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Fluorescence-mediated Tomography for the Detection and Quantification of Macrophage-related Murine Intestinal Inflammation
Published on: December 15, 2017
Computed tomographic enterography adds value to colonoscopy in differentiating Crohn's disease from intestinal
Ren Mao1, Wang-di Liao2, Yao He1
1Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Computed tomographic enterography (CTE) combined with colonoscopy significantly improves the diagnosis of Crohn's disease versus intestinal tuberculosis (ITB). This diagnostic algorithm allows for accurate differentiation between these challenging granulomatous disorders.
Area of Science:
- Gastroenterology
- Radiology
- Infectious Diseases
Background:
- Crohn's disease and intestinal tuberculosis (ITB) present similar symptoms, making differential diagnosis challenging.
- Computed tomographic enterography (CTE) shows promise for evaluating small bowel diseases, but its utility in distinguishing Crohn's from ITB is not well-defined.
Observation:
- A prospective study compared CTE and colonoscopy findings in 105 patients with Crohn's disease (n=67) and ITB (n=38).
- CTE features like proximal small-bowel involvement, asymmetrical mural thickening, segmental lesions, mural stratification, comb sign, and mesenteric fibrofatty proliferation were more frequent in Crohn's disease.
- Mesenteric lymph node changes (calcification, central necrosis) and focal ileocecal lesions were more common in ITB.
Findings:
- Multivariate analysis identified segmental small-bowel involvement and the comb sign as independent predictors of Crohn's disease.
- Combining CTE and colonoscopy findings increased diagnostic accuracy from 66.7% to 95.2% in the development set.
- The validation dataset showed a sensitivity of 92.5%, specificity of 80%, and an area under the ROC curve of 0.862 for the combined approach.
Implications:
- CTE provides valuable information that enhances colonoscopy for differentiating Crohn's disease from ITB.
- The developed diagnostic algorithm significantly improves diagnostic accuracy, aiding in appropriate patient management.
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