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Purpose <p>High-risk stage II colorectal cancer (CRC) shows heterogeneous outcomes despite adjuvant chemotherapy. We developed and validated an interpretable multimodal deep learning model integrating clinical data, serum biomarkers, and venous-phase CT to predict 5-year CRC-specific mortality in high-risk stage II CRC.</p> Methods <p>This retrospective, multicenter cohort included 778 high-risk stage II CRC patients from three centers, all treated with adjuvant chemotherapy and with complete preoperative clinical, biomarker, and venous-phase CT data. Patients were split into a development cohort (Centers A + B, <i>n</i> = 720) and an external testing cohort (Center C, <i>n</i> = 58). A multimodal model combining numerical (clinical + biomarker) and imaging (CT) inputs was developed and internally validated using tenfold cross-validation in the development cohort and evaluated in the external cohort. Interpretability was assessed using SHAP and Grad-CAM.</p> Results <p>In the development cohort, the multimodal model showed superior discrimination (AUC 0.89; 95% CI, 0.87–0.91) versus numerical-only (AUC 0.76) and imaging-only (AUC 0.69). In the external testing cohort (9/58 CRC-specific deaths), the multimodal model achieved an AUC of 0.88 (95% CI, 0.76–0.96). SHAP and Grad-CAM consistently highlighted age, CA125, and tumor regions on CT as key contributors.</p> Conclusion <p>This interpretable multimodal approach, using routine clinical, biomarker, and CT data, improves 5-year mortality risk stratification in high-risk stage II CRC and may inform risk-adapted surveillance and clinical decision support; prospective validation is warranted before treatment modification.</p>

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Development and external validation of an interpretable multimodal deep learning model for 5-year mortality in high-risk stage ii colorectal cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Xin Li,
  • Lei Liang,
  • Zhong-Hua Liu,
  • Chun Wang,
  • Tawfik Ali Hamood Alburiahi,
  • Zhen-Ya Yang,
  • Ning Xu,
  • Jun Yang

摘要

Purpose

High-risk stage II colorectal cancer (CRC) shows heterogeneous outcomes despite adjuvant chemotherapy. We developed and validated an interpretable multimodal deep learning model integrating clinical data, serum biomarkers, and venous-phase CT to predict 5-year CRC-specific mortality in high-risk stage II CRC.

Methods

This retrospective, multicenter cohort included 778 high-risk stage II CRC patients from three centers, all treated with adjuvant chemotherapy and with complete preoperative clinical, biomarker, and venous-phase CT data. Patients were split into a development cohort (Centers A + B, n = 720) and an external testing cohort (Center C, n = 58). A multimodal model combining numerical (clinical + biomarker) and imaging (CT) inputs was developed and internally validated using tenfold cross-validation in the development cohort and evaluated in the external cohort. Interpretability was assessed using SHAP and Grad-CAM.

Results

In the development cohort, the multimodal model showed superior discrimination (AUC 0.89; 95% CI, 0.87–0.91) versus numerical-only (AUC 0.76) and imaging-only (AUC 0.69). In the external testing cohort (9/58 CRC-specific deaths), the multimodal model achieved an AUC of 0.88 (95% CI, 0.76–0.96). SHAP and Grad-CAM consistently highlighted age, CA125, and tumor regions on CT as key contributors.

Conclusion

This interpretable multimodal approach, using routine clinical, biomarker, and CT data, improves 5-year mortality risk stratification in high-risk stage II CRC and may inform risk-adapted surveillance and clinical decision support; prospective validation is warranted before treatment modification.