ArticleGastroenterology2021
Development and Validation of a Novel Computed-Tomography Enterography Radiomic Approach for Characterization of Intestinal Fibrosis in Crohn's Disease.
Article in Gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06858553 (A Prospective, Multi-center Study to Characterize Intestinal Fibrosis in Patients With Crohn's Disease), which is not on this map. Cited by 70 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Prospective, Multi-center Study to Characterize Intestinal Fibrosis in Patients With Crohn's Disease (CD) Using MR Enterography (MRE)-Based Artificial Intelligence
Who cites it
70 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The global academic distribution and changes in research hotspots of artificial intelligence in inflammatory bowel disease since 2000.Frontiers in medicine · 2025Pooled it
- Defining mucosal healing in randomized controlled trials of inflammatory bowel disease: A systematic review and future perspective.United European gastroenterology journal · 2024Pooled it
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Extracellular volume fraction derived from dual-energy CT characterizing the fibrosis of the bowel wall in stricturing Crohn disease.Japanese journal of radiology · 2026Article
- Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.Chinese medical journal · 2026Review
- Development of a Computed Tomography Enterography-Based Radiomics Model to Characterize Inflammation, Fibrosis, and Smooth Muscle Thickening in Stricturing Crohn's Disease.The American journal of gastroenterology · 2026Article
- Computed tomography enterography-based deep learning radiomics models of intestinal lesions and perienteric fat for predicting Crohn's disease activity: a multicenter cohort study.Quantitative imaging in medicine and surgery · 2026Article
- Mesenteric and antimesenteric border subregionalization using MR enterography: advancing fibrosis evaluation in Crohn disease.European radiology experimental · 2026Article
- Multi-regional radiomics of obstructive sleep apnea based on synthetic MRI.Respiratory research · 2026Article
- Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Artificial Intelligence in Pediatric Inflammatory Bowel Disease: Applications in Diagnosis, Monitoring, and Therapeutic Decision-Making.Children (Basel, Switzerland) · 2026Review
- Crohn's disease: research progress in decoding pathogenic multi-network and precision management of artificial intelligence radiomics.Frontiers in immunology · 2026Review
- Variability and performance of radiologic stricture parameters in Crohn's disease: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Artificial Intelligence-Enabled Clinical Trials in Inflammatory Bowel Disease: Automating and Enhancing Disease Assessment and Study Management.Gastroenterology · 2025Review
- The functional role of the extracellular matrix in inflammatory bowel disease associated gut fibrosis.Matrix biology : journal of the International Society for Matrix Biology · 2025Review
- Comparison of volumetric and linear measurements of intestinal inflammation and treatment response in children with newly diagnosed ileal Crohn disease.European radiology · 2025Article
- SAR Consensus Recommendations for Defining Small Bowel Crohn Disease Strictures at CT and MR Enterography.Radiology · 2025Review
- Artificial intelligence in inflammatory bowel disease.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association · 2025Review
- The Importance of Magnetic Resonance Enterography in Monitoring Inflammatory Bowel Disease: A Review of Clinical Significance and Current Challenges.Diagnostics (Basel, Switzerland) · 2025Review
10 more citing papers are in PubMed but not listed here.
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25 authors.
Funding
Abstract
BACKGROUND &
aimsNo reliable method for evaluating intestinal fibrosis in Crohn's disease (CD) exists; therefore, we developed a computed-tomography enterography (CTE)-based radiomic model (RM) for characterizing intestinal fibrosis in CD.
methodsThis retrospective multicenter study included 167 CD patients with 212 bowel lesions (training, 98 lesions; test, 114 lesions) who underwent preoperative CTE and bowel resection at 1 of the 3 tertiary referral centers from January 2014 through June 2020. Bowel fibrosis was histologically classified as none-mild or moderate-severe. In the training cohort, 1454 radiomic features were extracted from venous-phase CTE and a machine learning-based RM was developed based on the reproducible features using logistic regression. The RM was validated in an independent external test cohort recruited from 3 centers. The diagnostic performance of RM was compared with 2 radiologists' visual interpretation of CTE using receiver operating characteristic (ROC) curve analysis.
resultsIn the training cohort, the area under the ROC curve (AUC) of RM for distinguishing moderate-severe from none-mild intestinal fibrosis was 0.888 (95% confidence interval [CI], 0.818-0.957). In the test cohort, the RM showed robust performance across 3 centers with an AUC of 0.816 (95% CI, 0.706-0.926), 0.724 (95% CI, 0.526-0.923), and 0.750 (95% CI, 0.560-0.940), respectively. Moreover, the RM was more accurate than visual interpretations by either radiologist (radiologist 1, AUC = 0.554; radiologist 2, AUC = 0.598; both, P < .001) in the test cohort. Decision curve analysis showed that the RM provided a better net benefit to predicting intestinal fibrosis than the radiologists.
conclusionsA CTE-based RM allows for accurate characterization of intestinal fibrosis in CD.
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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.