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Copyright: ©Author(s) 2026.
World J Gastroenterol. Aug 21, 2026; 32(31): 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 centersMultimodal LNM prediction integrates AI pathology + clinical context + host complexityIndividualized mesenteric resection plans based on quantified metastatic propensity
Neoadjuvant therapy selectionDecisions rely on TNM stage and imaging; occult LNM risk underestimatedModel identifies biologically high-risk patients despite imaging-negative nodesSupports escalation or de-escalation of neoadjuvant therapy
Adjuvant therapy tailoringStage-based decision often leads to overtreatment or undertreatmentMultimodal risk category reflects recurrence-related biologyEnables precision adjuvant chemotherapy decisions
Perioperative host optimizationLimited integration of inflammation, nutrition, circadian, or autonomic markersHost systemic complexity identifies modifiable vulnerabilities (IL-6, CRP, HRV, metabolic reserve)Personalized interventions (sleep, nutrition, autonomic regulation, anti-inflammatory strategies)
Surveillance strategyFollow-up schedules are stage-based and uniformMultimodal outputs yield individualized recurrence riskAllows dynamic, risk-adaptive surveillance intensity
Patient counseling and shared decision-makingRisk discussions often nonspecificClear risk categories + uncertainty quantificationImproves informed decision-making


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