AI-based fingerprint index of visceral adipose tissue for the prediction of bowel damage in patients with Crohn’s disease
Year: 2024
Type: article
Study design: Observational Study
Abstract: Summary 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.
Source: iScience
Institutions: Sun Yat-sen University, The First Affiliated Hospital, Sun Yat-sen University, First Affiliated Hospital of Wenzhou Medical University, Shenzhen University, First Affiliated Hospital of Shantou University Medical College, First People's Hospital of Foshan, Dongguan People’s Hospital, Southern Medical University, Guangdong Medical College, Jiangmen Central Hospital, Hainan General Hospital, Hainan Medical University, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Third Affiliated Hospital of Guangzhou Medical University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,150
Funders: National Natural Science Foundation of China, Sun Yat-sen University, China International Medical Foundation, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities, Fundamental Research Funds for Central Universities of the Central South University, Special Project for Research and Development in Key areas of Guangdong Province, Basic and Applied Basic Research Foundation of Guangdong Province
OpenAlex ID: W4402945733