ArticleAcademic radiology2018
Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project).
Article in Academic radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2025Review
- Volumetric measurement of terminal ileal Crohn's disease by magnetic resonance enterography: a feasibility study.European radiology · 2025Article
- Artificial intelligence in inflammatory bowel disease: innovations in diagnosis, monitoring, and personalized care.Therapeutic advances in gastroenterology · 2025Review
- Local Assessment and Small Bowel Crohn's Disease Severity Scoring using AI.Academic radiology · 2024Article
- Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications.Journal of clinical medicine · 2024Review
- Machine learning methods in automated detection of CT enterography findings in Crohn's disease: A feasibility study.Clinical imaging · 2024Article
- Assessing the inflammatory severity of the terminal ileum in Crohn disease using radiomics based on MRI.BMC medical imaging · 2022Article
- Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases.Inflammatory bowel diseases · 2022Review
- Assessing Small Bowel Stricturing and Morphology in Crohn's Disease Using Semi-automated Image Analysis.Inflammatory bowel diseases · 2020Article
- Relationship between MRI quantified small bowel motility and abdominal symptoms in Crohn's disease patients-a validation study.The British journal of radiology · 2018Article
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
RATIONALE AND
objectivesThe objective of this study was to develop and validate a predictive magnetic resonance imaging (MRI) activity score for ileocolonic Crohn disease activity based on both subjective and semiautomatic MRI features. MATERIALS AND
methodsAn MRI activity score (the "virtual gastrointestinal tract [VIGOR]" score) was developed from 27 validated magnetic resonance enterography datasets, including subjective radiologist observation of mural T2 signal and semiautomatic measurements of bowel wall thickness, excess volume, and dynamic contrast enhancement (initial slope of increase). A second subjective score was developed based on only radiologist observations. For validation, two observers applied both scores and three existing scores to a prospective dataset of 106 patients (59 women, median age 33) with known Crohn disease, using the endoscopic Crohn's Disease Endoscopic Index of Severity (CDEIS) as a reference standard.
resultsThe VIGOR score (17.1 × initial slope of increase + 0.2 × excess volume + 2.3 × mural T2) and other activity scores all had comparable correlation to the CDEIS scores (observer 1: r = 0.58 and 0.59, and observer 2: r = 0.34-0.40 and 0.43-0.51, respectively). The VIGOR score, however, improved interobserver agreement compared to the other activity scores (intraclass correlation coefficient = 0.81 vs 0.44-0.59). A diagnostic accuracy of 80%-81% was seen for the VIGOR score, similar to the other scores.
conclusionsThe VIGOR score achieves comparable accuracy to conventional MRI activity scores, but with significantly improved reproducibility, favoring its use for disease monitoring and therapy evaluation.
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