AI-based fingerprint index of visceral adipose tissue for the prediction of bowel damage in patients with Crohn's disease.

ABSTRACT: The fingerprint features of visceral adipose tissue (VAT) are intricately linked to bowel damage (BD) in patients with Crohn's disease (CD). We aimed to develop a VAT fingerprint index (VAT-FI) using radiomics and deep learning features extracted from computed tomography (CT) images of 1,135 CD patients across six hospitals (training cohort, n = 600; testing cohort, n = 535) for predicting BD, and to compare it with a subcutaneous adipose tissue (SAT)-FI. VAT-FI exhibited greater predictive accuracy than SAT-FI in both training (area under the receiver operating characteristic curve [AUC] = 0.822 vs. AUC = 0.745, p = 0.019) and testing (AUC = 0.791 vs. AUC = 0.687, p = 0.019) cohorts. Multivariate logistic regression analysis highlighted VAT-FI as the sole signi

SUBMITTER: Li X 

PROVIDER: S-EPMC11615179 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature