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

Abstract

Accurate prediction of lymph node metastasis (LNM) is critical for surgical decision-making and postoperative management in colorectal cancer. In a recent study, Zou et al published a study in World Journal of Gastroenterology, introduce a case-level multiple instance learning (MIL) framework that emulates expert diagnostic reasoning to extract clinically relevant morphologic patterns from whole-slide images. This artificial intelligence (AI)-augmented approach demonstrates promising discriminatory performance, particularly when integrated with clinical variables. However, morphology alone captures only a partial dimension of metastatic biology. Emerging evidence indicates that host systemic factors-including inflammation, immunometabolic status, and circadian regulation-play fundamental roles in shaping tumor progression and metastatic potential. Building upon the morphologic foundation provided by MIL, we argue that integrating AI-derived histopathologic features with host systemic signatures is essential for biologically coherent risk stratification. This editorial proposes a multimodal predictive framework that bridges tumor morphodynamics with host systemic complexity, offering a more comprehensive approach to LNM prediction. Such integration may refine individualized surgical strategies, guide perioperative optimization, and accelerate the transition toward precision oncology in colorectal cancer.

Keywords: Artificial intelligence; Deep learning; Computational pathology; Multiple instance learning; Colorectal cancer; Lymph node metastasis; Tumor microenvironment; Systemic inflammation; Physiologic complexity; Precision surgical oncology

Core Tip: The case-level multiple instance learning framework proposed by Zou et al represents an important advance in extracting prognostic morphologic features for lymph node metastasis in colorectal cancer. However, morphology alone is insufficient to capture the full spectrum of metastatic biology. Integrating artificial intelligence-derived histopathologic features with host systemic signatures-including inflammation, immunometabolic reserve, autonomic regulation, and circadian organization-provides a more biologically grounded and clinically actionable framework for risk stratification and precision surgical decision-making.