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

Table 1 Methodological innovations and limitations of the in-press multiple instance learning study

Dimension	Innovations of the in-press MIL study	Remaining limitations	Implications for a multimodal predictive framework
AI morphologic intelligence	Case-level MIL mimicking pathologist workflow. WSI-level aggregation captures heterogeneity. Attention-based patch selection enhances interpretability	Morphology alone insufficient to explain metastatic biology. Lacks explicit modeling of immune/stromal spatial ecology as independent biological entities	Structural backbone for multimodal fusion. Requires addition of radiologic, molecular, and microenvironmental features
Clinical-contextual variables	Improved discrimination when combined with TNM, CEA, radiology. Reflects real-world workflows	Limited clinical covariates. Does not incorporate surgical plan, frailty, comorbidities	Provides necessary boundary conditions for risk interpretation
Host systemic complexity	Not included but strongly supported by evidence	No modeling of IL-6, CRP, NLR, metabolic reserve, HRV, circadian stability	Host signatures refine metastatic propensity; supported by Wang and Pan 2025[8]
Explainability	Attention maps increase clinical trust	No mechanistic linkage to systemic biology. Limited uncertainty quantification	Future models should incorporate cross-domain explainability
Generalizability and scalability	Real-world compatible workflow	Single-cohort dataset. No prospective validation	Multimodal approaches improve robustness and external validity
Clinical utility	Enhanced discrimination vs traditional models	No mapping to surgical decision thresholds	Supports precision CME/TME, neoadjuvant selection, host optimization

The left-hand columns summarize key innovations and limitations of the multiple instance learning framework reported by Zou et al[6], whereas the right-hand column positions these findings within a broader multimodal predictive architecture. AI: Artificial intelligence; MIL: Multiple instance learning; CEA: Carcinoembryonic antigen; NLR: Neutrophil-lymphocyte ratio; WSI: Whole-slide image; TNM: Tumor-node-metastasis; IL: Interleukin-6; CRP: C-reactive protein; HRV: Heart rate variability; CME: Complete mesocolic excision; TME: Total mesorectal excision.