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 significant predictor (training cohort: hazard ratio [HR] = 1.684, p = 0.012; testing cohort: HR = 2.649, p < 0.001). Through Shapley additive explanation (SHAP) analysis, we further quantitatively elucidated the predictive relationship between VAT-FI and BD, highlighting potential connections such as Radio479 (wavelet-HLH-first-order standard deviation)-Frequency loose stools-BD severity. VAT-FI offers an accurate means for characterizing BD, minimizing the need for extensive clinical data.
Keywords: Artificial Intelligence; Health sciences.
© 2024 The Author(s).