Copyright: ©Author(s) 2026.
World J Gastroenterol. Aug 21, 2026; 32(31): 117409
Published online Aug 21, 2026. doi: 10.3748/wjg.117409
Published online Aug 21, 2026. doi: 10.3748/wjg.117409
Table 3 Potential clinical implications of extending the multiple instance learning-based prediction model by Zou et al[6] into a multimodal framework
| Clinical domain | Current practice limitation | Multimodal model contribution | Resulting clinical implication |
| Extent of mesenteric excision (CME/TME) | Surgical extent often based on morphology, experience, and imaging; variable across centers | Multimodal LNM prediction integrates AI pathology + clinical context + host complexity | Individualized mesenteric resection plans based on quantified metastatic propensity |
| Neoadjuvant therapy selection | Decisions rely on TNM stage and imaging; occult LNM risk underestimated | Model identifies biologically high-risk patients despite imaging-negative nodes | Supports escalation or de-escalation of neoadjuvant therapy |
| Adjuvant therapy tailoring | Stage-based decision often leads to overtreatment or undertreatment | Multimodal risk category reflects recurrence-related biology | Enables precision adjuvant chemotherapy decisions |
| Perioperative host optimization | Limited integration of inflammation, nutrition, circadian, or autonomic markers | Host systemic complexity identifies modifiable vulnerabilities (IL-6, CRP, HRV, metabolic reserve) | Personalized interventions (sleep, nutrition, autonomic regulation, anti-inflammatory strategies) |
| Surveillance strategy | Follow-up schedules are stage-based and uniform | Multimodal outputs yield individualized recurrence risk | Allows dynamic, risk-adaptive surveillance intensity |
| Patient counseling and shared decision-making | Risk discussions often nonspecific | Clear risk categories + uncertainty quantification | Improves informed decision-making |
- Citation: Wang G, Pan SJ. Artificial intelligence morphology and host complexity for precision prediction of nodal metastasis in colorectal cancer. World J Gastroenterol 2026; 32(31): 117409
- URL: https://www.wjgnet.com/1007-9327/full/v32/i31/117409.htm
- DOI: https://dx.doi.org/10.3748/wjg.117409