Machine Learning for Colitis-Associated Cancer in Inflammatory Bowel Disease: Evidence and Future Directions Toward Precision Medicine, a Narrative Review

Résumé fourni par la source

Colitis-associated cancer (CAC) represents a major long-term complication in patients with ulcerative colitis (UC) and Crohn's disease (CD), the two main forms of inflammatory bowel disease (IBD). Unlike sporadic colorectal cancer, CAC develops through a distinct inflammation-dysplasia-carcinoma sequence driven by chronic inflammation and complex molecular alterations. Early detection of dysplasia and accurate risk stratification remain critical challenges in IBD management. Conventional surveillance strategies, including endoscopy, histopathology, and immunohistochemistry, are time-consuming, operator-dependent, and may fail to identify early neoplastic changes. In this context, artificial intelligence (AI), including machine learning (ML) and deep learning (DL), has emerged as a promising approach to improve lesion detection, molecular characterization, and predictive risk modeling. Early studies, though limited, suggest that AI-based approaches may enhance the identification of dysplasia and CAC, improve risk prediction, and support personalized surveillance strategies. Furthermore, the integration of multimodal data, including clinical, endoscopic, histological, and molecular features, may further improve predictive performance and enable precision medicine approaches in IBD. This review summarizes current evidence on AI and ML applications for CAC detection and risk prediction in IBD, discusses technical and clinical challenges, and highlights future directions for integrating AI into routine clinical practice to improve surveillance and clinical outcomes in patients with IBD.

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