Crohn's disease: research progress in decoding pathogenic multi-network and precision management of artificial intelligence radiomics

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

• Level A · Stronger Clinical Evidence
• Level B · Emerging clinical evidence with positive signals
• Level C · Early human research exploring benefits
• Level D · Scientific groundwork from lab and animal studies
• Emerging · Emerging topic under active research

Abstract (original English)

Crohn's disease (CD) is a chronic, relapsing inflammatory bowel disease characterized by transmural inflammation. Its clinical presentation and disease course are highly heterogeneous across individuals, and the global disease burden continues to rise. Although biomarkers such as fecal calprotectin and anti-Saccharomyces cerevisiae antibodies (ASCA), together with computed tomography enterography (CTE)/magnetic resonance enterography (MRE) and endoscopy, play central roles in diagnosis and longitudinal monitoring, important unmet needs remain. In particular, current approaches show limited reproducibility and insufficient phenotypic granularity for stratifying transmural inflammation, mesenteric involvement, and fibrostenotic disease, as well as for predicting therapeutic response and surgical risk. In this review, we adopt a multi-network pathogenic framework-encompassing genetic susceptibility, barrier dysfunction, microbial dysbiosis, immune-driven inflammation, fibrotic remodeling, and mesenteric inflammation with adipose remodeling-to delineate how these interconnected processes shape intestinal and mesenteric imaging phenotypes. We then focus on AI-enabled radiomics in CTE/MRE, summarizing key workflows for phenotype quantification, feature extraction, and model development, and highlighting its potential as an imaging biomarker across major clinical applications, includi

Evidence level

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

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