Development and validation of the SYSU-score for MRI-based transmural healing assessment in Crohn's disease: a dual-center study.

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Abstract

Standardizing magnetic resonance enterography (MRE)-defined transmural healing (TH) remains challenging in Crohn's disease (CD) despite its prognostic superiority. We aimed to evaluate seven conventional MRE-defined TH criteria and develop a machine-learning-optimized model for improved TH assessment. In this double-center study, 467 active CD patients with 1263 MRE scans were stratified into three cohorts. Cohort 1 (n = 341) enabled retrospectively dual-metric comparison (attainment rate/prognostic protection) of seven MRE-defined TH criteria. Leveraging their strengths, we developed five machine-learning models for TH assessment to identify the optimal one. Semi-external validation was performed in prospective Ustekinumab (n = 92) and Upadacitinib (n = 34) cohorts. Among seven conventional TH criteria, magnetic resonance index of activity (MaRIA), C-score, and simplified MaRIA (sMaRIA) demonstrated higher attainment rates (23.75%/28.74%/41.64%) and lower disease progression rates (14.81%/18.37%/25.35%). Random forest (RF) model showed the most favorable overall performance across cohorts: Cohort 1 (AUC, 0.82 vs. 0.71-0.81), Ustekinumab (AUC, 0.83 vs. 0.73-0.81), and Upadacitinib (AUC, 0.80 vs. 0.58-0.80) cohorts. Dual-metric evaluation identified the RF model and C-score as clinically applicable tools. Notably, the RF model (namely SYSU-score) was more strongly associated with lower progression risk than C-score: in the Ustekinumab cohort, SYSU-score-defined TH showed lower disease progression risk (HR = 0.07, p < 0.001) than C-score (HR = 0.15, p < 0.001); Upadacitinib cohort validated SYSU-score as the only system significantly associated with lower progression risk (HR = 0.20, p < 0.05). We established a validated machine-learning-derived TH criterion integrating the strengths of conventional MRE-defined systems. SYSU-score-defined TH status was associated with lower progression risk with robust prognostic discrimination, advancing standardized TH assessment for clinical implementation. Question Among available MRI-based transmural healing (TH) criteria for Crohn's disease (CD), which are the most clinically applicable, and can a new model improve standardized prognostic assessment? Findings MaRIA, C-score, and sMaRIA showed relatively favorable clinical applicability, and the machine-learning-derived SYSU-score demonstrated robust prognostic performance in internal and semi-external validation. Clinical relevance SYSU-score may support more standardized MRI-based assessment of TH and improve risk stratification for disease progression in patients with CD.

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