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

Table 2 Conceptual extension of the multiple instance learning framework by Zou et al[6]: Cross-domain interactions between artificial intelligence-derived morphologic intelligence and host systemic complexity

Morphologic domain (AI pathology)	Corresponding host systemic feature	Biological interaction mechanism	Implication for LNM prediction
Glandular/stromal architecture	Inflammatory tone (IL-6, CRP, NLR)	Inflammation alters epithelial-stromal signaling, enhancing invasion	Morphology + inflammation captures metastatic aggressiveness
Immune infiltration patterns	Immune competence and nutritional reserve (prognostic nutritional index, albumin)	Immunonutritional depletion reshapes immune-stromal ecology	Improves detection of occult micrometastases
Tumor budding and microenvironmental topology	Autonomic regulation (HRV)	Dysautonomia promotes prometastatic inflammatory-metabolic state	Refines risk in highrisk microenvironment signatures
Spatial heterogeneity from WSI features	Circadian rhythm stability	Circadian disruption affects proliferation, DNA repair, metastatic potential	Adds temporal biological context absent from histology
Patch-level morphodynamics (MIL attention)	Composite physiological complexity indices	Low systemic complexity reduces resilience to tumor progression	Strengthens integrative risk scoring in borderline histologic cases

Host systemic features shown represent a minimum clinically feasible panel derived from routine laboratory tests, perioperative physiologic monitoring, and standard clinical assessment. Interpretation should consider timing (preoperative baseline and early perioperative phase) and potential confounders, including infection, steroid exposure, arrhythmias, and beta-blocker use. This table expands the morphology-based framework of Zou et al[6] by illustrating how histologic features captured by MIL may interact with systemic host factors, providing a conceptual basis for multimodal prediction of lymph node metastasis. AI: Artificial intelligence; LNM: Lymph node metastasis; IL-6: Interleukin-6; CRP: C-reactive protein; NLR: Neutrophil-lymphocyte ratio; HRV: Heart rate variability; MIL: Multiple instance learning; WSI: Whole-slide images.