Evidence map›Paper›PMID 42319405›Full record

ArticleEuropean radiology2026

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

Qingzhu Zheng, Qiaochu Zhao, Weitao He, Lili Huang, Xiaodi Shen, Luyao Wu, Yaoqi Ke, Weikai Zheng, Yangdi Wang, Yujun Chen and 5 more

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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Qingzhu Zheng *Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Qiaochu Zhao *Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Weitao He *Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Lili HuangDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Xiaodi ShenDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Luyao WuDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Yaoqi KeDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Weikai ZhengDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Yangdi WangDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Yujun ChenDepartment of Ultrasound, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Ren MaoDepartment of Gastroenterology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Zhenpeng PengDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Shi-Ting FengDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Ruonan ZhangDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China. zhangrn28@mail.sysu.edu.cn.
Xuehua LiDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China. lxueh@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0002-7476-2644

Funding

023 SKY Imaging Research Fund of the Chinese International Medical Foundation Z-2014-07-2301Fundamental Research Funds for the Central Universities, Sun Yat-sen University 24ykqb003Guangdong Basic and Applied Basic Research Foundation 2023A1515011097Guangdong Basic and Applied Basic Research Foundation 2023B1515020070Guangdong Basic and Applied Basic Research Foundation 2026A1515012338Guangdong Basic and Applied Basic Research Foundation 2026A1515012780National Natural Science Foundation of China 82270693, 82070680, and 82572164National Natural Science Foundation of China 82471948 and 82271958
6 · The paper itself

Abstract

objectivesStandardizing 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. MATERIALS AND

methodsIn 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.

resultsAmong 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).

conclusionsWe 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. KEY POINTS: 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.

Indexed as

Crohn diseaseDisease progressionMagnetic resonance enterography

Identifiers

PMID42319405

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