robust risk stratification and uncovers biological processes with potential therapeutic relevance.
Yan et al. (2026) studied this question.