ArticleJournal of inflammation research2021
A Novel Clinical Radiomics Nomogram to Identify Crohn's Disease from Intestinal Tuberculosis.
Article in Journal of inflammation research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- CT‑based radiomics of bowel wall at baseline predicts the efficacy of Ustekinumab at week 16 in patients with Crohn's disease.Abdominal radiology (New York) · 2026Article
- Urinary lipoarabinomannan for gastrointestinal tuberculosis: Another tool in the kit.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026Article
- 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
- MRI quantitative imaging biomarkers in differentiating brain parenchymal tuberculoma and lung cancer brain metastases.European journal of medical research · 2025Article
- Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study.BMC medical imaging · 2025Article
- Prediction of early mucosal healing of Crohn's disease after treatment with biologics- a novel nomogram based on radiomics and clinical risk factors.Frontiers in pharmacology · 2025Article
- A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn's disease.Abdominal radiology (New York) · 2024Article
- A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study.Frontiers in cellular and infection microbiology · 2024Article
- Deep-Learning, Radiomics and Clinic Based Fusion Models for Predicting Response to Infliximab in Crohn's Disease Patients: A Multicentre, Retrospective Study.Journal of inflammation research · 2024Article
- Differentiating gastrointestinal tuberculosis and Crohn's disease- a comprehensive review.BMC gastroenterology · 2023Review
- Computed tomography enterography-based multiregional radiomics model for differential diagnosis of Crohn's disease from intestinal tuberculosis.Abdominal radiology (New York) · 2023Article
- The Role of Radiomics in Fibrosis Crohn's Disease: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Development of a simplified model and nomogram in preoperative diagnosis of pediatric chronic cholangitis with pancreaticobiliary maljunction using clinical variables and MRI radiomics.Insights into imaging · 2023Article
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTo establish a clinical radiomics nomogram to differentiate Crohn's disease (CD) from intestinal tuberculosis (ITB). PATIENTS AND
methodsNinety-three patients with CD and 67 patients with ITB were recruited (111 in training cohort and 49 in test cohort). The region of interest (ROI) for the lesions in the ileocecal region was delineated on computed tomography enterography and radiomics features extracted. Radiomics features were filtered by the gradient boosting decision tree (GBDT), and a radiomics score was calculated by using the radiomics signature-based formula. We constructed a clinical radiomics model and nomogram combining clinical factors and radiomics score through multivariate logistic regression analysis, and the internal validation was undertaken by ten-fold cross validation. Analyses of receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the prediction performance. DeLong test was applied to evaluate the performance of the clinical, radiomics and combined model.
resultsThe clinical radiomics nomogram, which was based on the 9 radiomics signature and two clinical factors, indicated that the clinical radiomics model had an area under the ROC curve (AUC) value of 0.96 (95% confidence interval [CI]: 0.93-0.99) in the training cohort and 0.93 (95% CI: 0.86-1.00) in validation cohort. The clinical radiomics model was superior to the clinical model and radiomics model, and the difference was significant (
conclusionCTE-based radiomics model has a good performance in distinguishing CD from ITB. A nomogram constructed by combining radiomics and clinical factors can help clinicians accurately diagnose and select appropriate treatment strategies between CD and ITB.
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