ArticleInsights into imaging2024
Establishing a machine learning model based on dual-energy CT enterography to evaluate Crohn's disease activity.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Applications of artificial intelligence in abdominal imaging.Abdominal radiology (New York) · 2025Review
- Radiomics nomogram based on dual-energy CT-derived iodine maps: evaluation of mucosal healing in patients with Crohn's disease.Abdominal radiology (New York) · 2025Article
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Authors and funding
8 authors.
Funding
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Abstract
objectivesThe simplified endoscopic score of Crohn's disease (SES-CD) is the gold standard for quantitatively evaluating Crohn's disease (CD) activity but is invasive. This study aimed to develop and validate a machine learning (ML) model based on dual-energy CT enterography (DECTE) to noninvasively evaluate CD activity.
methodsWe evaluated the activity in 202 bowel segments of 46 CD patients according to the SES-CD score and divided the segments randomly into training set and testing set at a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) was used for feature selection, and three models based on significant parameters were established based on logistic regression. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and clinical decision curves.
resultsThere were 110 active and 92 inactive bowel segments. In univariate analysis, the slope of spectral curve in the venous phases (λ
conclusionsThe ML model based the DECTE can feasibly evaluate CD activity, and DECTE parameters provide a quantitative analysis basis for evaluating specific bowel activities in CD patients. CRITICAL RELEVANCE STATEMENT: The machine learning model based on dual-energy computed tomography enterography can be used for evaluating Crohn's disease activity noninvasively and quantitatively. KEY POINTS: Dual-energy CT parameters are related to Crohn's disease activity. Three machine learning models effectively evaluated Crohn's disease activity. Combined models based on conventional and dual-energy CT have the best performance.
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