Artificial intelligence morphology and host complexity for precision prediction of nodal metastasis in colorectal cancer

Figure 2 Multimodal predictive architecture for lymph node metastasis in colorectal cancer. Multimodal predictive architecture extending the multiple instance learning framework reported by Zou et al[6] for lymph node metastasis in colorectal cancer. This conceptual framework builds upon AI-derived morphologic intelligence from whole-slide images, as implemented in the MIL model of Zou et al[6], and integrates two additional domains: (1) Clinical-contextual variables, including tumor-node-metastasis stage, tumor markers (carcinoembryonic antigen), radiologic features (computed tomography, magnetic resonance imaging), and comorbidities; and (2) Host systemic complexity, such as inflammatory tone (interleukin-6, C-reactive protein), heart rate variability, and immune response (neutrophil-lymphocyte ratio). A cross-domain fusion layer extends the morphology-based prediction paradigm toward a biologically integrated model of lymph node metastasis risk, supporting more comprehensive and individualized surgical decision-making. AI: Artificial intelligence; WSI: Whole-slide images; IL: Interleukin; CRP: C-reactive protein; HRV: Heart rate variability; NLR: Neutrophil-lymphocyte ratio; TNM: Tumor-node-metastasis; CEA: Carcinoembryonic antigen; CT: Computed tomography; MRI: Magnetic resonance imaging; LNM: Lymph node metastasis.