This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn's disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on the validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learning (DL) radiomics features were extracted from the arterial and venous phases images, respectively. The interobserver consistency analysis, Spearman's correlation, univariate analysis, and the least absolute shrinkage and selection operator (LASSO) regression were used to select features. Based on extracted multi-phase radiomics features, six logistic regression models were finally constructed. The diagnostic performances of different models were compared using ROC analysis and Delong test. The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality with AUCs of 0.885, 0.877, and 0.800 in SMOTE dataset, validation dataset one, and validation dataset two, respectively. Moreover, the deep learning radiomics model outperformed the handcrafted radiomics model in same phase images. In validation dataset one, the Delong test results indicated that there was a significant difference in the AUC of the arterial models (p = 0.037), while not in venous and arterial-venous combined models (p = 0.398 and p = 0.265) as comparing deep learning radiomics models and handcrafted radiomics models. In our study, the arterial-venous combined model based on deep learning radiomics analysis exhibited good performance in differentiating between CD and ITB.

Intestinal Behçet disease is typically associated with ileocecal punched-out ulcers and significant morbidity and mortality. Intestinal ultrasound is a noninvasive imaging technique for disease monitoring. However, no previous reports have compared intestinal ultrasound with endoscopic ulcer activity or histopathological findings for intestinal Behçet disease. We evaluated the usefulness of intestinal ultrasound for assessing the activity of ileocecal ulcers in intestinal Behçet disease.

We retrospectively compared intestinal ultrasound findings with 73 corresponding endoscopic images and 6 resected specimens. The intestinal ultrasound findings were assessed for 7 parameters (bowel wall thickness, vascularity [evaluated using the modified Limberg score with color Doppler], bowel wall stratification, white-plaque sign [strong hyperechogenic lines or spots], mesenteric lymphadenopathy, extramural phlegmons, and fistulas), and endoscopic ulcer activity was classified into active, healing, and scar stages. Histopathological findings were evaluated by consensus among experienced pathologists.

Bowel wall thickness (P< 0.001), vascularity (P< 0.001), loss of bowel wall stratification (P= 0.015), and white-plague sign (P= 0.013) were significantly exacerbated in the endoscopic active ulcer stage. Receiver operating characteristic curve analysis revealed that a bowel wall thickness of > 5.5 mm (sensitivity 89.7%, specificity 85.3%) was potentially useful for detecting active lesions. When compared with histopathological findings, an increase in bowel wall thickness reflected the ulcer marginal ridge, and the white-plaque sign reflected the ulcer bottom.

Intestinal ultrasound is useful for monitoring intestinal ulcer activity in intestinal Behçet disease.

Behçet's disease (BD) is a chronic inflammatory disorder prone to frequent recurrences, with a high predilection for intestinal involvement. However, the efficacy and long-term effects of surgical treatment for intestinal BD are unknown. In the current issue of World J Gastrointest Surg, Park et al conducted a retrospective analysis of 31 patients with intestinal BD who received surgical treatment. They found that elevated C-reactive protein levels and emergency surgery were poor prognostic factors for postoperative recurrence, emphasizing the adverse impact of severe inflammation on the prognosis of patients with intestinal BD. This work has clinical significance for evaluating the postoperative condition of intestinal BD. The editorial attempts to summarize the clinical diagnosis and treatment of intestinal BD, focusing on the impact of adverse factors on surgical outcomes. We hope this review will facilitate more precise postoperative management of patients with intestinal BD by clinicians.

Behçet's disease (BD) behaves similarly to Crohn's disease (CD) when the bowel is involved. Computed tomography enterography (CTE) can accurately show intestinal involvement and obtain body composition data. The objective of this study was to evaluate whether CTE could improve the ability to distinguish between intestinal BD and CD. This study evaluated clinical, laboratory, endoscopic, and CTE features on first admission. Body composition analysis was based on the CTE arterial phase. The middle layers of the L1-L5 vertebral body were selected. The indicators assessed included: the area ratio of visceral adipose tissue (VAT)/subcutaneous adipose tissue (SAT) (VSR) in each layer, the total volume ratio of VAT/SAT, the quartile of VAT attenuation in each layer and the coefficient of variation (CV) of the VAT area for each patient was also calculated. Two models were developed based on the above indicators: one was a traditional model (age, gender, ulcer distribution) and the other was a comprehensive model (age, gender, ulcer distribution, proximal ileum involvement, asymmetrical thickening of bowel wall, intestinal stenosis, VSRL4, and CV). The areas under the receiver operating characteristic (ROC) curve of the traditional (sensitivity: 80.0%, specificity: 81.0%) and comprehensive (sensitivity: 95.0%, specificity: 87.2%) models were 0.862 and 0.941, respectively (p = 0.005).