Evidence map›Paper›PMID 41794654›Full record

ArticleMagnetic resonance in medicine2026

Semi-Automatic Assessment of Crohn's Disease Activity by Combined Analysis of Bowel Lesions and Creeping Fat.

Antoine Kneib, Astrée Lemore, Gabriela Hossu, Laurent Peyrin-Biroulet, Valérie Laurent, Freddy Odille

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Antoine KneibIADI (U1254), Université de Lorraine and Inserm, Nancy, France.ORCID 0009-0000-4280-1250
Astrée LemoreCIC-IT 1433, Inserm, Université de Lorraine and CHRU Nancy, Nancy, France.ORCID 0009-0007-7745-9417
Gabriela HossuIADI (U1254), Université de Lorraine and Inserm, Nancy, France.
Laurent Peyrin-BirouletService d'Hépato-gastro-entérologie CHRU Nancy, Nancy, France.
Valérie LaurentIADI (U1254), Université de Lorraine and Inserm, Nancy, France.
Freddy OdilleIADI (U1254), Université de Lorraine and Inserm, Nancy, France.ORCID 0000-0001-5260-8905

Funding

Agence Nationale de la Recherche (ANR) ANR-23-IAHU-0012Agence Nationale de la Recherche (ANR) ANR-23-RHUS-0016European Regional Development Fund ERDF 2014-2020France Life Imaging ANR-11-INBS-0006Région Grand Est CPER 2015-2020 IT2MP
6 · The paper itself

Abstract

purposeTo develop and evaluate Crohn-BOOST, an open-source tool enabling semi-automatic segmentation of intestinal lesions and creeping fat on magnetic resonance enterography (MR Enterography), and to assess whether quantitative metrics derived from these segmentations relate to radiological disease activity scored with simplified MaRIA (sMaRIA) and Nancy indices. A fully annotated MR Enterography dataset was also curated to support future artificial intelligence research.

methodsThis retrospective single-center study included 102 patients with Crohn's disease (134 analyzable small-bowel lesions). Crohn-BOOST was used to delineate bowel lesions and creeping fat. Extracted metrics included lesion volume, wall thickness, lesion length, ADC, arterial enhancement, and creeping fat volume. Radiologic activity was assessed with sMaRIA and Nancy scores. Spearman correlations (95% CI by bootstrap), multivariable linear regression, and fivefold cross-validation were performed (p < 0.05).

resultsSegmentation time was < 3 min per patient. Inter-reader reproducibility in a two-reader subset (n = 30) was high (Dice 0.85 for lesions; 0.87 for creeping fat). Lesion volume (sMaRIA r = 0.65; Nancy r = 0.61; p < 0.001) and ADC (sMaRIA r = -0.70) showed the strongest correlations with radiological activity. In multivariable models, these two metrics remained independent predictors, whereas creeping fat volume and enhancement metrics were not.

conclusionCrohn-BOOST enables fast, reproducible 3D quantification on routine MR Enterography and supports objective estimation of radiological disease activity. Lesion volume and ADC show strong associations with sMaRIA and Nancy scores, while creeping fat volume appears more related to chronic remodeling than acute inflammation.

Indexed as

Adipose TissueCrohn DiseaseImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedIntestinesIntestine, SmallMagnetic Resonance ImagingAdolescentAdultAlgorithmsArtificial IntelligenceFemaleHumansMaleMiddle AgedReproducibility of Results

Identifiers

PMID41794654
PMCPMC13156446

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.