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CT-based delta-radiomics signature of visceral adipose tissue for prediction of disease progression in ileal stricturing Crohn's disease. | LitMetric

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Article Abstract

Objectives: To establish and validate a model based on CT imaging during follow-ups for predicting the disease progression in ileal stricturing Crohn's disease (CD).

Methods: Between January 2014 and February 2024, a retrospective review was conducted on 71 patients (training, n = 49; test, n = 22) who were initially diagnosed with ileal stricturing CD. Disease progression referred to the development of penetrating diseases, the requirement for CD-related hospitalization or surgery during follow-up. Radiomics features were extracted from visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) on baseline and follow-up CT scans, respectively. Integrating clinical characteristics and body composition features, a novel CT-based delta-radiomics nomogram was established according to multivariate Cox stepwise regression analysis. Receiver operating characteristic (ROC) analysis was performed to assess diagnostic performance.

Results: The delta-VAT radiomics model (RM) exhibited satisfactory performance in training cohort (the area under the ROC curve [AUC] = 0.792, 95% confidence Interval [CI] 0.666-0.917) and in test cohort (AUC = 0.640, 95% CI 0.411-0.870). The AUCs of the delta-SAT RM were 0.777 (95% CI 0.648-0.907) in training cohort and 0.612 (95% CI 0.377-0.846) in test cohort. The combined nomogram model showed good discrimination for predicting disease progression, with a C-index of 0.808 and 0.702 in the training and test cohorts, respectively.

Conclusions: We first constructed a comprehensive model incorporating delta-adipose radiomics, baseline neutrophil-to-lymphocyte ratio (NLR) level and the application of biological therapy to predict progression in ileal stricturing CD, which aids in the timely adjustment of therapeutic strategies and enhances patients' quality of life.

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http://dx.doi.org/10.1007/s11604-025-01779-5DOI Listing

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