ArticleInternational journal of colorectal disease2026
Development and external validation of an interpretable multimodal deep learning model for 5-year mortality in high-risk stage ii colorectal cancer.
Article in International journal of colorectal disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeHigh-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.
methodsThis 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.
resultsIn 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.
conclusionThis 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.
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