ArticleAbdominal radiology (New York)2023
Computed tomography enterography-based multiregional radiomics model for differential diagnosis of Crohn's disease from intestinal tuberculosis.
Article in Abdominal radiology (New York), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- The Overlap Between Crohn's Disease and Intestinal Tuberculosis: A Never-Ending Story.Medicina (Kaunas, Lithuania) · 2026Review
- Crohn's disease: research progress in decoding pathogenic multi-network and precision management of artificial intelligence radiomics.Frontiers in immunology · 2026Review
- Computed tomography enterography-based radiomics for assessing mucosal healing in patients with small bowel Crohn's disease.World journal of gastroenterology · 2025Article
- Development and Validation of a CTE-Based Radiomics Nomogram for Predicting Clinical Adverse Outcomes in Patients with Stricturing Crohn's Disease.Journal of inflammation research · 2025Article
- Clinical utility of multi-row spiral CT in diagnosing hepatic nodular lesions, gastric cancer, and Crohn's disease: a comprehensive meta-analysis.American journal of clinical and experimental immunology · 2024Article
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Authors and funding
8 authors at 2 institutions in 1 country.
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Abstract
purposeTo build computed tomography enterography (CTE)-based multiregional radiomics model for distinguishing Crohn's disease (CD) from intestinal tuberculosis (ITB). MATERIALS AND
methodsA total of 105 patients with CD and ITB who underwent CTE were retrospectively enrolled. Volume of interest segmentation were performed on CTE and radiomic features were obtained separately from the intestinal wall of lesion, the largest lymph node (LN), and region surrounding the lesion in the ileocecal region. The most valuable radiomic features was selected by the selection operator and least absolute shrinkage. We established nomogram combining clinical factors, endoscopy results, CTE features, and radiomic score through multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the prediction performance. DeLong test was applied to compare the performance of the models.
resultsThe clinical-radiomic combined model comprised of four variables including one radiomic signature from intestinal wall, one radiomic signature from LN, involved bowel segments on CTE, and longitudinal ulcer on endoscopy. The combined model showed good diagnostic performance with an area under the ROC curve (AUC) of 0.975 (95% CI 0.953-0.998) in the training cohort and 0.958 (95% CI 0.925-0.991) in the validation cohort. The combined model showed higher AUC than that of the clinical model in cross-validation set (0.958 vs. 0.878, P = 0.004). The DCA showed the highest benefit for the combined model.
conclusionClinical-radiomic combined model constructed by combining CTE-based radiomics from the intestinal wall of lesion and LN, endoscopy results, and CTE features can accurately distinguish CD from ITB.
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