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<p>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 (<i>n</i> = 417). This integrative analytic framework provides robust risk stratification and uncovers biological processes with potential therapeutic relevance.</p>

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Integration of radiomics, deep learning, transcriptomics, and metabolomics reveals prognostic risk stratification and underlying biological mechanisms in colorectal cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月6日

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  • Zhiheng Li,
  • Rongzhi Cai,
  • Yangyang Qin,
  • Xiaoqing Liao,
  • Enqi Wang,
  • Xuanyu Wu,
  • Yan Zhao,
  • Zengxin Lu,
  • Yan Lin

摘要

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.